Reviewerly/exHarmony huggingface dataset:
Quantum metrology with delegated tasks

A quantum metrology scheme can be decomposed into three quantum tasks: state preparation, parameter encoding, and measurements. Consequently, it is imperative to have access to the technologies which can execute the aforementioned tasks to fully implement a quantum metrology scheme. In the absence of one or more of these technologies, one can proceed by delegating the tasks to a third party. However, doing so has security ramifications: the third party can bias the result or leak information. In this paper, we outline different scenarios where one or more tasks are delegated to an untrusted (and possibly malicious) third party. In each scenario, we outline cryptographic protocols which can be used to circumvent malicious activity. Further, we link the effectiveness of the quantum metrology scheme to the soundness of the cryptographic protocols.
Quantum-accelerated algorithms for generating random primitive polynomials over finite fields

Primitive polynomials over finite fields are crucial for various domains of computer science, including classical pseudo-random number generation, coding theory and post-quantum cryptography. Nevertheless, the pursuit of an efficient classical algorithm for generating random primitive polynomials over finite fields remains an ongoing challenge. In this paper, we show how to solve this problem efficiently through hybrid quantum-classical algorithms, and designs of the specific quantum circuits to implement them are also presented. Our research paves the way for the rapid and real-time generation of random primitive polynomials in diverse quantum communication and computation applications.
TO-Scene: A Large-Scale Dataset for Understanding 3D Tabletop Scenes

Many basic indoor activities such as eating or writing are always conducted upon different tabletops (e.g., coffee tables, writing desks). It is indispensable to understanding tabletop scenes in 3D indoor scene parsing applications. Unfortunately, it is hard to meet this demand by directly deploying data-driven algorithms, since 3D tabletop scenes are rarely available in current datasets. To remedy this defect, we introduce TO-Scene, a large-scale dataset focusing on tabletop scenes, which contains 20,740 scenes with three variants. To acquire the data, we design an efficient and scalable framework, where a crowdsourcing UI is developed to transfer CAD objects from ModelNet [43] and ShapeNet [5] onto tables from ScanNet [10], then the output tabletop scenes are simulated into real scans and annotated automatically. Further, a tabletop-aware learning strategy is proposed for better perceiving the small-sized tabletop instances. Notably, we also provide a real scanned test set TO-Real to verify the practical value of TO-Scene. Experiments show that the algorithms trained on TO-Scene indeed work on the realistic test data, and our proposed tabletop-aware learning strategy greatly improves the state-of-the-art results on both 3D semantic segmentation and object detection tasks. Dataset and code are available at https://github.com/GAP-LAB-CUHK-SZ/TO-Scene .
Quantum algorithm for solving a quadratic nonlinear system of equations

Solving a quadratic nonlinear system of equations (QNSE) is a fundamental, but important, task in nonlinear science. We propose an efficient quantum algorithm for solving $n$-dimensional QNSE. Our algorithm embeds QNSE into a finite-dimensional system of linear equations using the homotopy perturbation method and a linearization technique; then we solve the linear equations with a quantum linear system solver and obtain a state which is $\ensuremath{\epsilon}$-close to the normalized exact solution of the QNSE with success probability $\mathrm{\ensuremath{\Omega}}(1)$. The complexity of our algorithm is $O(\mathrm{polylog}(n/\ensuremath{\epsilon}))$, which provides an exponential improvement over the optimal classical algorithm in dimension $n$, and the dependence on $\ensuremath{\epsilon}$ is almost optimal. Therefore, our algorithm exponentially accelerates the solution of QNSE and has wide applications in all kinds of nonlinear problems, contributing to the research progress of nonlinear science.
Do Human Capital and Economic Development Drive Adoption of Digital Technologies Across Countries? Some Correlational Evidence

The goal of this study is to explore the relationship between human capital, economic development and the adoption of digital technologies across countries. Using data for 175 countries for the year 2015, we find that human capital and GDP per capita are positively correlation with Digital Adoption Index (DAI). One standard deviation increases in HCI (SD = 0.13) is associated with slightly less than a standard deviation increases in DAI. The policy implications are discussed.
A descriptive study on misidentifications of a person as a familiar person in an everyday situation

The purpose of this study is to show the characteristics of person misidentifications, that is, experiences in which persons are misidentified as known persons. A total of 121 participants were asked how many times they misidentified persons in the last year and details of a recent person misidentification were recorded through a traditional questionnaire. Additionally, they answered questions in a diary method questionnaire, about the details of person misidentification each time they experienced it, during the two-week survey period. The questionnaires revealed that the participants misidentified both known and unknown persons as familiar persons more than several times a year on average, regardless of whether they expected the persons to be there. They were more likely to misidentify a person as a familiar than as a less familiar person. It was also shown that the similarity of the faces of the person actually seen and the person they were mistaken for was not as high as the similarities of build and clothing. This study is expected to provide suggestions for models of person identification and enhance the research on errors.
On Neural Differential Equations

The conjoining of dynamical systems and deep learning has become a topic of great interest. In particular, neural differential equations (NDEs) demonstrate that neural networks and differential equation are two sides of the same coin. Traditional parameterised differential equations are a special case. Many popular neural network architectures, such as residual networks and recurrent networks, are discretisations. NDEs are suitable for tackling generative problems, dynamical systems, and time series (particularly in physics, finance, ...) and are thus of interest to both modern machine learning and traditional mathematical modelling. NDEs offer high-capacity function approximation, strong priors on model space, the ability to handle irregular data, memory efficiency, and a wealth of available theory on both sides. This doctoral thesis provides an in-depth survey of the field. Topics include: neural ordinary differential equations (e.g. for hybrid neural/mechanistic modelling of physical systems); neural controlled differential equations (e.g. for learning functions of irregular time series); and neural stochastic differential equations (e.g. to produce generative models capable of representing complex stochastic dynamics, or sampling from complex high-dimensional distributions). Further topics include: numerical methods for NDEs (e.g. reversible differential equations solvers, backpropagation through differential equations, Brownian reconstruction); symbolic regression for dynamical systems (e.g. via regularised evolution); and deep implicit models (e.g. deep equilibrium models, differentiable optimisation). We anticipate this thesis will be of interest to anyone interested in the marriage of deep learning with dynamical systems, and hope it will provide a useful reference for the current state of the art.
Blended Diffusion for Text-driven Editing of Natural Images

Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language description along with an ROI mask. We achieve our goal by leveraging and combining a pretrained language-image model (CLIP), to steer the edit towards a user-provided text prompt, with a denoising diffusion probabilistic model (DDPM) to generate natural-looking results. To seamlessly fuse the edited region with the unchanged parts of the image, we spatially blend noised versions of the input image with the local text-guided diffusion latent at a progression of noise levels. In addition, we show that adding augmentations to the diffusion process mitigates adversarial results. We compare against several baselines and related methods, both qualitatively and quantitatively, and show that our method outperforms these solutions in terms of overall realism, ability to preserve the background and matching the text. Finally, we show several text-driven editing applications, including adding a new object to an image, removing/replacing/altering existing objects, background replacement, and image extrapolation. Code is available at: https://omriavrahami.com/blended-diffusion-page/
Nonparametric Differentially Private Confidence Intervals for the Median

Abstract Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data. However, research on proper statistical inference, that is, research on properly quantifying the uncertainty of the (noisy) sample estimate regarding the true value in the population, is currently still limited. This article proposes and evaluates several strategies to compute valid differentially private confidence intervals for the median. Instead of computing a differentially private point estimate and deriving its uncertainty, we directly estimate the interval bounds and discuss why this approach is superior if ensuring privacy is important. We also illustrate that addressing both sources of uncertainty—the error from sampling and the error from protecting the output—simultaneously should be preferred over simpler approaches that incorporate the uncertainty in a sequential fashion. We evaluate the performance of the different algorithms under various parameter settings in extensive simulation studies and demonstrate how the findings could be applied in practical settings using data from the 1940 Decennial Census.
The Orthologic of Epistemic Modals

Epistemic modals have peculiar logical features that are challenging to account for in a broadly classical framework. For instance, while a sentence of the form $p\wedge\Diamond\neg p$ ('$p$, but it might be that not $p$') appears to be a contradiction, $\Diamond\neg p$ does not entail $\neg p$, which would follow in classical logic. Likewise, the classical laws of distributivity and disjunctive syllogism fail for epistemic modals. Existing attempts to account for these facts generally either under- or over-correct. Some predict that $p\wedge\Diamond\neg p$, a so-called epistemic contradiction, is a contradiction only in an etiolated sense, under a notion of entailment that does not always allow us to replace $p\wedge\Diamond\neg p$ with a contradiction; these theories underpredict the infelicity of embedded epistemic contradictions. Other theories savage classical logic, eliminating not just rules that intuitively fail but also rules like non-contradiction, excluded middle, De Morgan's laws, and disjunction introduction, which intuitively remain valid for epistemic modals. In this paper, we aim for a middle ground, developing a semantics and logic for epistemic modals that makes epistemic contradictions genuine contradictions and that invalidates distributivity and disjunctive syllogism but that otherwise preserves classical laws that intuitively remain valid. We start with an algebraic semantics, based on ortholattices instead of Boolean algebras, and then propose a more concrete possibility semantics, based on partial possibilities related by compatibility. Both semantics yield the same consequence relation, which we axiomatize. We then show how to lift an arbitrary possible worlds model for a non-modal language to a possibility model for a language with epistemic modals.
SwinTrack: A Simple and Strong Baseline for Transformer Tracking

Recently Transformer has been largely explored in tracking and shown state-of-the-art (SOTA) performance. However, existing efforts mainly focus on fusing and enhancing features generated by convolutional neural networks (CNNs). The potential of Transformer in representation learning remains under-explored. In this paper, we aim to further unleash the power of Transformer by proposing a simple yet efficient fully-attentional tracker, dubbed SwinTrack, within classic Siamese framework. In particular, both representation learning and feature fusion in SwinTrack leverage the Transformer architecture, enabling better feature interactions for tracking than pure CNN or hybrid CNN-Transformer frameworks. Besides, to further enhance robustness, we present a novel motion token that embeds historical target trajectory to improve tracking by providing temporal context. Our motion token is lightweight with negligible computation but brings clear gains. In our thorough experiments, SwinTrack exceeds existing approaches on multiple benchmarks. Particularly, on the challenging LaSOT, SwinTrack sets a new record with 0.713 SUC score. It also achieves SOTA results on other benchmarks. We expect SwinTrack to serve as a solid baseline for Transformer tracking and facilitate future research. Our codes and results are released at https://github.com/LitingLin/SwinTrack.
A movement planning and control method of special robot over obstacle based on centroid monitoring

Aiming at the problems of scattered operating functions, low non-line-of-sight remote control efficiency and poor stability in the practical application of tracked special robots, this paper proposes a convenient movement planning and control method of special robot over obstacle based on centroid monitoring. Firstly, the typical structure of the tracked special robot is analyzed. Then, the centroid kinematics model of the robot with movable structure is established based on the centroid coordinate formula. The non-line-of-sight obstacle height estimation method is proposed. The obstacle-climbing movement planning and control method is carried out based on the centroid monitoring. Finally, a kinematic model comparative experiment and obstacle-climbing simulation experiment are designed to verify the feasibility of the proposed method. This method can integrate the robot's obstacle-climbing actions, adjust the position of the movable joints automatically, and independently adjust the robot's centroid distribution. Based on proposed method, the robot achieves a stable and convenient semi-autonomous obstacle-climbing function at a given speed and direction. The proposed method reduces the operator's difficulty in non-line-of-sight condition and greatly improves the efficiency and reliability of obstacle crossing task.
EFFECT OF USING YOUTUBE ON STUDENTS’ MOTIVATION IN LEARNING ENGLISH VOCABULARY

YouTube is an online media platform containing a variety of videos. It is also a place to post, share, and download videos. Its users range from children, teenagers, adults to parents, and no exception including students. This study aims to investigate the effect of using YouTube on students’ motivation in learning English vocabulary of the ninth grade at SMPN 35 Bekasi. A quantitative method was employed in this study involving 74 students from two groups – experimental class and control class. Data were obtained using questionnaires and interviews. Questionnaires were distributed to both groups and interviews were conducted in one group, i.e., experimental class. Data were calculated using the SPSS 22.00 computer program for the window. The results show that there was a significant effect of the use of YouTube on students’ motivation in learning English vocabulary in the ninth grade. The effect can be seen from the difference in the results of table of descriptive statistics with the mean value of the experimental class of 51.38 higher than that of the control class of 43.59 with each respondent (N) 37. The significant value of independent sample t-test calculated sig. (2-tailed).000 was &lt; 0.05. This study indicates that the technique of using YouTube motivates students in learning English vocabulary.
Query and Depth Upper Bounds for Quantum Unitaries via Grover Search

We prove that any $n$-qubit unitary transformation can be implemented (i) approximately in time $\tilde O\big(2^{n/2}\big)$ with query access to an appropriate classical oracle, and also (ii) exactly by a circuit of depth $\tilde O\big(2^{n/2}\big)$ with one- and two-qubit gates and $2^{O(n)}$ ancillae. The proofs involve similar reductions to Grover search. The proof of (ii) also involves a linear-depth construction of arbitrary quantum states using one- and two-qubit gates (in fact, this can be improved to constant depth with the addition of fanout and generalized Toffoli gates) which may be of independent interest. We also prove a matching $\Omega\big(2^{n/2}\big)$ lower bound for (i) and (ii) for a certain class of implementations.
Tensions between digital inequalities and digital learning opportunities in Russian universities during the pandemic

The COVID-19 pandemic had a huge impact upon all spheres of our life — social, economic, cultural, political, academic, and others. A shift to a new digital reality intensified already existing digital gaps and inequalities across societies and social groups within the countries and triggered a discussion about new forms of the digital divides in the pandemic and post-pandemic world. One of the areas that have been seriously affected by a shift to digital life is education. By looking at various educational platforms and tools used by Russian universities since lockdown in spring 2020, both for educational purposes and entrance admission routine, we discuss the challenges and new conflicts digital reality has brought to universities. At the same time, this study focuses on the advantages of this new reality for learning and educational processes. We argue that in the post-pandemic world new digital divides and new demands to university staff members and students have appeared, illustrating this argument with examples from Russia.
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations

Abstract Providing explanations in the context of Visual Question Answering (VQA) presents a fundamental problem in machine learning. To obtain detailed insights into the process of generating natural language explanations for VQA, we introduce the large-scale CLEVR-X dataset that extends the CLEVR dataset with natural language explanations. For each image-question pair in the CLEVR dataset, CLEVR-X contains multiple structured textual explanations which are derived from the original scene graphs. By construction, the CLEVR-X explanations are correct and describe the reasoning and visual information that is necessary to answer a given question. We conducted a user study to confirm that the ground-truth explanations in our proposed dataset are indeed complete and relevant. We present baseline results for generating natural language explanations in the context of VQA using two state-of-the-art frameworks on the CLEVR-X dataset. Furthermore, we provide a detailed analysis of the explanation generation quality for different question and answer types. Additionally, we study the influence of using different numbers of ground-truth explanations on the convergence of natural language generation (NLG) metrics. The CLEVR-X dataset is publicly available at https://github.com/ExplainableML/CLEVR-X .
Parameter-efficient Model Adaptation for Vision Transformers

In computer vision, it has achieved great transfer learning performance via adapting large-scale pretrained vision models (e.g., vision transformers) to downstream tasks. Common approaches for model adaptation either update all model parameters or leverage linear probes. In this paper, we aim to study parameter-efficient model adaptation strategies for vision transformers on the image classification task. We formulate efficient model adaptation as a subspace training problem and perform a comprehensive benchmarking over different efficient adaptation methods. We conduct an empirical study on each efficient model adaptation method focusing on its performance alongside parameter cost. Furthermore, we propose a parameter-efficient model adaptation framework, which first selects submodules by measuring local intrinsic dimensions and then projects them into subspace for further decomposition via a novel Kronecker Adaptation (KAdaptation) method. We analyze and compare our method with a diverse set of baseline model adaptation methods (including state-of-the-art methods for pretrained language models). Our method performs the best in terms of the tradeoff between accuracy and parameter efficiency across 20 image classification datasets under the few-shot setting and 7 image classification datasets under the full-shot setting.
Comparative Analysis of Different Text Summarization Techniques Using Enhanced Tokenization

As a huge amount of data is being generated everyday, text summarization is a must-have technique to obtain the required information concisely. Summaries reduce reading time. When it comes to researching documents, summaries make the job easier. The challenge of creating a short and fluent summary while retaining important information content and overall meaning is known as automatic text summarization. As a huge amount of data is being generated everyday, text summarization is a must-have technique to obtain the required information concisely. It is simple to deal with summarization in the other languages like English, Turkish, Arabic. But due to the diverse and complex nature of the Bangla language, not much has been done on the technique of summarizing the Bangla text. Given the importance of text summarization, this paper focused on the creation of an extraction-based summary approach that works on Bangla text documents. Here we apply different kind of model for generating a summary for a single bangla text document. As compared to other outcomes, our experimental results are outstanding and people who read the summary evaluated them. Further development of these methods will undoubtedly deliver fascinating results. This can also contribute significantly in the effort to build smart machines, which form the basis of industry 4.0.
Negotiation in Indonesian Culture: A Cultural Linguistic Analysis of Bahasa Indonesia Textbooks

One of the important goals of education is to shape good character among the students, and one of the sources of good character is culture. Therefore it is important to ensure that materials used in the teaching process are culturally appropriate. This article aims to elaborate on cultural content in Bahasa Indonesia textbooks especially in terms of negotiation. Using the Cultural Linguistics analysis, this study revealed three cultural schemas related to the role of building a good relationship, assertiveness, and communication style in negotiations.