Reviewerly/exHarmony huggingface dataset:
Depth Estimation Using Sparse Depth and Transformer

Depth prediction from single image is a challenging task due to the intra scale ambiguity and unavailability of prior information. The prediction of an unambiguous depth from single RGB image is very important aspect for computer vision applications. In this paper, an end-to-end sparse-to-dense network using transformers is proposed for depth estimation. The proposed network processes single images along with the additional sparse depth samples which have been generated for depth estimation. The additional sparse depth sample are acquired either with a low-resolution depth sensor or calculated by visual simultaneous localization. Here, we have proposed a model that utilises both sparse samples and transformers and along with a encoder-decoder structure that helps us in giving great depth results that are comparable to other state-of-the-art results.
Identifying Opioid Use Disorder from Longitudinal Healthcare Data using a Multi-stream Transformer.

Opioid Use Disorder (OUD) is a public health crisis costing the US billions of dollars annually in healthcare, lost workplace productivity, and crime. Analyzing longitudinal healthcare data is critical in addressing many real-world problems in healthcare. Leveraging the real-world longitudinal healthcare data, we propose a novel multi-stream transformer model called MUPOD for OUD identification. MUPOD is designed to simultaneously analyze multiple types of healthcare data streams, such as medications and diagnoses, by attending to segments within and across these data streams. Our model tested on the data from 392,492 patients with long-term back pain problems showed significantly better performance than the traditional models and recently developed deep learning models.
On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data

Borrowing ideas from Production functions in micro-economics, in this paper we introduce a framework to systematically evaluate the performance and cost trade-offs between machinetranslated and manually-created labelled data for task-specific fine-tuning of massively multilingual language models.We illustrate the effectiveness of our framework through a casestudy on the TyDIQA-GoldP dataset.One of the interesting conclusions of the study is that if the cost of machine translation is greater than zero, the optimal performance at least cost is always achieved with at least some or only manually-created data.To our knowledge, this is the first attempt towards extending the concept of production functions to study data collection strategies for training multilingual models, and can serve as a valuable tool for other similar cost vs data trade-offs in NLP.
Response Construct Tagging: NLP-Aided Assessment for Engineering Education

Recent advances in natural language processing (NLP) have greatly helped educational applications, for both teachers and students.In higher education, there is great potential to use NLP tools for advancing pedagogical research.In this paper, we focus on how NLP can help understand student experiences in engineering, thus facilitating engineering educators to carry out large scale analysis that is helpful for redesigning the curriculum.Here, we introduce a new task we call response construct tagging (RCT), in which student responses to tailored survey questions are automatically tagged for six constructs measuring transformative experiences and engineering identity of students.We experiment with state-of-the-art classification models for this task and investigate the effects of different sources of additional information.Our best model achieves an F1 score of 48.We further investigate multi-task training on the related task of sentiment classification, which improves our model's performance to 55 F1.Finally, we provide a detailed qualitative analysis of model performance.
Strengths and challenges of the artificial intelligence in the assessment of dense breasts

Objectives: High breast density is a risk factor for breast cancer and overlapping of glandular tissue can mask lesions thus lowering mammographic sensitivity. Also, dense breasts are more vulnerable to increase recall rate and false-positive results. New generations of artificial intelligence (AI) have been introduced to the realm of mammography. We aimed to assess the strengths and challenges of adopting artificial intelligence in reading mammograms of dense breasts. Methods: This study included 6600 mammograms of dense patterns “c” and “d” and presented 4061 breast abnormalities. All the patients were subjected to full-field digital mammography, breast ultrasound, and their mammographic images were scanned by AI software (Lunit INSIGHT MMG). Results: Diagnostic indices of the sono-mammography: a sensitivity of 98.71%, a specificity of 88.04%, a positive-predictive value of 80.16%, a negative-predictive value of 99.29%, and a diagnostic accuracy of 91.5%. AI-aided mammograms presented sensitivity of 88.29%, a specificity of 96.34%, a positive-predictive value of 92.2%, a negative-predictive value of 94.4%, and a diagnostic accuracy of 94.5% in its ability to read dense mammograms Conclusion: Dense breasts scanned with AI showed a notable reduction of mammographic misdiagnosis. Knowledge of such software challenges would enhance its application as a decision support tool to mammography in the diagnosis of cancer. Advances in knowledge: Dense breast is challenging for radiologists and renders low sensitivity mammogram. Mammogram scanned by AI could be used to overcome such limitation, enhance the discrimination between benign and malignant breast abnormalities and the early detection of breast cancer.
Linking Use Cases and Associated Requirements

A wide variety of use case templates supports different variants to link a use case with its associated requirements. Regardless of the linking, a reader must process the related information simultaneously to understand them.Linking variants are intended to cause a specific reading behavior in which a reader interrelates a use case and its associated requirements. Due to the effort to create and maintain links, we investigated the impact of different linking variants on the reading behavior in terms of visual effort and the intended way of interrelating both artifacts.We designed an eye tracking study about reading a use case and requirements. We conducted the study twice each with 15 subjects as a baseline experiment and as a repetition. The results of the baseline experiment, its repetition, and their joint analysis are consistent. All investigated linking variants cause comparable visual effort. In all cases, reading the single artifacts one after the other is the most frequently occurring behavior. Only links embedded in the fields of a use case description significantly increase the readers’ efforts to interrelate both artifacts. None of the investigated linking variants impedes reading a use case and requirements. However, only the most detailed linking variant causes readers to process related information simultaneously.
Examining online learning motivations of university students studying via distance education in the Covid-19 pandemic by digital literacy variable

Purpose: A total of 422 university students participates in this study where the digital literacy of university students studying via distance education in the Covid-19 pandemic by online learning motivation variable is examined. Methodology: In the study, Personal Information Form, Digital Literacy Scale (DLS) and Online Learning Motivation Scale (OLMS) are used. The research is structured in descriptive and relational scanning models. Findings: According to the data obtained, it is stated that the digital literacy and online learning motivation of the students within the scope of the research is at medium level; digital literacy levels do not differ by gender and grade level; there is a statistically significant difference in online learning motivation level of students in favor of female students; on the other hand, in the score of the whole online learning motivation scale, there is a significant difference between 2nd and 4th grade students in favor of 2nd grade students and between 3rd and 4th grade students in favor of 3rd grade students; there is a moderately positive and statistically significant relationship between students’ digital literacy and online learning motivation levels and their online learning motivation and digital literacy predict each other by 21.8 %. Highlights: Given that 21.8 % of students’ online learning motivation is interpreted by their digital literacy or vice versa, increasing the development of students’ both digital literacy and online learning motivation levels with in-school and out-of-school trainings is suggested.
Classification of Breast Cancer Based on Histopathological Image Using EfficientNet-B0 on Convolutional Neural Network

Breast cancer has been chosen as the leading cause of cancer-related death in women. Biopsy is still the most accurate way to detect cancer cells. However, this is time-consuming and requires a relatively expensive cost and requires a pathologist. Advances in machine learning make it possible to detect and diagnose breast cancer using histopathological images that are the result of a biopsy. BreakHis dataset is a dataset that provides histopathological images. This study proposes the use of this dataset for cancer classification based on histopathological images using EfficientNet-B0 on the Convolutional Neural Network (CNN). The purpose of this study is to improve the performance of previous studies and to determine the effect of augmentation and dropout layers on the proposed model. This study also applies cross-validation to get more accurate results. The results showed that the EfficientNet-B0 model combined with augmentation and dropout layers with the application of kfold cross validation k=7 was able to improve performance in previous studies with an accuracy of 98.90%. The application of data augmentation and dropout layer techniques has also been shown to increase accuracy and reduce overfitting of the proposed model.
Effectiveness of Augmented Reality-Based Applications on Liquid Measurement Theme in Mathematics Course: An Experimental Study

This research examines augmented reality-based applications prepared for primary school third grade students concerning academic achievement and anxiety in a mathematics course. A quasi-experimental design, one of the experimental research designs, with a pretest-posttest control group was preferred in this study. The research group of the research consisted of 26 students attending grade 3 in a school in Siirt during the 2020-2021 academic year. Participants were determined as two different groups, 13 of which were experimental and 13 of which were control groups. In the research, AR-based applications were designed for various gains aimed at the "Liquid measurement" theme in the mathematics course, and these applications were presented to the students through the tablet. These applications were conducted during five weeks, including pre and post-test. Research data were collected through data sets obtained from achievement test and anxiety scale that were carried out before and after the application. Findings of the research indicate that there is a significant difference between the experimental and control groups in favor of the experimental group. It was found that augmented reality-based applications increased the academic achievement of the experimental group students and reduced their anxiety levels concerning the mathematics lesson.
Bias-variance decomposition of overparameterized regression with random linear features

In classical statistics, the bias-variance trade-off describes how varying a model's complexity (e.g., number of fit parameters) affects its ability to make accurate predictions. According to this trade-off, optimal performance is achieved when a model is expressive enough to capture trends in the data, yet not so complex that it overfits idiosyncratic features of the training data. Recently, it has become clear that this classic understanding of the bias variance must be fundamentally revisited in light of the incredible predictive performance of overparameterized models-models that avoid overfitting even when the number of fit parameters is large enough to perfectly fit the training data. Here, we present results for one of the simplest examples of an overparameterized model: regression with random linear features (i.e., a two-layer neural network with a linear activation function). Using the zero-temperature cavity method, we derive analytic expressions for the training error, test error, bias, and variance. We show that the linear random features model exhibits three phase transitions: two different transitions to an interpolation regime where the training error is zero, along with an additional transition between regimes with large bias and minimal bias. Using random matrix theory, we show how each transition arises due to small nonzero eigenvalues in the Hessian matrix. Finally, we compare and contrast the phase diagram of the random linear features model to the random nonlinear features model and ordinary regression, highlighting the additional phase transitions that result from the use of linear basis functions.
Algorithmic Improvement and GPU Acceleration of the GenASM Algorithm

We improve on GenASM, a recent algorithm for genomic sequence alignment, by significantly reducing its memory footprint and bandwidth requirement. Our algorithmic improvements reduce the memory footprint by 24 × and the number of memory accesses by 12 ×. We efficiently parallelize the algorithm for GPUs, achieving a 4.1 × speedup over a CPU implementation of the same algorithm, a 62× speedup over minimap2's CPU-based KSW2 and a 7.2 × speedup over the CPU-based Edlib for long reads.
Adaptive Real-Time Gesture Recognition in a Dynamic Scenario for Human-Robot Collaborative Applications

One of the enabling technologies of Industry 4.0 is represented by collaborative robotics. The basis of its success lies in the increase in safety, productivity and flexibility but above all in the improvement of the employees working conditions, as well as their well-being. Human Robot Collaboration (HRC) applications are increasing due to the ease of communication between robots and humans. To this aim, this paper presents a vision based hand gesture recognition to help humans manage the workstation operations. An application with three levels of access has been proposed to guarantee an appropriate level of user friendliness. The system provides both hardware and software support for real-time gesture recognition. The developed system was tested using a standard webcam and applied to a case study to control the tasks performed by UR10e. Thanks to its fast and on point recognition, it can be used as a real-time gesture image recognition system.
Experimental optimal verification of three-dimensional entanglement on a silicon chip

Abstract High-dimensional entanglement is significant for the fundamental studies of quantum physics and offers unique advantages in various quantum information processing tasks. Integrated quantum devices have recently emerged as a promising platform for creating, processing, and detecting complex high-dimensional entangled states. A crucial step toward practical quantum technologies is to verify that these devices work reliably with an optimal strategy. In this work, we experimentally implement an optimal quantum verification strategy on a three-dimensional maximally entangled state using local projective measurements on a silicon photonic chip. A 95% confidence is achieved from 1190 copies to verify the target quantum state. The obtained scaling of infidelity as a function of the number of copies is −0.5497 ± 0.0002, exceeding the standard quantum limit of −0.5 with 248 standard deviations. Our results indicate that quantum state verification could serve as an efficient tool for complex quantum measurement tasks.
Modeling and simulating the sample complexity of solving LWE using BKW-style algorithms

Abstract The Learning with Errors (LWE) problem receives much attention in cryptography, mainly due to its fundamental significance in post-quantum cryptography. Among its solving algorithms, the Blum-Kalai-Wasserman (BKW) algorithm, originally proposed for solving the Learning Parity with Noise (LPN) problem, performs well, especially for certain parameter settings with cryptographic importance. The BKW algorithm consists of two phases, the reduction phase and the solving phase. In this work, we study the performance of distinguishers used in the solving phase. We show that the Fast Fourier Transform (FFT) distinguisher from Eurocrypt’15 has the same sample complexity as the optimal distinguisher, when making the same number of hypotheses. We also show via simulation that it performs much better than previous theory predicts and develop a sample complexity model that matches the simulations better. We also introduce an improved, pruned version of the FFT distinguisher. Finally, we indicate, via extensive experiments, that the sample dependency due to both LF2 and sample amplification is limited.
Introduction

Explainable Natural Language Processing (NLP)1 is a new subfield of NLP, only really taking off within the last four or five years.2 Whenever a new subfield of a scientific discipline emerges, it almost by definition attracts a mixed crowd of researchers from related, but different subfields. These researchers of course do not yet form a consolidated network and also typically bring slightly different traditions, languages, and protocols from their respective subfields. This, undoubtedly, leads to a lot of redundancy: researchers presenting what is essentially the same, in radically different ways; researchers reinventing what they did not know already existed; and researchers proposing new models that only in the absence of established evaluation protocols, seem superior to existing ones.
Seismic Signal Denoising using U-Net in the Time-Frequency Domain

Signal denoising is one of the main routines comprised in the seismic data processing chain in order to improve the signal-to-noise ratio (SNR) of registered signals. In this paper, we propose a new method for seismic signal denoising based on a U-Net convolutional neural network architecture. The model is able to learn a decomposition of the noisy seismic signal into the denoised version of the signal and noise. This decomposition is performed in the time-frequency domain, by learning masks to extract both the denoised seismic signal and the corresponding noise. In order to prove the effectiveness of the proposed approach, we use a publicly available dataset, namely the Stanford Earthquake Dataset (STEAD).
On the Expressiveness of Mixed Choice Sessions

Session types provide a flexible programming style for structuring interaction, and are used to guarantee a safe and consistent composition of distributed processes. Traditional session types include only one-directional input (external) and output (internal) guarded choices. This prevents the session-processes to explore the full expressive power of the pi-calculus where the mixed choices are proved more expressive than the (non-mixed) guarded choices. To account this issue, recently Casal, Mordido, and Vasconcelos proposed the binary session types with mixed choices (CMV+). This paper carries a surprising, unfortunate result on CMV+: in spite of an inclusion of unrestricted channels with mixed choice, CMV+'s mixed choice is rather separate and not mixed. We prove this negative result using two methodologies (using either the leader election problem or a synchronisation pattern as distinguishing feature), showing that there exists no good encoding from the pi-calculus into CMV+, preserving distribution. We then close their open problem on the encoding from CMV+ into CMV (without mixed choice), proving its soundness and thereby that the encoding is good up to coupled similarity.
Native measurement-based quantum approximate optimization algorithm applied to the Max <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> -Cut problem

Photonic quantum computers, programmed within the framework of the measurement-based quantum computing (MBQC), currently concur with gate-based platforms in the race towards useful quantum advantage, and some algorithms emerged as main candidates to reach this goal in the near term. Yet, the majority of these algorithms are only expressed in the gate-based model of computation, which is incompatible with photonic platforms. Methods to translate gate-based algorithms into the MBQC framework exist, but they are not always optimal in terms of resource cost. In our work, we propose an MBQC algorithm to run the Quantum Approximate Optimization Algorithm (QAOA). Furthermore, we apply the MBQC-QAOA algorithm to the MAX $K$-CUT problem, working for all values of $K$, expressing the cost Hamiltonian and its constraints in a form easily implementable in the MBQC model. We conclude analyzing the resource-cost of our algorithm, compared to the case of translating a gate-based QAOA algorithm into MBQC rules showing up to a 30-fold improvement. With our work, we contribute to close the gap between gate-based and MBQC near-term algorithms, a gap not reflecting the current status of the hardware development.
Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target

The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target.