Reviewerly/exHarmony huggingface dataset:
Rethinking the Dilated Encoder in TE-YOLOF: An Approach Based on Attention Mechanism to Improve Performance for Blood Cell Detection

Blood cell detection is an essential branch of microscopic imaging for disease diagnosis. TE-YOLOF is an effective model for blood cell detection, and was recently found to have an outstanding trade-off between accuracy and model complexity. However, there is a lack of understanding of whether the dilated encoder in TE-YOLOF works well for blood cell detection. To address this issue, we perform a thorough experimental analysis and find the interesting fact that the dilated encoder is not necessary for TE-YOLOF to perform the blood cell detection task. For the purpose of increasing performance on blood cell detection, in this research, we use the attention mechanism to dominate the dilated encoder place in TE-YOLOF and find that the attention mechanism is effective to address this problem. Based upon these findings, we propose a novel approach, named Enhanced Channel Attention Module (ECAM), based on attention mechanism to achieve precision improvement with less growth on model complexity. Furthermore, we examine the proposed ECAM method compared with other tip-top attention mechanisms and find that the proposed attention method is more effective on blood cell detection task. We incorporate the spatial attention mechanism in CBAM with our ECAM to form a new module, which is named Enhanced-CBAM. We propose a new network named Enhanced Channel Attention Network (ENCANet) based upon Enhanced-CBAM to perform blood cell detection on BCCD dataset. This network can increase the accuracy to 90.3 AP while the parameter is only 6.5 M. Our ENCANet is also effective for conducting cross-domain blood cell detection experiments.
Goal selection and feedback for solving math word problems

Solving Math Word Problems (MWPs) automatically is a challenging task for AI-tutoring in online education. Most of the existing State-Of-The-Art (SOTA) neural models for solving MWPs use Goal-driven Tree-structured Solver (GTS) as their decoders. However, owing to the defects of the tree-structured recurrent neural networks, GTS can not obtain the information of all generated nodes in each decoding time step. Therefore, the performance for long math expressions is not satisfactory enough. To address such limitations, we propose a Goal Selection and Feedback (GSF) decoding module. In each time step of GSF, we firstly feed the latest result back to all goal vectors through goal feedback operation, and then the goal selection operation based on attention mechanism is designed for generate the new goal vector. Not only can the decoder collect the historical information from all generated nodes through goal selection operation, but also these generated nodes are always updated timely by goal feedback operation. In addition, a Multilayer Fusion Network (MFN) is proposed to provide a better representation of each hidden state during decoding. Combining the ELECTRA language model with our novel decoder, experiments on the Math23k, Ape-clean, and MAWPS datasets show that our model outperforms the SOTA baselines, especially on the MWPs of complex samples with long math expressions. The ablation study and case study further verify that our model can better solve the samples with long expressions, and the proposed components are indeed able to help enhance the performance of the model.
Projective Ranking-based GNN Evasion Attacks

Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. However, GNNs are at risk of adversarial attacks. Two primary limitations of the current evasion attack methods are highlighted: (1) The current <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GradArgmax</i> ignores the “long-term” benefit of the perturbation. It is faced with zero-gradient and invalid benefit estimates in certain situations. (2) In the reinforcement learning-based attack methods, the learned attack strategies might not be transferable when the attack budget changes. To this end, we first formulate the perturbation space and propose an evaluation framework and the projective ranking method. We aim to learn a powerful attack strategy then adapt it as little as possible to generate adversarial samples under dynamic budget settings. In our method, based on mutual information, we rank and assess the attack benefits of each perturbation for an effective attack strategy. By projecting the strategy, our method dramatically minimizes the cost of learning a new attack strategy when the attack budget changes. In the comparative assessment with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GradArgmax</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RL-S2V</i> , the results show our method owns high attack performance and effective transferability. The visualization of our method also reveals various attack patterns in the generation of adversarial samples.
A Converse for Fault-tolerant Quantum Computation

As techniques for fault-tolerant quantum computation keep improving, it is natural to ask: what is the fundamental lower bound on redundancy? In this paper, we obtain a lower bound on the redundancy required for $\epsilon$-accurate implementation of a large class of operations that includes unitary operators. For the practically relevant case of sub-exponential depth and sub-linear gate size, our bound on redundancy is tighter than the known lower bounds. We obtain this bound by connecting fault-tolerant computation with a set of finite blocklength quantum communication problems whose accuracy requirements satisfy a joint constraint. The lower bound on redundancy obtained here leads to a strictly smaller upper bound on the noise threshold for non-degradable noise. Our bound directly extends to the case where noise at the outputs of a gate are non-i.i.d. but noise across gates are i.i.d.
State-of-the-art Models for Object Detection in Various Fields of Application

We present a list of datasets and their best models with the goal of advancing the state-of-the-art in object detection by placing the question of object recognition in the context of the two types of state-of-the-art methods: one-stage methods and two stage-methods. We provided an in-depth statistical analysis of the five top datasets in the light of recent developments in granulated Deep Learning models - COCO minival, COCO test, Pascal VOC 2007, ADE20K, and ImageNet. The datasets are handpicked after closely comparing them with the rest in terms of diversity, quality of data, minimal bias, labeling quality etc. More importantly, our work extends to provide the best combination of these datasets with the emerging models in the last two years. It lists the top models and their optimal use cases for each of the respective datasets. We have provided a comprehensive overview of a variety of both generic and specific object detection models, enlisting comparative results like inference time and average precision of box (AP) fixed at different Intersection Over Union (IoUs) and for different sized objects. The qualitative and quantitative analysis will allow experts to achieve new performance records using the best combination of datasets and models.
Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models

Generative models are becoming popular for the synthesis of medical images. Recently, neural diffusion models have demonstrated the potential to generate photo-realistic images of objects. However, their potential to generate medical images is not explored yet. In this work, we explore the possibilities of synthesis of medical images using neural diffusion models. First, we use a pre-trained DALLE2 model to generate lungs X-Ray and CT images from an input text prompt. Second, we train a stable diffusion model with 3165 X-Ray images and generate synthetic images. We evaluate the synthetic image data through a qualitative analysis where two independent radiologists label randomly chosen samples from the generated data as real, fake, or unsure. Results demonstrate that images generated with the diffusion model can translate characteristics that are otherwise very specific to certain medical conditions in chest X-Ray or CT images. Careful tuning of the model can be very promising. To the best of our knowledge, this is the first attempt to generate lungs X-Ray and CT images using neural diffusion models. This work aims to introduce a new dimension in artificial intelligence for medical imaging. Given that this is a new topic, the paper will serve as an introduction and motivation for the research community to explore the potential of diffusion models for medical image synthesis. We have released the synthetic images on https://www.kaggle.com/datasets/hazrat/awesomelungs.
Characterizing Intrinsic Compositionality in Transformers with Tree Projections

When trained on language data, do transformers learn some arbitrary computation that utilizes the full capacity of the architecture or do they learn a simpler, tree-like computation, hypothesized to underlie compositional meaning systems like human languages? There is an apparent tension between compositional accounts of human language understanding, which are based on a restricted bottom-up computational process, and the enormous success of neural models like transformers, which can route information arbitrarily between different parts of their input. One possibility is that these models, while extremely flexible in principle, in practice learn to interpret language hierarchically, ultimately building sentence representations close to those predictable by a bottom-up, tree-structured model. To evaluate this possibility, we describe an unsupervised and parameter-free method to \emph{functionally project} the behavior of any transformer into the space of tree-structured networks. Given an input sentence, we produce a binary tree that approximates the transformer's representation-building process and a score that captures how "tree-like" the transformer's behavior is on the input. While calculation of this score does not require training any additional models, it provably upper-bounds the fit between a transformer and any tree-structured approximation. Using this method, we show that transformers for three different tasks become more tree-like over the course of training, in some cases unsupervisedly recovering the same trees as supervised parsers. These trees, in turn, are predictive of model behavior, with more tree-like models generalizing better on tests of compositional generalization.
Multisensor Random Finite Set Information Fusion

In this chapter, we provide an overview of cutting-edge approaches and remaining challenges in multisensor multitarget information fusion based on the random finite set (RFS) framework. The fusion that plays a fundamental role in multisensor collaboration is classified into data-level multitarget measurement fusion and estimate-level multitarget density fusion, which share and fuse local measurements (typically the corresponding likelihood function) and posterior densities (including both cardinality distribution and multitarget localization distribution) between sensors, respectively. In particular, two optimization-oriented density-averaging approaches, namely arithmetic-average fusion and geometric average fusion, are addressed in detail for various RFSs. Important properties of each fusion rule including the optimality, sub-optimality, conservativeness, advantages and disadvantages are presented. Remaining challenges and emerging research topics such as the continuous-time trajectory modeling and estimation, and heterogeneous fusion are also highlighted.
THE PERFORMANCE OF GOOGLE TRANSLATE IN TRANSLATING THREE CATHOLIC FUNDAMENTAL PRAYERS

Literary translation is one of the most difficult areas of machine translation (MT) development. Accuracy in MT is susceptible to issues that are frequently encountered in literary works, such as lexical ambiguity, syntax complexity, and structural grammatical constructs. This study examines the literary translation of three Catholic prayers: the "Sign of the Cross," "The Lord's Prayer," and "Hail Mary." These objects were chosen for their language's unique characteristics, which include antiquated vocabulary, strange structures, and unusual line breaks. The research is conducted to determine the MT's ability, that is represented by Google Translate (GT), to overcome hurdles in literary translation as measured by the number of errors made, their discussion, and their relative difficulty rectification based on Hutchins and Somers' assertions. The quality of a translation, whether human or computer, has long been a subject of discussion, with no universally accepted metric. There are a variety of factors to consider based on the circumstances around the exercise, including accuracy, naturalness, fluency, and function. For machine translation, which is frequently used to obtain quick information about a document, semantic accuracy should definitely take precedence over fluency as Koponen states.The research attemps to measure GT’s performance in translating three Catholic prayers using Koponen’s error category. Koponen's notion of semantic accuracy categorizes the errors produced by the MT into two broad categories: individual concept errors and relational concept errors. The two groups are further subdivided. The results indicate that GT is underperformed when confronted with the individual concept errors, but performs admirably when the ST line is straightforward and well-structured. GT made a total of 12 errors of individual concepts, but generated no "destructive error” in relational concepts. In conclusion, GT shows a sufficiently reliable translation, and performs admirably and consistently in the three Catholic prayers.
Revisiting Artistic Style Transfer for Data Augmentation in A Real-Case Scenario

A tremendous number of techniques have been proposed to transfer artistic style from one image to another. In particular, techniques exploiting neural representation of data; from Convolutional Neural Networks to Generative Adversarial Networks. However, most of these techniques do not accurately account for the semantic information related to the objects present in both images or require a considerable training set. In this paper, we provide a data augmentation technique that is as faithful as possible to the style of the reference artist, while requiring as few training samples as possible, as artworks containing the same semantics of an artist are usually rare. Hence, this paper aims to improve the state-of-the-art by first applying semantic segmentation on both images to then transfer the style from the painting to a photo while preserving common semantic regions. The method is exemplified on Van Gogh’s paintings, shown to be challenging to segment.
Reinforcing Neuron Extraction from Calcium Imaging Data via Depth-Estimation Constrained Nonnegative Matrix Factorization

We develop a novel depth-estimation constrained nonnegative matrix factorization framework for reinforcing neuron extraction, which takes advantage of accurate depth estimation to alleviate the scattering of raw data and compensate for depth loss in the vectorization operation. Established on bright and dark channel priors, a depth-dependent transmission estimation model is effective in estimating the more accurate depth of raw data, where the atmospheric light of each pixel is finely estimated to alleviate the problem of image descattering and overcome the limitation of conventionally constant assumption. Besides, our framework is simply implemented in constrained nonnegative matrix factorization, and can be flexibly accommodated to various neuron extraction approaches. Extensive experiments confirm the superior performance of our framework in terms of reinforcing more accuracy of neuron extraction and demixing better results of spatially overlapping neurons, moreover, the utility of our depth-estimation model is proved for imaging whole brain of zebrafish larvae.
On the practical usefulness of the Hardware Efficient Ansatz

Variational Quantum Algorithms (VQAs) and Quantum Machine Learning (QML) models train a parametrized quantum circuit to solve a given learning task. The success of these algorithms greatly hinges on appropriately choosing an ansatz for the quantum circuit. Perhaps one of the most famous ansatzes is the one-dimensional layered Hardware Efficient Ansatz (HEA), which seeks to minimize the effect of hardware noise by using native gates and connectives. The use of this HEA has generated a certain ambivalence arising from the fact that while it suffers from barren plateaus at long depths, it can also avoid them at shallow ones. In this work, we attempt to determine whether one should, or should not, use a HEA. We rigorously identify scenarios where shallow HEAs should likely be avoided (e.g., VQA or QML tasks with data satisfying a volume law of entanglement). More importantly, we identify a Goldilocks scenario where shallow HEAs could achieve a quantum speedup: QML tasks with data satisfying an area law of entanglement. We provide examples for such scenario (such as Gaussian diagonal ensemble random Hamiltonian discrimination), and we show that in these cases a shallow HEA is always trainable and that there exists an anti-concentration of loss function values. Our work highlights the crucial role that input states play in the trainability of a parametrized quantum circuit, a phenomenon that is verified in our numerics.
PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales

Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters. To make this reasoning process more explicit, recent works retrieve a rationalizing LM's internal knowledge by training or prompting it to generate free-text rationales, which can be used to guide task predictions made by either the same LM or a separate reasoning LM. However, rationalizing LMs require expensive rationale annotation and/or computation, without any assurance that their generated rationales improve LM task performance or faithfully reflect LM decision-making. In this paper, we propose PINTO, an LM pipeline that rationalizes via prompt-based learning, and learns to faithfully reason over rationales via counterfactual regularization. First, PINTO maps out a suitable reasoning process for the task input by prompting a frozen rationalizing LM to generate a free-text rationale. Second, PINTO's reasoning LM is fine-tuned to solve the task using the generated rationale as context, while regularized to output less confident predictions when the rationale is perturbed. Across four datasets, we show that PINTO significantly improves the generalization ability of the reasoning LM, yielding higher performance on both in-distribution and out-of-distribution test sets. Also, we find that PINTO's rationales are more faithful to its task predictions than those generated by competitive baselines.
Fast and robust Bayesian Inference using Gaussian Processes with GPry

We present the GPry algorithm for fast Bayesian inference of general (non-Gaussian) posteriors with a moderate number of parameters. GPry does not need any pre-training, special hardware such as GPUs, and is intended as a drop-in replacement for traditional Monte Carlo methods for Bayesian inference. Our algorithm is based on generating a Gaussian Process surrogate model of the log-posterior, aided by a Support Vector Machine classifier that excludes extreme or non-finite values. An active learning scheme allows us to reduce the number of required posterior evaluations by two orders of magnitude compared to traditional Monte Carlo inference. Our algorithm allows for parallel evaluations of the posterior at optimal locations, further reducing wall-clock times. We significantly improve performance using properties of the posterior in our active learning scheme and for the definition of the GP prior. In particular we account for the expected dynamical range of the posterior in different dimensionalities. We test our model against a number of synthetic and cosmological examples. GPry outperforms traditional Monte Carlo methods when the evaluation time of the likelihood (or the calculation of theoretical observables) is of the order of seconds; for evaluation times of over a minute it can perform inference in days that would take months using traditional methods. GPry is distributed as an open source Python package (pip install gpry) and can also be found at https://github.com/jonaselgammal/GPry.
Use and adoption of digital educommunication media by university professors during the Covid-19 pandemic: cases of the National Autonomous University of Mexico and the Autonomous University of Chihuahua

This article analyzes the educational use of digital educommunication media (DEM) by four groups of professors working at the faculties of Philosophy and Letters and of Engineering at the Autonomous University of Chihuahua (UACH) and at the National Autonomous University of Mexico (UNAM). Seven hypotheses and three research questions were posed, which were related to the use of DEM (specifically: images, animations, and video; presentations (PowerPoint/Prezi); digital texts; the Cloud; social media/instant messaging; and email), their qualities and the differences regarding their use, among the different groups studied. A two-phase mixed-methods explanatory sequential approach was employed, with a first phase of quantitative data collection and a second qualitative phase. A stratified sample of 177 professors was selected, which was distributed proportionally between the two selected faculties and universities. All professors completed a 144-item questionnaire in the first phase, and on the basis of their answers, ten professors were selected to be interviewed in the second phase, to ensure the diversity of the interviewed group in terms of sex, age, faculty, and educational level at which they teach, as well as their teaching experience and experience in the use of DEM. Among the results, we found that: a) the choice between using DEM or traditional media in class was not determined by teachers’ perception about their students’ learning with technology; b) the characteristics of each DEM determined its use, but the use of a given DEM was not related to the activities that it could enable; c) the professors exclusively teaching in graduate programs, the younger ones, those from UNAM, or those of Engineering did not present significant differences to their counterparts in terms of their use and assessment of DEM; and d) the qualitative data reaffirmed these trends and helped typify the challenges and opportunities of using DEM, particularly those that arose from the period of the exclusively online education model that was adopted owing to the COVID-19 pandemic.
Methods for Measuring the Formation of Design and Research Activity of Students

The paper investigates a methodology for measuring and assessing the formation of design and research activities of students in the study of the theory of limits in mathematical analysis. The research required solving the problem of measuring the impact of problem-based learning, independent study on the level of students' design and research activities. To control the quality of students' knowledge, the indicators of mastering the materials are conventionally divided into 4 levels: the level of familiarity, the level of reproduction, the level of skills and abilities and the level of creativity. The results of the study showed that experimental materials had a positive effect on the growth of the level of knowledge, skills and abilities of students, and experimental materials can be included in the content of the main course of mathematical analysis.&#x0D; &#x0D; Received: 10 July 2022 / Accepted: 5 October 2022 / Published: 5 November 2022