Reviewerly/exHarmony huggingface dataset:
On Some Closure Properties of nc-eNCE Graph Grammars

In the study of automata and grammars, closure properties of the associated languages have been studied extensively. In particular, closure properties of various types of graph grammars have been examined in (Rozenberg and Welzl, Inf. and Control,1986) and (Rozenberg and Welzl, Acta Informatica,1986). In this paper we examine some critical closure properties of the nc-eNCE graph grammars discussed in (Jayakrishna and Mathew, Symmetry 2023) and (Jayakrishna and Mathew, ICMICDS 2022).
Learning nonparametric DAGs with incremental information via high-order HSIC

Score-based methods for learning Bayesain networks(BN) aim to maximizing the global score functions. However, if local variables have direct and indirect dependence simultaneously, the global optimization on score functions misses edges between variables with indirect dependent relationship, of which scores are smaller than those with direct dependent relationship. In this paper, we present an identifiability condition based on a determined subset of parents to identify the underlying DAG. By the identifiability condition, we develop a two-phase algorithm namely optimal-tuning (OT) algorithm to locally amend the global optimization. In the optimal phase, an optimization problem based on first-order Hilbert-Schmidt independence criterion (HSIC) gives an estimated skeleton as the initial determined parents subset. In the tuning phase, the skeleton is locally tuned by deletion, addition and DAG-formalization strategies using the theoretically proved incremental properties of high-order HSIC. Numerical experiments for different synthetic datasets and real-world datasets show that the OT algorithm outperforms existing methods. Especially in Sigmoid Mix model with the size of the graph being ${\rm\bf d=40}$, the structure intervention distance (SID) of the OT algorithm is 329.7 smaller than the one obtained by CAM, which indicates that the graph estimated by the OT algorithm misses fewer edges compared with CAM.Source code of the OT algorithm is available at https://github.com/YafeiannWang/optimal-tune-algorithm.
A Case Study on Context Encoding in Multi-Encoder based Document-Level Neural Machine Translation

Recent studies have shown that the multi-encoder models are agnostic to the choice of context, and the context encoder generates noise which helps improve the models in terms of BLEU score. In this paper, we further explore this idea by evaluating with context-aware pronoun translation test set by training multi-encoder models trained on three different context settings viz, previous two sentences, random two sentences, and a mix of both as context. Specifically, we evaluate the models on the ContraPro test set to study how different contexts affect pronoun translation accuracy. The results show that the model can perform well on the ContraPro test set even when the context is random. We also analyze the source representations to study whether the context encoder generates noise. Our analysis shows that the context encoder provides sufficient information to learn discourse-level information. Additionally, we observe that mixing the selected context (the previous two sentences in this case) and the random context is generally better than the other settings.
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction

The recently proposed stochastic Polyak stepsize (SPS) and stochastic line-search (SLS) for SGD have shown remarkable effectiveness when training over-parameterized models. However, in non-interpolation settings, both algorithms only guarantee convergence to a neighborhood of a solution which may result in a worse output than the initial guess. While artificially decreasing the adaptive stepsize has been proposed to address this issue (Orvieto et al. [2022]), this approach results in slower convergence rates for convex and over-parameterized models. In this work, we make two contributions: Firstly, we propose two new variants of SPS and SLS, called AdaSPS and AdaSLS, which guarantee convergence in non-interpolation settings and maintain sub-linear and linear convergence rates for convex and strongly convex functions when training over-parameterized models. AdaSLS requires no knowledge of problem-dependent parameters, and AdaSPS requires only a lower bound of the optimal function value as input. Secondly, we equip AdaSPS and AdaSLS with a novel variance reduction technique and obtain algorithms that require $\smash{\widetilde{\mathcal{O}}}(n+1/\epsilon)$ gradient evaluations to achieve an $\mathcal{O}(\epsilon)$-suboptimality for convex functions, which improves upon the slower $\mathcal{O}(1/\epsilon^2)$ rates of AdaSPS and AdaSLS without variance reduction in the non-interpolation regimes. Moreover, our result matches the fast rates of AdaSVRG but removes the inner-outer-loop structure, which is easier to implement and analyze. Finally, numerical experiments on synthetic and real datasets validate our theory and demonstrate the effectiveness and robustness of our algorithms.
Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classification

This paper presents a computer-aided cytology diagnosis system designed for animals, focusing on image quality assessment (IQA) using Convolutional Neural Networks (CNNs). The system's building blocks are tailored to seamlessly integrate IQA, ensuring reliable performance in disease classification. We extensively investigate the CNN's ability to handle various image variations and scenarios, analyzing the impact on detecting low-quality input data. Additionally, the network's capacity to differentiate valid cellular samples from those with artifacts is evaluated. Our study employs a ResNet18 network architecture and explores the effects of input sizes and cropping strategies on model performance. The research sheds light on the significance of CNN-based IQA in computer-aided cytology diagnosis for animals, enhancing the accuracy of disease classification.
Test-Time Backdoor Defense via Detecting and Repairing

Deep neural networks have played a crucial part in many critical domains, such as autonomous driving, face recognition, and medical diagnosis. However, deep neural networks are facing security threats from backdoor attacks and can be manipulated into attacker-decided behaviors by the backdoor attacker. To defend the backdoor, prior research has focused on using clean data to remove backdoor attacks before model deployment. In this paper, we investigate the possibility of defending against backdoor attacks at test time by utilizing partially poisoned data to remove the backdoor from the model. To address the problem, a two-stage method Test-Time Backdoor Defense (TTBD) is proposed. In the first stage, we propose a backdoor sample detection method DDP to identify poisoned samples from a batch of mixed, partially poisoned samples. Once the poisoned samples are detected, we employ Shapley estimation to calculate the contribution of each neuron's significance in the network, locate the poisoned neurons, and prune them to remove backdoor in the models. Our experiments demonstrate that TTBD removes the backdoor successfully with only a batch of partially poisoned data across different model architectures and datasets against different types of backdoor attacks.
Generating quantum channels from functions on discrete sets

Using the recent ability of quantum computers to initialize quantum states rapidly with high fidelity, we use a function operating on a discrete set to create a simple class of quantum channels. Fixed points and periodic orbits, that are present in the function, generate fixed points and periodic orbits in the associated quantum channel. Phenomenology such as periodic doubling is visible in a 6 qubit dephasing channel constructed from a truncated version of the logistic map. Using disjoint subsets, discrete function-generated channels can be constructed that preserve coherence within subspaces. Error correction procedures can be in this class as syndrome detection uses an initialized quantum register. A possible application for function-generated channels is in hybrid classical/quantum algorithms. We illustrate how these channels can aid in carrying out classical computations involving iteration of non-invertible functions on a quantum computer with the Euclidean algorithm for finding the greatest common divisor of two integers.
Visual Overviews for Sheet Music Structure

We propose different methods for alternative representation and visual augmentation of sheet music that help users gain an overview of general structure, repeating patterns, and the similarity of segments. To this end, we explored mapping the overall similarity between sections or bars to colors. For these mappings, we use dimensionality reduction or clustering to assign similar segments to similar colors and vice versa. To provide a better overview, we further designed simplified music notation representations, including hierarchical and compressed encodings. These overviews allow users to display whole pieces more compactly on a single screen without clutter and to find and navigate to distant segments more quickly. Our preliminary evaluation with guitarists and tablature shows that our design supports users in tasks such as analyzing structure, finding repetitions, and determining the similarity of specific segments to others.
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models

Current deep networks are very data-hungry and benefit from training on largescale datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data can be generated infinitely using generative models such as DALL-E and diffusion models, with minimal effort and cost. In this paper, we present DatasetDM, a generic dataset generation model that can produce diverse synthetic images and the corresponding high-quality perception annotations (e.g., segmentation masks, and depth). Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation. We show that the rich latent code of the diffusion model can be effectively decoded as accurate perception annotations using a decoder module. Training the decoder only needs less than 1% (around 100 images) manually labeled images, enabling the generation of an infinitely large annotated dataset. Then these synthetic data can be used for training various perception models for downstream tasks. To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation. Notably, it achieves 1) state-of-the-art results on semantic segmentation and instance segmentation; 2) significantly more robust on domain generalization than using the real data alone; and state-of-the-art results in zero-shot segmentation setting; and 3) flexibility for efficient application and novel task composition (e.g., image editing). The project website and code can be found at https://weijiawu.github.io/DatasetDM_page/ and https://github.com/showlab/DatasetDM, respectively
Quantifying and estimating dependence via sensitivity of conditional distributions

Recently established, directed dependence measures for pairs $(X,Y)$ of random variables build upon the natural idea of comparing the conditional distributions of $Y$ given $X=x$ with the marginal distribution of $Y$. They assign pairs $(X,Y)$ values in $[0,1]$, the value is $0$ if and only if $X,Y$ are independent, and it is $1$ exclusively for $Y$ being a function of $X$. Here we show that comparing randomly drawn conditional distributions with each other instead or, equivalently, analyzing how sensitive the conditional distribution of $Y$ given $X=x$ is on $x$, opens the door to constructing novel families of dependence measures $\Lambda_\varphi$ induced by general convex functions $\varphi: \mathbb{R} \rightarrow \mathbb{R}$, containing, e.g., Chatterjee's coefficient of correlation as special case. After establishing additional useful properties of $\Lambda_\varphi$ we focus on continuous $(X,Y)$, translate $\Lambda_\varphi$ to the copula setting, consider the $L^p$-version and establish an estimator which is strongly consistent in full generality. A real data example and a simulation study illustrate the chosen approach and the performance of the estimator. Complementing the afore-mentioned results, we show how a slight modification of the construction underlying $\Lambda_\varphi$ can be used to define new measures of explainability generalizing the fraction of explained variance.
Change Point Detection with Conceptors

Offline change point detection retrospectively locates change points in a time series. Many nonparametric methods that target i.i.d. mean and variance changes fail in the presence of nonlinear temporal dependence, and model based methods require a known, rigid structure. For the at most one change point problem, we propose use of a conceptor matrix to learn the characteristic dynamics of a baseline training window with arbitrary dependence structure. The associated echo state network acts as a featurizer of the data, and change points are identified from the nature of the interactions between the features and their relationship to the baseline state. This model agnostic method can suggest potential locations of interest that warrant further study. We prove that, under mild assumptions, the method provides a consistent estimate of the true change point, and quantile estimates are produced via a moving block bootstrap of the original data. The method is evaluated with clustering metrics and Type 1 error control on simulated data, and applied to publicly available neural data from rats experiencing bouts of non-REM sleep prior to exploration of a radial maze. With sufficient spacing, the framework provides a simple extension to the sparse, multiple change point problem.
Continual Face Forgery Detection via Historical Distribution Preserving

Face forgery techniques have advanced rapidly and pose serious security threats. Existing face forgery detection methods try to learn generalizable features, but they still fall short of practical application. Additionally, finetuning these methods on historical training data is resource-intensive in terms of time and storage. In this paper, we focus on a novel and challenging problem: Continual Face Forgery Detection (CFFD), which aims to efficiently learn from new forgery attacks without forgetting previous ones. Specifically, we propose a Historical Distribution Preserving (HDP) framework that reserves and preserves the distributions of historical faces. To achieve this, we use universal adversarial perturbation (UAP) to simulate historical forgery distribution, and knowledge distillation to maintain the distribution variation of real faces across different models. We also construct a new benchmark for CFFD with three evaluation protocols. Our extensive experiments on the benchmarks show that our method outperforms the state-of-the-art competitors.
KETM:A Knowledge-Enhanced Text Matching method

Text matching is the task of matching two texts and determining the relationship between them, which has extensive applications in natural language processing tasks such as reading comprehension, and Question-Answering systems. The mainstream approach is to compute text representations or to interact with the text through attention mechanism, which is effective in text matching tasks. However, the performance of these models is insufficient for texts that require commonsense knowledge-based reasoning. To this end, in this paper, We introduce a new model for text matching called the Knowledge Enhanced Text Matching model (KETM), to enrich contextual representations with real-world common-sense knowledge from external knowledge sources to enhance our model understanding and reasoning. First, we use Wiktionary to retrieve the text word definitions as our external knowledge. Secondly, we feed text and knowledge to the text matching module to extract their feature vectors. The text matching module is used as an interaction module by integrating the encoder layer, the co-attention layer, and the aggregation layer. Specifically, the interaction process is iterated several times to obtain in-depth interaction information and extract the feature vectors of text and knowledge by multi-angle pooling. Then, we fuse text and knowledge using a gating mechanism to learn the ratio of text and knowledge fusion by a neural network that prevents noise generated by knowledge. After that, experimental validation on four datasets are carried out, and the experimental results show that our proposed model performs well on all four datasets, and the performance of our method is improved compared to the base model without adding external knowledge, which validates the effectiveness of our proposed method. The code is available at https://github.com/1094701018/KETM
Covid-19 Public Sentiment Analysis for Indian Tweets Classification

When any extraordinary event takes place in the world wide area, it is the social media that acts as the fastest carrier of the news along with the consequences dealt with that event. One can gather much information through social networks regarding the sentiments, behavior, and opinions of the people. In this paper, we focus mainly on sentiment analysis of twitter data of India which comprises of COVID-19 tweets. We show how Twitter data has been extracted and then run sentimental analysis queries on it. This is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and are either positive or negative or neutral in some cases.
The Effect of Using Strategy Word Square Toward Students’ Vocabulary Mastery in Descriptive Text at MTS’S Darussalam Parmeraan Academic Year 2021-2022

Vocabulary is one of the elements in language. In English there four skills, they are listening, speaking, reading, and writing. In mastering English, the basic thing that students have to learn and understand is vocabulary, because the mastery of vocabulary influence to another skills in English. The objective of this research was to know whether using word square game is able or not to improve students’ vocabulary mastery at the second year of MTs S Darussalam Parmeraan. This research was aimed to find out the improvement of students’ vocabulary mastery after giving treatment using word square game.&#x0D; This research used pre-experimental design, with one group pre-test and post- test. The pre-test was given before treatment to know their prior vocabulary, while the post-test was given after treatment to know their vocabulary after getting treatment. The population of this research was the second year of MTs S Darussalam Parmeraan. The researcher took VII.3 as the sample which was consisting of 24 students.&#x0D; The result of this research was found that there was a significant between the pre-test and post-test. The mean score of pre-test was 53.8, while the mean score of posttest was 72.33. The result finding that score of t-test value was 8.73 higher than the score of t-table was 1.714 for the level significance 0.05 degree of freedom 23. Based on the result above, the researcher concluded that using word square game was able to improve students’ vocabulary mastery at the second year of MTs S Darussalam Pameraan.
Effect of Using English Song on Students’ Vocabulary Mastery at 7th Grade of MTS YP Al Azhar Medan

Four skills in teaching English, that we always learn are reading, listening, speaking, and writing. There are several elements of language which support these four skills are structure, vocabulary, pronunciation and spelling. Vocabulary is one of the most significant things to be learned in the language, and it is the main center of learning a foreign language. Anggraini (2013:2) states that in teaching English, teaching vocabulary is an essential activity since vocabulary mastery applied to all language learning and concern all or any four language skills such learning, speaking, reading and writing. Learning vocabulary is significant because the student’s ability to learn how to write and read English, as well as to proposing their ideas and knowing the subject, vocabulary mastery is decided. If the students lack vocabulary, they will have trouble communicating and transmitting message. It is also difficult for students with limited vocabulary to understand the oral and written language because vocabulary is a central component of learning English.
On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks

Graph Neural Networks (GNNs) have succeeded in various computer science applications, yet deep GNNs underperform their shallow counterparts despite deep learning's success in other domains. Over-smoothing and over-squashing are key challenges when stacking graph convolutional layers, hindering deep representation learning and information propagation from distant nodes. Our work reveals that over-smoothing and over-squashing are intrinsically related to the spectral gap of the graph Laplacian, resulting in an inevitable trade-off between these two issues, as they cannot be alleviated simultaneously. To achieve a suitable compromise, we propose adding and removing edges as a viable approach. We introduce the Stochastic Jost and Liu Curvature Rewiring (SJLR) algorithm, which is computationally efficient and preserves fundamental properties compared to previous curvature-based methods. Unlike existing approaches, SJLR performs edge addition and removal during GNN training while maintaining the graph unchanged during testing. Comprehensive comparisons demonstrate SJLR's competitive performance in addressing over-smoothing and over-squashing.
Theory of entangled two-photon emission/absorption [E2P-EA] between molecules

This paper presents a comprehensive study of the theory of entangled two-photon emission/absorption (E2P-EA) between a many-level cascade donor and a many-level acceptor (which could be quantum dots or molecules) using second-order perturbation theory and where the donor–acceptor pair is in a homogeneous but dispersive medium. To understand the mechanism of E2P-EA, we analyze how dipole orientation, radiative lifetime, energy detuning between intermediate states, separation distance, and entanglement time impact the E2P-EA rate. Our study shows that there are quantum interference effects in the E2P-EA rate expression that lead to oscillations in the rate as a function of entanglement time. Furthermore, we find that the E2P-EA rate for a representative system consisting of two quantum dots can be comparable to one-photon emission/absorption (OP-EA) when donor and acceptor are within a few nm. However, the E2P-EA rate falls off much more quickly with separation distance than does OP-EA.
Transfer Learning based Breast Cancer Classification via Deep Convolutional Neural Network

Breast cancer is a leading cause of death among women, and the subjectivity of human visual perception and lack of automated detection methods can lead to misclassification of breast cancer images.In this study, a breast cancer classification model using a Convolutional Neural Network (CNN) deep learning algorithm was proposed.The model demonstrated high accuracy in classifying breast images as benign or malignant, with a classification accuracy of 97.1%.The model was also able to run on low computational resources.The study used a dataset of 2009 breast images labeled by two radiologists and included six scenarios based on different hyperparameters, augmentation values, pretrained models, and models built from scratch.While the performance of the proposed model was promising, further improvement may be achieved by using a larger breast image dataset and a machine with more powerful GPU hardware.