Reviewerly/exHarmony huggingface dataset:
Sequential Graph Convolutional Network for Active Learning

We propose a novel pool-based Active Learning framework constructed on a sequential Graph Convolution Network (GCN). Each image's feature from a pool of data represents a node in the graph and the edges encode their similarities. With a small number of randomly sampled images as seed labelled examples, we learn the parameters of the graph to distinguish labelled vs unlabelled nodes by minimising the binary cross-entropy loss. GCN performs message-passing operations between the nodes, and hence, induces similar representations of the strongly associated nodes. We exploit these characteristics of GCN to select the unlabelled examples which are sufficiently different from labelled ones. To this end, we utilise the graph node embeddings and their confidence scores and adapt sampling techniques such as CoreSet and uncertainty-based methods to query the nodes. We flip the label of newly queried nodes from unlabelled to labelled, re-train the learner to optimise the downstream task and the graph to minimise its modified objective. We continue this process within a fixed budget. We evaluate our method on 6 different benchmarks:4 real image classification, 1 depth-based hand pose estimation and 1 synthetic RGB image classification datasets. Our method outperforms several competitive baselines such as VAAL, Learning Loss, CoreSet and attains the new state-of-the-art performance on multiple applications The implementations can be found here: https://github.com/razvancaramalau/Sequential-GCN-for-Active-Learning
Learning to Utilize Correlated Auxiliary Noise: A Possible Quantum  Advantage

This paper has two messages. First, we demonstrate that neural networks that process noisy data can learn to exploit, when available, access to auxiliary noise that is correlated with the noise on the data. In effect, the network learns to use the correlated auxiliary noise as an approximate key to decipher its noisy input data. Second, we show that, for this task, the scaling behavior with increasing noise is such that future quantum machines could possess an advantage. In particular, decoherence generates correlated auxiliary noise in the environment. The new approach could, therefore, help enable future quantum machines by providing machine-learned quantum error correction.
A Note on Self-Dual Generalized Reed-Solomon Codes

A linear code is called an MDS self-dual code if it is both an MDS code and a self-dual code with respect to the Euclidean inner product. The parameters of such codes are completely determined by the code length. In this paper, we consider new constructions of MDS self-dual codes via generalized Reed-Solomon (GRS) codes and their extended codes. The critical idea of our constructions is to choose suitable evaluation points such that the corresponding (extended) GRS codes are self-dual. The evaluation set of our constructions is consists of a subgroup of finite fields and its cosets in a bigger subgroup. Four new families of MDS self-dual codes are obtained and they have better parameters than previous results in certain region. Moreover, by the Mobius action over finite fields, we give a systematic way to construct self-dual GRS codes with different evaluation points provided any known self-dual GRS codes. Specially, we prove that all the self-dual extended GRS codes over $\mathbb{F}_{q}$ with length $n< q+1$ can be constructed from GRS codes with the same parameters.
Cross-lingual Multispeaker Text-to-Speech under Limited-Data Scenario

Modeling voices for multiple speakers and multiple languages in one text-to-speech system has been a challenge for a long time. This paper presents an extension on Tacotron2 to achieve bilingual multispeaker speech synthesis when there are limited data for each language. We achieve cross-lingual synthesis, including code-switching cases, between English and Mandarin for monolingual speakers. The two languages share the same phonemic representations for input, while the language attribute and the speaker identity are independently controlled by language tokens and speaker embeddings, respectively. In addition, we investigate the model's performance on the cross-lingual synthesis, with and without a bilingual dataset during training. With the bilingual dataset, not only can the model generate high-fidelity speech for all speakers concerning the language they speak, but also can generate accented, yet fluent and intelligible speech for monolingual speakers regarding non-native language. For example, the Mandarin speaker can speak English fluently. Furthermore, the model trained with bilingual dataset is robust for code-switching text-to-speech, as shown in our results and provided samples.{https://caizexin.github.io/mlms-syn-samples/index.html}.
Normalized Convolutional Neural Network

In this paper, we propose Normalized Convolutional Neural Network(NCNN). NCNN is more fitted to a convolutional operator than other nomralizaiton methods. The normalized process is similar to a normalization methods, but NCNN is more adapative to sliced-inputs and corresponding the convolutional kernel. Therefor NCNN can be targeted to micro-batch training. Normalizaing of NC is conducted during convolutional process. In short, NC process is not usual normalization and can not be realized in deep learning framework optimizing standard convolution process. Hence we named this method 'Normalized Convolution'. As a result, NC process has universal property which means NC can be applied to any AI tasks involving convolution neural layer . Since NC don't need other normalization layer, NCNN looks like convolutional version of Self Normalizing Network.(SNN). Among micro-batch trainings, NCNN outperforms other batch-independent normalization methods. NCNN archives these superiority by standardizing rows of im2col matrix of inputs, which theoretically smooths the gradient of loss. The code need to manipulate standard convolution neural networks step by step. The code is available : https://github.com/kimdongsuk1/ NormalizedCNN.
Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm

Not all generate-and-test search algorithms are created equal. Bayesian Optimization (BO) invests a lot of computation time to generate the candidate solution that best balances the predicted value and the uncertainty given all previous data, taking increasingly more time as the number of evaluations performed grows. Evolutionary Algorithms (EA) on the other hand rely on search heuristics that typically do not depend on all previous data and can be done in constant time. Both the BO and EA community typically assess their performance as a function of the number of evaluations. However, this is unfair once we start to compare the efficiency of these classes of algorithms, as the overhead times to generate candidate solutions are significantly different. We suggest to measure the efficiency of generate-and-test search algorithms as the expected gain in the objective value per unit of computation time spent. We observe that the preference of an algorithm to be used can change after a number of function evaluations. We therefore propose a new algorithm, a combination of Bayesian optimization and an Evolutionary Algorithm, BEA for short, that starts with BO, then transfers knowledge to an EA, and subsequently runs the EA. We compare the BEA with BO and the EA. The results show that BEA outperforms both BO and the EA in terms of time efficiency, and ultimately leads to better performance on well-known benchmark objective functions with many local optima. Moreover, we test the three algorithms on nine test cases of robot learning problems and here again we find that BEA outperforms the other algorithms.
Progressively Pretrained Dense Corpus Index for Open-Domain Question  Answering

To extract answers from a large corpus, open-domain question answering (QA) systems usually rely on information retrieval (IR) techniques to narrow the search space. Standard inverted index methods such as TF-IDF are commonly used as thanks to their efficiency. However, their retrieval performance is limited as they simply use shallow and sparse lexical features. To break the IR bottleneck, recent studies show that stronger retrieval performance can be achieved by pretraining a effective paragraph encoder that index paragraphs into dense vectors. Once trained, the corpus can be pre-encoded into low-dimensional vectors and stored within an index structure where the retrieval can be efficiently implemented as maximum inner product search. Despite the promising results, pretraining such a dense index is expensive and often requires a very large batch size. In this work, we propose a simple and resource-efficient method to pretrain the paragraph encoder. First, instead of using heuristically created pseudo question-paragraph pairs for pretraining, we utilize an existing pretrained sequence-to-sequence model to build a strong question generator that creates high-quality pretraining data. Second, we propose a progressive pretraining algorithm to ensure the existence of effective negative samples in each batch. Across three datasets, our method outperforms an existing dense retrieval method that uses 7 times more computational resources for pretraining.
Expanding Sparse Guidance for Stereo Matching

The performance of image based stereo estimation suffers from lighting variations, repetitive patterns and homogeneous appearance. Moreover, to achieve good performance, stereo supervision requires sufficient densely-labeled data, which are hard to obtain. In this work, we leverage small amount of data with very sparse but accurate disparity cues from LiDAR to bridge the gap. We propose a novel sparsity expansion technique to expand the sparse cues concerning RGB images for local feature enhancement. The feature enhancement method can be easily applied to any stereo estimation algorithms with cost volume at the test stage. Extensive experiments on stereo datasets demonstrate the effectiveness and robustness across different backbones on domain adaption and self-supervision scenario. Our sparsity expansion method outperforms previous methods in terms of disparity by more than 2 pixel error on KITTI Stereo 2012 and 3 pixel error on KITTI Stereo 2015. Our approach significantly boosts the existing state-of-the-art stereo algorithms with extremely sparse cues.
On the Convergence of Quantized Parallel Restarted SGD for Central  Server Free Distributed Training

Communication is a crucial phase in the context of distributed training. Because parameter server (PS) frequently experiences network congestion, recent studies have found that training paradigms without a centralized server outperform the traditional server-based paradigms in terms of communication efficiency. However, with the increasing growth of model sizes, these server-free paradigms are also confronted with substantial communication overhead that seriously deteriorates the performance of distributed training. In this paper, we focus on communication efficiency of two serverless paradigms, i.e., Ring All-Reduce (RAR) and gossip, by proposing the Quantized Parallel Restarted Stochastic Gradient Descent (QPRSGD), an algorithm that allows multiple local SGD updates before a global synchronization, in synergy with the quantization to significantly reduce the communication overhead. We establish the bound of accumulative errors according to the synchronization mode and the network topology, which is essential to ensure the convergence property. Under both aggregation paradigms, the algorithm achieves the linear speedup property with respect to the number of local updates as well as the number of workers. Remarkably, the proposed algorithm achieves a convergence rate $O(1/\sqrt{NK^2M})$ under the gossip paradigm and outperforms all existing compression methods, where $N$ is the times of global synchronizations, and $K$ is the number of local updates, while $M$ is the number of nodes. An empirical study on various machine learning models demonstrates that the communication overhead is reduced by 90\%, and the convergence speed is boosted by up to 18.6 times, in a low bandwidth network, in comparison with Parallel SGD.
Unsupervised Facial Action Unit Intensity Estimation via Differentiable  Optimization

The automatic intensity estimation of facial action units (AUs) from a single image plays a vital role in facial analysis systems. One big challenge for data-driven AU intensity estimation is the lack of sufficient AU label data. Due to the fact that AU annotation requires strong domain expertise, it is expensive to construct an extensive database to learn deep models. The limited number of labeled AUs as well as identity differences and pose variations further increases the estimation difficulties. Considering all these difficulties, we propose an unsupervised framework GE-Net for facial AU intensity estimation from a single image, without requiring any annotated AU data. Our framework performs differentiable optimization, which iteratively updates the facial parameters (i.e., head pose, AU parameters and identity parameters) to match the input image. GE-Net consists of two modules: a generator and a feature extractor. The generator learns to "render" a face image from a set of facial parameters in a differentiable way, and the feature extractor extracts deep features for measuring the similarity of the rendered image and input real image. After the two modules are trained and fixed, the framework searches optimal facial parameters by minimizing the differences of the extracted features between the rendered image and the input image. Experimental results demonstrate that our method can achieve state-of-the-art results compared with existing methods.
Architecture Disentanglement for Deep Neural Networks

Understanding the inner workings of deep neural networks (DNNs) is essential to provide trustworthy artificial intelligence techniques for practical applications. Existing studies typically involve linking semantic concepts to units or layers of DNNs, but fail to explain the inference process. In this paper, we introduce neural architecture disentanglement (NAD) to fill the gap. Specifically, NAD learns to disentangle a pre-trained DNN into sub-architectures according to independent tasks, forming information flows that describe the inference processes. We investigate whether, where, and how the disentanglement occurs through experiments conducted with handcrafted and automatically-searched network architectures, on both object-based and scene-based datasets. Based on the experimental results, we present three new findings that provide fresh insights into the inner logic of DNNs. First, DNNs can be divided into sub-architectures for independent tasks. Second, deeper layers do not always correspond to higher semantics. Third, the connection type in a DNN affects how the information flows across layers, leading to different disentanglement behaviors. With NAD, we further explain why DNNs sometimes give wrong predictions. Experimental results show that misclassified images have a high probability of being assigned to task sub-architectures similar to the correct ones. Code will be available at: https://github.com/hujiecpp/NAD.
M$^5$L: Multi-Modal Multi-Margin Metric Learning for RGBT Tracking

Classifying the confusing samples in the course of RGBT tracking is a quite challenging problem, which hasn't got satisfied solution. Existing methods only focus on enlarging the boundary between positive and negative samples, however, the structured information of samples might be harmed, e.g., confusing positive samples are closer to the anchor than normal positive samples.To handle this problem, we propose a novel Multi-Modal Multi-Margin Metric Learning framework, named M$^5$L for RGBT tracking in this paper. In particular, we design a multi-margin structured loss to distinguish the confusing samples which play a most critical role in tracking performance boosting. To alleviate this problem, we additionally enlarge the boundaries between confusing positive samples and normal ones, between confusing negative samples and normal ones with predefined margins, by exploiting the structured information of all samples in each modality.Moreover, a cross-modality constraint is employed to reduce the difference between modalities and push positive samples closer to the anchor than negative ones from two modalities.In addition, to achieve quality-aware RGB and thermal feature fusion, we introduce the modality attentions and learn them using a feature fusion module in our network. Extensive experiments on large-scale datasets testify that our framework clearly improves the tracking performance and outperforms the state-of-the-art RGBT trackers.
Deep Blind Video Super-resolution

Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration. However, this assumption does not hold for video SR and usually leads to over-smoothed super-resolved images. In this paper, we propose a deep convolutional neural network (CNN) model to solve video SR by a blur kernel modeling approach. The proposed deep CNN model consists of motion blur estimation, motion estimation, and latent image restoration modules. The motion blur estimation module is used to provide reliable blur kernels. With the estimated blur kernel, we develop an image deconvolution method based on the image formation model of video SR to generate intermediate latent images so that some sharp image contents can be restored well. However, the generated intermediate latent images may contain artifacts. To generate high-quality images, we use the motion estimation module to explore the information from adjacent frames, where the motion estimation can constrain the deep CNN model for better image restoration. We show that the proposed algorithm is able to generate clearer images with finer structural details. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.
Why do State-of-the-art Super-Resolution Methods not work well for Bone Microstructure CT Imaging?

3D Computerized Tomography (CT) is a gold stan-dard technique to assess bone microstructure in the context of bone diseases such as osteoporosis. However, when acquired in-vivo, bone images may suffer from a low spatial resolution and the presence of noise due to the limited tolerable radiation exposure. One way to overcome this issue consists in applying Super-Resolution (SR) techniques that aim at recovering high resolution images. Significant progress has been recently made thanks to deep learning SR methods trained on natural image datasets. To measure the reconstruction quality, Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) are commonly used in the SR literature. In this paper, we give evidence of the limitation of these two criteria. Through extensive experiments performed from a dataset of mice tibias specifically collected and imaged for this study, we show that state of the art deep learning-based SR methods miss important details about the bone microstructure which is not reflected by the PSNR and SSIM values. This study opens the door to future promising lines of research including new SR methods regularized with respect to morphometric and tonological parameters of bone microstructures.
Administration and Management in an Educational Institution

Based on the system-activity approach, the article presents an analysis of the interaction of administration and motivational management, which aims to effectively manage the implementation of state educational standards in the practice of educational institutions. The authors believe that ignoring the importance of management and emphasizing the priority of administration leads to the fact that the most common problem in educational organizations is working with personnel. The authors point out that these problems are of a motivational and organizational nature because they relate to the rejection of changes in the work organization, irresponsibility, incompetence, lack of initiative of participants in activities, internal organizational conflicts, and organizational inefficiency. In the article, the authors discuss the solution to such pressing problems as management vision, human factor, subjective-functional management, subordination, regulations, orders, instructions, etc. The article also notes that one of the most important tasks of management is the transformation of participants in educational relations into a community of like-minded people who will be interested in introducing a new state educational standard and motivated to achieve both personal and socially significant results. Thus, the article explains that in an educational organization, priority is given to personality-oriented management aimed at the individual. Therefore, management tasks and areas of interest are not only personnel who “decide everything”, but also persons involved in the activity and management of an educational organization, their interrelationships, worldviews, positions, abilities, solving problems of their activity, their subject of interest, socially reflexive competence, their Motivation and focus on achieving results. Keywords: value-semantic attitudes, motivation, management.
Learning Natural Language Generation with Truncated Reinforcement Learning

Alice Martin, Guillaume Quispe, Charles Ollion, Sylvain Le Corff, Florian Strub, Olivier Pietquin. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
Disentangling Indirect Answers to Yes-No Questions in Real Conversations

In this paper, we explore the task of determining indirect answers to yes-no questions in real conversations.We work with transcripts of phone conversations in the Switchboard Dialog Act (SwDA) corpus and create SwDA-IndirectAnswers (SwDA-IA), a subset of SwDA consisting of all conversations containing a yes-no question with an indirect answer.We annotate the underlying direct answers to the yes-no questions (yes, probably yes, middle, probably no, or no).We show that doing so requires taking into account conversation context: the indirect answer alone is insufficient to determine the ground truth.Experimental results also show that taking into account context is beneficial.More importantly, our results demonstrate that existing corpora with synthetic indirect answers to yes-no questions are not beneficial when working with real conversations.Our best models outperform the majority baseline by a substantial margin, but the task remains a challenge (F1: 0.46).
When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes

Mycal Tucker, Tiwalayo Eisape, Peng Qian, Roger Levy, Julie Shah. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
ZHIXIAOBAO at SemEval-2022 Task 10: Apporoaching Structured Sentiment with Graph Parsing

This paper presents our submission to task 10, Structured Sentiment Analysis of the SemEval 2022 competition.The task aims to extract all elements of the fine-grained sentiment in a text.We cast structured sentiment analysis to the prediction of the sentiment graphs following (Barnes et al., 2021), where nodes are spans of sentiment holders, targets and expressions, and directed edges denote the relation types between them.Our approach closely follows that of semantic dependency parsing (Dozat and Manning, 2018).The difference is that we use pre-trained language models (e.g., BERT and RoBERTa) as text encoder to solve the problem of limited annotated data.Additionally, we make improvements on the computation of cross attention and present the suffix masking technique to make further performance improvement.Substantially, our model achieved the Top-1 average Sentiment Graph F1 score on seven datasets in five different languages in the monolingual subtask.