pwc-archive/papers-with-abstracts huggingface dataset:
From exemplar to copy: the scribal appropriation of a Hadewijch manuscript computationally explored
Authors: Wouter Haverals, Mike Kestemont
Date: 2022-10-25 00:00:00

This study is devoted to two of the oldest known manuscripts in which the oeuvre of the medieval mystical author Hadewijch has been preserved: Brussels, KBR, 2879-2880 (ms. A) and Brussels, KBR, 2877-2878 (ms. B). On the basis of codicological and contextual arguments, it is assumed that the scribe who produced B used A as an exemplar. While the similarities in both layout and content between the two manuscripts are striking, the present article seeks to identify the differences. After all, regardless of the intention to produce a copy that closely follows the exemplar, subtle linguistic variation is apparent. Divergences relate to spelling conventions, but also to the way in which words are abbreviated (and the extent to which abbreviations occur). The present study investigates the spelling profiles of the scribes who produced mss. A and B in a computational way. In the first part of this study, we will present both manuscripts in more detail, after which we will consider prior research carried out on scribal profiling. The current study both builds and expands on Kestemont (2015). Next, we outline the methodology used to analyse and measure the degree of scribal appropriation that took place when ms. B was copied off the exemplar ms. A. After this, we will discuss the results obtained, focusing on the scribal variation that can be found both at the level of individual words and n-grams. To this end, we use machine learning to identify the most distinctive features that separate manuscript A from B. Finally, we look at possible diachronic trends in the appropriation by B's scribe of his exemplar. We argue that scribal takeovers in the exemplar impacts the practice of the copying scribe, while transitions to a different content matter cause little to no effect.
Contrastive Search Is What You Need For Neural Text Generation
Authors: Yixuan Su, Nigel Collier
Date: 2022-10-25 00:00:00
Tasks: Contrastive Learning, Language Modeling, Language Modelling, Text Generation

Generating text with autoregressive language models (LMs) is of great importance to many natural language processing (NLP) applications. Previous solutions for this task often produce text that contains degenerative expressions or lacks semantic consistency. Recently, Su et al. introduced a new decoding method, contrastive search, based on the isotropic representation space of the language model and obtained new state of the art on various benchmarks. Additionally, Su et al. argued that the representations of autoregressive LMs (e.g. GPT-2) are intrinsically anisotropic which is also shared by previous studies. Therefore, to ensure the language model follows an isotropic distribution, Su et al. proposed a contrastive learning scheme, SimCTG, which calibrates the language model's representations through additional training. In this study, we first answer the question: "Are autoregressive LMs really anisotropic?". To this end, we extensively evaluate the isotropy of LMs across 16 major languages. Surprisingly, we find that the anisotropic problem only exists in the two specific English GPT-2-small/medium models. On the other hand, all other evaluated LMs are naturally isotropic which is in contrast to the conclusion drawn by previous studies. Based on our findings, we further assess the contrastive search decoding method using off-the-shelf LMs on four generation tasks across 16 languages. Our experimental results demonstrate that contrastive search significantly outperforms previous decoding methods without any additional training. More notably, on 12 out of the 16 evaluated languages, contrastive search performs comparably with human-level performances as judged by human evaluations. Our code and other related resources are publicly available at https://github.com/yxuansu/Contrastive_Search_Is_What_You_Need.
Towards Interpretable Summary Evaluation via Allocation of Contextual Embeddings to Reference Text Topics
Authors: Ben Schaper, Christopher Lohse, Marcell Streile, Andrea Giovannini, Richard Osuala
Date: 2022-10-25 00:00:00

Despite extensive recent advances in summary generation models, evaluation of auto-generated summaries still widely relies on single-score systems insufficient for transparent assessment and in-depth qualitative analysis. Towards bridging this gap, we propose the multifaceted interpretable summary evaluation method (MISEM), which is based on allocation of a summary's contextual token embeddings to semantic topics identified in the reference text. We further contribute an interpretability toolbox for automated summary evaluation and interactive visual analysis of summary scoring, topic identification, and token-topic allocation. MISEM achieves a promising .404 Pearson correlation with human judgment on the TAC'08 dataset.
CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization
Authors: Hossein Rajaby Faghihi, Bashar Alhafni, Ke Zhang, Shihao Ran, Joel Tetreault, Alejandro Jaimes
Date: 2022-10-25 00:00:00
Tasks: Abstractive Text Summarization, Benchmarking

Social media has increasingly played a key role in emergency response: first responders can use public posts to better react to ongoing crisis events and deploy the necessary resources where they are most needed. Timeline extraction and abstractive summarization are critical technical tasks to leverage large numbers of social media posts about events. Unfortunately, there are few datasets for benchmarking technical approaches for those tasks. This paper presents CrisisLTLSum, the largest dataset of local crisis event timelines available to date. CrisisLTLSum contains 1,000 crisis event timelines across four domains: wildfires, local fires, traffic, and storms. We built CrisisLTLSum using a semi-automated cluster-then-refine approach to collect data from the public Twitter stream. Our initial experiments indicate a significant gap between the performance of strong baselines compared to the human performance on both tasks. Our dataset, code, and models are publicly available.
Adapitch: Adaption Multi-Speaker Text-to-Speech Conditioned on Pitch Disentangling with Untranscribed Data
Authors: xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao
Date: 2022-10-25 00:00:00
Tasks: Decoder, Disentanglement, text-to-speech, Text to Speech

In this paper, we proposed Adapitch, a multi-speaker TTS method that makes adaptation of the supervised module with untranscribed data. We design two self supervised modules to train the text encoder and mel decoder separately with untranscribed data to enhance the representation of text and mel. To better handle the prosody information in a synthesized voice, a supervised TTS module is designed conditioned on content disentangling of pitch, text, and speaker. The training phase was separated into two parts, pretrained and fixed the text encoder and mel decoder with unsupervised mode, then the supervised mode on the disentanglement of TTS. Experiment results show that the Adaptich achieved much better quality than baseline methods.
Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approach
Authors: xulong Zhang, Jianzong Wang, Ning Cheng, Kexin Zhu, Jing Xiao
Date: 2022-10-25 00:00:00
Tasks: Representation Learning, Speaker Recognition, Speech Representation Learning

Recovering the masked speech frames is widely applied in speech representation learning. However, most of these models use random masking in the pre-training. In this work, we proposed two kinds of masking approaches: (1) speech-level masking, making the model to mask more speech segments than silence segments, (2) phoneme-level masking, forcing the model to mask the whole frames of the phoneme, instead of phoneme pieces. We pre-trained the model via these two approaches, and evaluated on two downstream tasks, phoneme classification and speaker recognition. The experiments demonstrated that the proposed masking approaches are beneficial to improve the performance of speech representation.
MetaSpeech: Speech Effects Switch Along with Environment for Metaverse
Authors: xulong Zhang, Jianzong Wang, Ning Cheng, Jing Xiao
Date: 2022-10-25 00:00:00
Tasks: Voice Conversion

Metaverse expands the physical world to a new dimension, and the physical environment and Metaverse environment can be directly connected and entered. Voice is an indispensable communication medium in the real world and Metaverse. Fusion of the voice with environment effects is important for user immersion in Metaverse. In this paper, we proposed using the voice conversion based method for the conversion of target environment effect speech. The proposed method was named MetaSpeech, which introduces an environment effect module containing an effect extractor to extract the environment information and an effect encoder to encode the environment effect condition, in which gradient reversal layer was used for adversarial training to keep the speech content and speaker information while disentangling the environmental effects. From the experiment results on the public dataset of LJSpeech with four environment effects, the proposed model could complete the specific environment effect conversion and outperforms the baseline methods from the voice conversion task.
Jet tagging algorithm of graph network with HaarPooling message passing
Authors: Fei Ma, Feiyi Liu, Wei Li
Date: 2022-10-25 00:00:00
Tasks: Jet Tagging

Recently methods of graph neural networks (GNNs) have been applied to solving the problems in high energy physics (HEP) and have shown its great potential for quark-gluon tagging with graph representation of jet events. In this paper, we introduce an approach of GNNs combined with a HaarPooling operation to analyze the events, called HaarPooling Message Passing neural network (HMPNet). In HMPNet, HaarPooling not only extracts the features of graph, but embeds additional information obtained by clustering of k-means of different particle features. We construct Haarpooling from five different features: absolute energy $\log E$, transverse momentum $\log p_T$, relative coordinates $(\Delta\eta,\Delta\phi)$, the mixed ones $(\log E, \log p_T)$ and $(\log E, \log p_T, \Delta\eta,\Delta\phi)$. The results show that an appropriate selection of information for HaarPooling enhances the accuracy of quark-gluon tagging, as adding extra information of $\log P_T$ to the HMPNet outperforms all the others, whereas adding relative coordinates information $(\Delta\eta,\Delta\phi)$ is not very effective. This implies that by adding effective particle features from HaarPooling can achieve much better results than solely pure message passing neutral network (MPNN) can do, which demonstrates significant improvement of feature extraction via the pooling process. Finally we compare the HMPNet study, ordering by $p_T$, with other studies and prove that the HMPNet is also a good choice of GNN algorithms for jet tagging.
TraffSign: Multilingual Traffic Signboard Text Detection and Recognition for Urdu and English
Authors: Muhammad Atif Butt, Adnan Ul-Hasan, and Faisal Shafait
Date: 2022-05-18 00:00:00
Proceeding: Document Analysis Systems 2022 5
Tasks: Scene Text Detection, Self-Driving Cars, Text Detection

Scene-text detection and recognition methods have demonstrated remarkable performance on standard benchmark datasets. These methods can be utilized in human-driven/self-driving cars to perform navigation assistance through traffic signboard text detection and recognition. Existing datasets include scripts in numerous languages like English, Chinese, French, Arabic, German, etc. However, traffic navigation signboards in Pakistan and many states of India are written in Urdu along with the English translation to guide human drivers. To this end, we present Deep Learning Laboratory’s Traffic Signboards Dataset (DLL-TraffSiD) to develop multi-lingual text detection and recognition methods for traffic signboards. In addition, we present a pipeline for multi-lingual text detection and recognition for an outdoor road environment. The results show that our presented system signified better applicability in text-detection and text recognition, and achieved 89% and 92.18% accuracy on the proposed dataset (The proposed dataset along with implementation is available at https://github.com/aatiibutt/TraffSign/).
CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentation
Authors: Muhammad Atif Butt, Faisal Riaz
Date: 2022-02-17 00:00:00
Proceeding: Signal Processing: Image Communication 2022 2
Tasks: 2D Object Detection, Autonomous Driving, Object, object-detection, Object Detection, Scene Segmentation, Scene Understanding, Self-Driving Cars, Semantic Segmentation, Transfer Learning, vehicle detection

Vision-based object detection and scene understanding are becoming key features of environment perception and autonomous driving. In the past couple of years, numerous large-scale datasets for visual object detection and semantic understanding have been released which has enormously benefited the environment perception in self-driving cars. However, these datasets like Kitti and Cityscapes only focus on well-organized and urban road traffic scenarios of European countries while ignoring the dense and unpattern traffic conditions of sub-continent countries like Pakistan, India, Bangladesh, and Sri Lanka. Consequently, the environment perception system developed on these datasets cannot efficiently assist self-driving cars in traffic scenarios of sub-continent countries. To this end, we present CARL-D, a large-scale dataset and benchmark suite for developing 2D object detection and instance/pixel-level segmentation methods for self-driving cars. CARL-D comprises large-scale stereo vision-based driving videos captured from more than 100 cities of Pakistan, including motorways, dense, and unpattern traffic scenarios of urban, rural, and hilly areas. As a benchmark selection, 15,000 suitable images are labeled for 2D object detection and recognition. Whereas the semantic segmentation benchmark contains 2500 images with pixel-level high-quality fine annotations and 5000 coarse-annotated images which could help in enabling the deep neural networks to leverage the weakly labeled data. Alongside the dataset, we also present transfer learning-based 2D vehicle detection and scene segmentation methods to evaluate the performance of existing state-of-the-art deep neural networks on our dataset. Lastly, an extensive experimental evaluation along with the comparative study has been carried out, demonstrating the upper edge of our dataset in terms of interclass diversity, scene variability, and annotations richness. The proposed benchmark suite is available at https://carl-dataset.github.io/index/.
Streaming Radiance Fields for 3D Video Synthesis
Authors: Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Ping Tan
Date: 2022-10-26 00:00:00
Tasks: Incremental Learning, Model Optimization, Novel View Synthesis

We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes. Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame. By exploiting the simple yet effective tuning strategy with narrow bands, the proposed method realizes a feasible framework for handling video sequences on-the-fly with high training efficiency. The storage overhead induced by using explicit grid representations can be significantly reduced through the use of model difference based compression. We also introduce an efficient strategy to further accelerate model optimization for each frame. Experiments on challenging video sequences demonstrate that our approach is capable of achieving a training speed of 15 seconds per-frame with competitive rendering quality, which attains $1000 \times$ speedup over the state-of-the-art implicit methods. Code is available at https://github.com/AlgoHunt/StreamRF.
BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples
Authors: Mohaddeseh Bastan, Mihai Surdeanu, Niranjan Balasubramanian
Date: 2022-10-26 00:00:00
Tasks: Decision Making, Natural Language Inference

Natural language inference (NLI) is critical for complex decision-making in biomedical domain. One key question, for example, is whether a given biomedical mechanism is supported by experimental evidence. This can be seen as an NLI problem but there are no directly usable datasets to address this. The main challenge is that manually creating informative negative examples for this task is difficult and expensive. We introduce a novel semi-supervised procedure that bootstraps an NLI dataset from existing biomedical dataset that pairs mechanisms with experimental evidence in abstracts. We generate a range of negative examples using nine strategies that manipulate the structure of the underlying mechanisms both with rules, e.g., flip the roles of the entities in the interaction, and, more importantly, as perturbations via logical constraints in a neuro-logical decoding system. We use this procedure to create a novel dataset for NLI in the biomedical domain, called BioNLI and benchmark two state-of-the-art biomedical classifiers. The best result we obtain is around mid 70s in F1, suggesting the difficulty of the task. Critically, the performance on the different classes of negative examples varies widely, from 97% F1 on the simple role change negative examples, to barely better than chance on the negative examples generated using neuro-logic decoding.
Moment Estimation for Nonparametric Mixture Models Through Implicit Tensor Decomposition
Authors: Yifan Zhang, Joe Kileel
Date: 2022-10-25 00:00:00
Tasks: Tensor Decomposition

We present an alternating least squares type numerical optimization scheme to estimate conditionally-independent mixture models in $\mathbb{R}^n$, without parameterizing the distributions. Following the method of moments, we tackle an incomplete tensor decomposition problem to learn the mixing weights and componentwise means. Then we compute the cumulative distribution functions, higher moments and other statistics of the component distributions through linear solves. Crucially for computations in high dimensions, the steep costs associated with high-order tensors are evaded, via the development of efficient tensor-free operations. Numerical experiments demonstrate the competitive performance of the algorithm, and its applicability to many models and applications. Furthermore we provide theoretical analyses, establishing identifiability from low-order moments of the mixture and guaranteeing local linear convergence of the ALS algorithm.
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models
Authors: Zijie J. Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, Duen Horng Chau
Date: 2022-10-26 00:00:00
Tasks: Diversity, Misinformation

With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introduce DiffusionDB, the first large-scale text-to-image prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. We analyze the syntactic and semantic characteristics of prompts. We pinpoint specific hyperparameter values and prompt styles that can lead to model errors and present evidence of potentially harmful model usage, such as the generation of misinformation. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. DiffusionDB is publicly available at: https://poloclub.github.io/diffusiondb.
Broken Neural Scaling Laws
Authors: Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger
Date: 2022-10-26 00:00:00
Tasks: Adversarial Robustness, Continual Learning, Contrastive Learning, Form, Math, Out-of-Distribution Detection, Out-of-Distribution Generalization, Quantization, Retrieval, Self-Supervised Learning, Transfer Learning

We present a smoothly broken power law functional form (referred to by us as a Broken Neural Scaling Law (BNSL)) that accurately models and extrapolates the scaling behaviors of deep neural networks (i.e. how the evaluation metric of interest varies as the amount of compute used for training, number of model parameters, training dataset size, model input size, number of training steps, or upstream performance varies) for various architectures and for each of various tasks within a large and diverse set of upstream and downstream tasks, in zero-shot, prompted, and fine-tuned settings. This set includes large-scale vision, language, audio, video, diffusion, generative modeling, multimodal learning, contrastive learning, AI alignment, robotics, out-of-distribution (OOD) generalization, continual learning, transfer learning, uncertainty estimation / calibration, out-of-distribution detection, adversarial robustness, distillation, sparsity, retrieval, quantization, pruning, molecules, computer programming/coding, math word problems, "emergent" "phase transitions / changes", arithmetic, unsupervised/self-supervised learning, and reinforcement learning (single agent and multi-agent). When compared to other functional forms for neural scaling behavior, this functional form yields extrapolations of scaling behavior that are considerably more accurate on this set. Moreover, this functional form accurately models and extrapolates scaling behavior that other functional forms are incapable of expressing such as the non-monotonic transitions present in the scaling behavior of phenomena such as double descent and the delayed, sharp inflection points present in the scaling behavior of tasks such as arithmetic. Lastly, we use this functional form to glean insights about the limit of the predictability of scaling behavior. Code is available at https://github.com/ethancaballero/broken_neural_scaling_laws
Quantum deep recurrent reinforcement learning
Authors: Samuel Yen-Chi Chen
Date: 2022-10-26 00:00:00
Tasks: Decision Making, Q-Learning, Quantum Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of quantum machine learning (QML). Reinforcement learning (RL) is one of the ML paradigms which can be used to solve complex sequential decision making problems. Classical RL has been shown to be capable to solve various challenging tasks. However, RL algorithms in the quantum world are still in their infancy. One of the challenges yet to solve is how to train quantum RL in the partially observable environments. In this paper, we approach this challenge through building QRL agents with quantum recurrent neural networks (QRNN). Specifically, we choose the quantum long short-term memory (QLSTM) to be the core of the QRL agent and train the whole model with deep $Q$-learning. We demonstrate the results via numerical simulations that the QLSTM-DRQN can solve standard benchmark such as Cart-Pole with more stable and higher average scores than classical DRQN with similar architecture and number of model parameters.