pwc-archive/papers-with-abstracts huggingface dataset:
Nonlinear social evolution and the emergence of collective action
Authors: Benjamin Allen, Abdur-Rahman Khwaja, James L. Donahue, Cassidy Lattanzio, Yulia A. Dementieva, Christine Sample
Date: 2023-02-28 00:00:00

Organisms from microbes to humans engage in a variety of social behaviors, which affect fitness in complex, often nonlinear ways. The question of how these behaviors evolve has consequences ranging from antibiotic resistance to human origins. However, evolution with nonlinear social interactions is challenging to model mathematically, especially in combination with spatial, group, and/or kin assortment. We derive a mathematical condition for natural selection with synergistic interactions among any number of individuals. This result applies to populations with arbitrary (but fixed) spatial or network structure, group subdivision, and/or mating patterns. In this condition, nonlinear fitness effects are ascribed to collectives, and weighted by a new measure of collective relatedness. For weak selection, this condition can be systematically evaluated by computing branch lengths of ancestral trees. We apply this condition to pairwise games between diploid relatives, and to dilemmas of collective help or harm among siblings and on spatial networks. Our work provides a rigorous basis for extending the notion of ``actor", in the study of social evolution, from individuals to collectives.
TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation
Authors: Debasmit Das, Shubhankar Borse, Hyojin Park, Kambiz Azarian, Hong Cai, Risheek Garrepalli, Fatih Porikli
Date: 2023-02-24 00:00:00
Tasks: Segmentation, Semantic Segmentation, Test-time Adaptation

Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-world, where samples arrive one-by-one in an online fashion. To tackle online settings, we propose TransAdapt, a framework that uses transformer and input transformations to improve segmentation performance. Specifically, we pre-train a transformer-based module on a segmentation network that transforms unsupervised segmentation output to a more reliable supervised output, without requiring test-time online training. To also facilitate test-time adaptation, we propose an unsupervised loss based on the transformed input that enforces the model to be invariant and equivariant to photometric and geometric perturbations, respectively. Overall, our framework produces higher quality segmentation masks with up to 17.6% and 2.8% mIOU improvement over no-adaptation and competitive baselines, respectively.
Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation
Authors: Ryan Benkert, Oluwaseun Joseph Aribido, Ghassan AlRegib
Date: 2023-02-24 00:00:00
Tasks: Seismic Interpretation, Style Transfer

In recent years, deep neural networks have significantly impacted the seismic interpretation process. Due to the simple implementation and low interpretation costs, deep neural networks are an attractive component for the common interpretation pipeline. However, neural networks are frequently met with distrust due to their property of producing semantically incorrect outputs when exposed to sections the model was not trained on. We address this issue by explaining model behaviour and improving generalization properties through example forgetting: First, we introduce a method that effectively relates semantically malfunctioned predictions to their respectful positions within the neural network representation manifold. More concrete, our method tracks how models "forget" seismic reflections during training and establishes a connection to the decision boundary proximity of the target class. Second, we use our analysis technique to identify frequently forgotten regions within the training volume and augment the training set with state-of-the-art style transfer techniques from computer vision. We show that our method improves the segmentation performance on underrepresented classes while significantly reducing the forgotten regions in the F3 volume in the Netherlands.
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
Authors: Wei Fan, Pengyang Wang, Dongkun Wang, Dongjie Wang, Yuanchun Zhou, Yanjie Fu
Date: 2023-02-22 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models. Existing works towards distribution shift in time series are mostly limited in the quantification of distribution and, more importantly, overlook the potential shift between lookback and horizon windows. To address above challenges, we systematically summarize the distribution shift in TSF into two categories. Regarding lookback windows as input-space and horizon windows as output-space, there exist (i) intra-space shift, that the distribution within the input-space keeps shifted over time, and (ii) inter-space shift, that the distribution is shifted between input-space and output-space. Then we introduce, Dish-TS, a general neural paradigm for alleviating distribution shift in TSF. Specifically, for better distribution estimation, we propose the coefficient net (CONET), which can be any neural architectures, to map input sequences into learnable distribution coefficients. To relieve intra-space and inter-space shift, we organize Dish-TS as a Dual-CONET framework to separately learn the distribution of input- and output-space, which naturally captures the distribution difference of two spaces. In addition, we introduce a more effective training strategy for intractable CONET learning. Finally, we conduct extensive experiments on several datasets coupled with different state-of-the-art forecasting models. Experimental results show Dish-TS consistently boosts them with a more than 20% average improvement. Code is available.
A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems
Authors: Farimehr Zohari, S. M. Mahdi Shahabi, Mehrdad Ardebilipour
Date: 2023-01-24 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

This letter investigates an intelligent reflecting surfaces (IRS)-enhanced network from spectral efficiency enhancement point of view for downlink multi-user (MU) multi-input-single-output systems (MISO). In contrast to previous works which mainly focused on alternative optimization methods, we investigate the non-convex joint optimization problem of the active transmit beamforming matrix at the base station together with the passive phase shift matrix at the IRS by utilizing two deep reinforcement learning frameworks, i. e., deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3). Simulation results reveal that the neural networks in the latter scheme perform generally more satisfactorily in various situations.
PS4: a Next-Generation Dataset for Protein Single Sequence Secondary Structure Prediction
Authors: Omar Peracha
Date: 2023-03-01 00:00:00
Proceeding: biorXiv Preprint 2023 3
Tasks: Prediction, Protein Folding, Protein Secondary Structure Prediction

Protein secondary structure prediction is a subproblem of protein folding. A lightweight algorithm capable of accurately predicting secondary structure from only the protein residue sequence could therefore provide a useful input for tertiary structure prediction, alleviating the reliance on MSA typically seen in today's best-performing models. This in turn could see the development of protein folding algorithms which perform better on orphan proteins, and which are much more accessible for both research and industry adoption due to reducing the necessary computational resources to run. Unfortunately, existing datasets for secondary structure prediction are small, creating a bottleneck in the rate of progress of automatic secondary structure prediction. Furthermore, protein chains in these datasets are often not identified, hampering the ability of researchers to use external domain knowledge when developing new algorithms. 

We present PS4, a dataset of 18,731 non-redundant protein chains and their respective Q8 secondary structure labels. Each chain is identified by its PDB code, and the dataset is also non-redundant against other secondary structure datasets commonly seen in the literature. We perform ablation studies by training secondary structure prediction algorithms on the PS4 training set, and obtain state-of-the-art Q8 and Q3 accuracy on the CB513 test set in zero shots, without further fine-tuning. Furthermore, we provide a software toolkit for the community to run our evaluation algorithms, train models from scratch and add new samples to the dataset. 

All code and data required to reproduce our results and make new inferences is available at https://github.com/omarperacha/ps4-dataset
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
Authors: Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han
Date: 2023-03-01 00:00:00
Tasks: Federated Learning

Privacy and security concerns in real-world applications have led to the development of adversarially robust federated models. However, the straightforward combination between adversarial training and federated learning in one framework can lead to the undesired robustness deterioration. We discover that the attribution behind this phenomenon is that the generated adversarial data could exacerbate the data heterogeneity among local clients, making the wrapped federated learning perform poorly. To deal with this problem, we propose a novel framework called Slack Federated Adversarial Training (SFAT), assigning the client-wise slack during aggregation to combat the intensified heterogeneity. Theoretically, we analyze the convergence of the proposed method to properly relax the objective when combining federated learning and adversarial training. Experimentally, we verify the rationality and effectiveness of SFAT on various benchmarked and real-world datasets with different adversarial training and federated optimization methods. The code is publicly available at https://github.com/ZFancy/SFAT.
Indescribable Multi-modal Spatial Evaluator
Authors: Lingke Kong, X. Sharon Qi, Qijin Shen, Jiacheng Wang, Jingyi Zhang, Yanle Hu, Qichao Zhou
Date: 2023-03-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Image Registration, Image-to-Image Translation

Multi-modal image registration spatially aligns two images with different distributions. One of its major challenges is that images acquired from different imaging machines have different imaging distributions, making it difficult to focus only on the spatial aspect of the images and ignore differences in distributions. In this study, we developed a self-supervised approach, Indescribable Multi-model Spatial Evaluator (IMSE), to address multi-modal image registration. IMSE creates an accurate multi-modal spatial evaluator to measure spatial differences between two images, and then optimizes registration by minimizing the error predicted of the evaluator. To optimize IMSE performance, we also proposed a new style enhancement method called Shuffle Remap which randomizes the image distribution into multiple segments, and then randomly disorders and remaps these segments, so that the distribution of the original image is changed. Shuffle Remap can help IMSE to predict the difference in spatial location from unseen target distributions. Our results show that IMSE outperformed the existing methods for registration using T1-T2 and CT-MRI datasets. IMSE also can be easily integrated into the traditional registration process, and can provide a convenient way to evaluate and visualize registration results. IMSE also has the potential to be used as a new paradigm for image-to-image translation. Our code is available at https://github.com/Kid-Liet/IMSE.
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
Authors: Yuechen Yu, Yulin Li, Chengquan Zhang, Xiaoqiang Zhang, Zengyuan Guo, Xiameng Qin, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang
Date: 2023-03-01 00:00:00
Tasks: Document Image Classification, image-classification, Image Classification, Language Modeling, Language Modelling, Masked Language Modeling, Optical Character Recognition (OCR), Semantic entity labeling

In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction. It consists of two self-supervised pre-training tasks: masked image modeling and masked language modeling, based on text region-level image masking. The proposed method randomly masks some image regions according to the bounding box coordinates of text words. The objectives of our pre-training tasks are reconstructing the pixels of masked image regions and the corresponding masked tokens simultaneously. Hence the pre-trained encoder can capture more textual semantics in comparison to the masked image modeling that usually predicts the masked image patches. Compared to the masked multi-modal modeling methods for document image understanding that rely on both the image and text modalities, StrucTexTv2 models image-only input and potentially deals with more application scenarios free from OCR pre-processing. Extensive experiments on mainstream benchmarks of document image understanding demonstrate the effectiveness of StrucTexTv2. It achieves competitive or even new state-of-the-art performance in various downstream tasks such as image classification, layout analysis, table structure recognition, document OCR, and information extraction under the end-to-end scenario.
Two Views of Constrained Differential Privacy: Belief Revision and Update
Authors: Likang Liu, Keke Sun, Chunlai Zhou, Yuan Feng
Date: 2023-03-01 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, we provide two views of constrained differential private (DP) mechanisms. The first one is as belief revision. A constrained DP mechanism is obtained by standard probabilistic conditioning, and hence can be naturally implemented by Monte Carlo algorithms. The other is as belief update. A constrained DP is defined according to l2-distance minimization postprocessing or projection and hence can be naturally implemented by optimization algorithms. The main advantage of these two perspectives is that we can make full use of the machinery of belief revision and update to show basic properties for constrained differential privacy especially some important new composition properties. Within the framework established in this paper, constrained DP algorithms in the literature can be classified either as belief revision or belief update. At the end of the paper, we demonstrate their differences especially in utility in a couple of scenarios.
BiSVP: Building Footprint Extraction via Bidirectional Serialized Vertex Prediction
Authors: Mingming Zhang, Ye Du, Zhenghui Hu, Qingjie Liu, Yunhong Wang
Date: 2023-03-01 00:00:00
Tasks: Instance Segmentation, Prediction, Semantic Segmentation

Extracting building footprints from remote sensing images has been attracting extensive attention recently. Dominant approaches address this challenging problem by generating vectorized building masks with cumbersome refinement stages, which limits the application of such methods. In this paper, we introduce a new refinement-free and end-to-end building footprint extraction method, which is conceptually intuitive, simple, and effective. Our method, termed as BiSVP, represents a building instance with ordered vertices and formulates the building footprint extraction as predicting the serialized vertices directly in a bidirectional fashion. Moreover, we propose a cross-scale feature fusion (CSFF) module to facilitate high resolution and rich semantic feature learning, which is essential for the dense building vertex prediction task. Without bells and whistles, our BiSVP outperforms state-of-the-art methods by considerable margins on three building instance segmentation benchmarks, clearly demonstrating its superiority. The code and datasets will be made public available.
SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks
Authors: Kai-Wei Chang, Yu-Kai Wang, Hua Shen, Iu-thing Kang, Wei-Cheng Tseng, Shang-Wen Li, Hung-Yi Lee
Date: 2023-03-01 00:00:00
Tasks: Classification, Language Modeling, Language Modelling, Spoken Language Understanding

Prompt tuning is a technology that tunes a small set of parameters to steer a pre-trained language model (LM) to directly generate the output for downstream tasks. Recently, prompt tuning has demonstrated its storage and computation efficiency in both natural language processing (NLP) and speech processing fields. These advantages have also revealed prompt tuning as a candidate approach to serving pre-trained LM for multiple tasks in a unified manner. For speech processing, SpeechPrompt shows its high parameter efficiency and competitive performance on a few speech classification tasks. However, whether SpeechPrompt is capable of serving a large number of tasks is unanswered. In this work, we propose SpeechPrompt v2, a prompt tuning framework capable of performing a wide variety of speech classification tasks, covering multiple languages and prosody-related tasks. The experiment result shows that SpeechPrompt v2 achieves performance on par with prior works with less than 0.15M trainable parameters in a unified framework.
A Systematic Analysis of Vocabulary and BPE Settings for Optimal Fine-tuning of NMT: A Case Study of In-domain Translation
Authors: J. Pourmostafa Roshan Sharami, D. Shterionov, P. Spronck
Date: 2023-03-01 00:00:00
Tasks: Machine Translation, NMT

The effectiveness of Neural Machine Translation (NMT) models largely depends on the vocabulary used at training; small vocabularies can lead to out-of-vocabulary problems -- large ones, to memory issues. Subword (SW) tokenization has been successfully employed to mitigate these issues. The choice of vocabulary and SW tokenization has a significant impact on both training and fine-tuning an NMT model. Fine-tuning is a common practice in optimizing an MT model with respect to new data. However, new data potentially introduces new words (or tokens), which, if not taken into consideration, may lead to suboptimal performance. In addition, the distribution of tokens in the new data can differ from the distribution of the original data. As such, the original SW tokenization model could be less suitable for the new data. Through a systematic empirical evaluation, in this work we compare different strategies for SW tokenization and vocabulary generation with the ultimate goal to uncover an optimal setting for fine-tuning a domain-specific model. Furthermore, we developed several (in-domain) models, the best of which achieves 6 BLEU points improvement over the baseline.
Bootstrapping Parallel Anchors for Relative Representations
Authors: Irene Cannistraci, Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Emanuele Rodolà
Date: 2023-03-01 00:00:00
Tasks: Semantic correspondence

The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot model stitching across a wide range of applications. Nevertheless, relative representations rely on a certain amount of parallel anchors to be given as input, which can be impractical to obtain in certain scenarios. To overcome this limitation, we propose an optimization-based method to discover new parallel anchors from a limited known set (seed). Our approach can be used to find semantic correspondence between different domains, align their relative spaces, and achieve competitive results in several tasks.
Forward-PECVaR Algorithm: Exact Evaluation for CVaR SSPs
Authors: Willy Arthur Silva Reis, Denis Benevolo Pais, Valdinei Freire, Karina Valdivia Delgado
Date: 2023-03-01 00:00:00

The Stochastic Shortest Path (SSP) problem models probabilistic sequential-decision problems where an agent must pursue a goal while minimizing a cost function. Because of the probabilistic dynamics, it is desired to have a cost function that considers risk. Conditional Value at Risk (CVaR) is a criterion that allows modeling an arbitrary level of risk by considering the expectation of a fraction $\alpha$ of worse trajectories. Although an optimal policy is non-Markovian, solutions of CVaR-SSP can be found approximately with Value Iteration based algorithms such as CVaR Value Iteration with Linear Interpolation (CVaRVIQ) and CVaR Value Iteration via Quantile Representation (CVaRVILI). These type of solutions depends on the algorithm's parameters such as the number of atoms and $\alpha_0$ (the minimum $\alpha$). To compare the policies returned by these algorithms, we need a way to exactly evaluate stationary policies of CVaR-SSPs. Although there is an algorithm that evaluates these policies, this only works on problems with uniform costs. In this paper, we propose a new algorithm, Forward-PECVaR (ForPECVaR), that evaluates exactly stationary policies of CVaR-SSPs with non-uniform costs. We evaluate empirically CVaR Value Iteration algorithms that found solutions approximately regarding their quality compared with the exact solution, and the influence of the algorithm parameters in the quality and scalability of the solutions. Experiments in two domains show that it is important to use an $\alpha_0$ smaller than the $\alpha$ target and an adequate number of atoms to obtain a good approximation.
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
Authors: Philine Bommer, Marlene Kretschmer, Anna Hedström, Dilyara Bareeva, Marina M. -C. Höhne
Date: 2023-03-01 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Explainable artificial intelligence (XAI) methods shed light on the predictions of machine learning algorithms. Several different approaches exist and have already been applied in climate science. However, usually missing ground truth explanations complicate their evaluation and comparison, subsequently impeding the choice of the XAI method. Therefore, in this work, we introduce XAI evaluation in the climate context and discuss different desired explanation properties, namely robustness, faithfulness, randomization, complexity, and localization. To this end, we chose previous work as a case study where the decade of annual-mean temperature maps is predicted. After training both a multi-layer perceptron (MLP) and a convolutional neural network (CNN), multiple XAI methods are applied and their skill scores in reference to a random uniform explanation are calculated for each property. Independent of the network, we find that XAI methods Integrated Gradients, layer-wise relevance propagation, and input times gradients exhibit considerable robustness, faithfulness, and complexity while sacrificing randomization performance. Sensitivity methods -- gradient, SmoothGrad, NoiseGrad, and FusionGrad, match the robustness skill but sacrifice faithfulness and complexity for randomization skill. We find architecture-dependent performance differences regarding robustness, complexity and localization skills of different XAI methods, highlighting the necessity for research task-specific evaluation. Overall, our work offers an overview of different evaluation properties in the climate science context and shows how to compare and benchmark different explanation methods, assessing their suitability based on strengths and weaknesses, for the specific research problem at hand. By that, we aim to support climate researchers in the selection of a suitable XAI method.