pwc-archive/papers-with-abstracts huggingface dataset:
The stability and instability of the language control network: a longitudinal resting-state functional magnetic resonance imaging study
Authors: Zilong Li, Cong Liu, Xin Pan, Guosheng Ding, Ruiming Wang
Date: 2024-01-23 00:00:00

The language control network is vital among language-related networks responsible for solving the problem of multiple language switching. Researchers have expressed concerns about the instability of the language control network when exposed to external influences (e.g., Long-term second language learning). However, some studies have suggested that the language control network is stable. Therefore, whether the language control network is stable or not remains unclear. In the present study, we directly evaluated the stability and instability of the language control network using resting-state functional magnetic resonance imaging (rs-fMRI). We employed cohorts of Chinese first-year college students majoring in English who underwent second language (L2) acquisition courses at a university and those who did not. Two resting-state fMRI scans were acquired approximately 1 year apart. We found that the language control network was both moderately stable and unstable. We further investigated the morphological coexistence patterns of stability and instability within the language control network. First, we extracted connections representing stability and plasticity from the entire network. We then evaluated whether the coexistence patterns were modular (stability and instability involve different brain regions) or non-modular (stability and plasticity involve the same brain regions but have unique connectivity patterns). We found that both stability and instability coexisted in a non-modular pattern. Compared with the non-English major group, the English major group has a more non-modular coexistence pattern.. These findings provide preliminary evidence of the coexistence of stability and instability in the language control network.
Experiencing an elongated limb in virtual reality modifies the tactile distance perception of the corresponding real limb
Authors: François Le Jeune, Marco D'Alonzo, Valeria Piombino, Alessia Noccaro, Domenico Formica, Giovanni Di Pino
Date: 2024-01-23 00:00:00

In measurement, a reference frame is needed to compare the measured object to something already known. This raises the neuroscientific question of which reference frame is used by humans when exploring the environment. Previous studies suggested that, in touch, the body employed as measuring tool also serves as reference frame. Indeed, an artificial modification of the perceived dimensions of the body changes the tactile perception of external object dimensions. However, it is unknown if such a change in tactile perception would occur when the body schema is modified through the illusion of owning an limb altered in size. Therefore, employing a virtual hand illusion paradigm with an elongated forearm of different lengths, we systematically tested the subjective perception of distance between two points (tactile distance perception task, TDP task) on the corresponding real forearm following the illusion. Thus, TDP task is used as a proxy to gauge changes in the body schema. Embodiment of the virtual arm was found significantly greater after the synchronous visuo-tactile stimulation condition compared to the asynchronous one, and the forearm elongation significantly increased the TDP. However, we did not find any link between the visuo-tactile induced ownership over the elongated arm and TDP variation, suggesting that vision plays the main role in the modification of the body schema. Additionally, significant effect of elongation found on TDP but not on proprioception suggests that these are affected differently by body schema modifications. These findings confirm the body schema malleability and its role as reference frame in touch.
New approximate stochastic dominance approaches for Enhanced Indexation models
Authors: Francesco Cesarone, Justo Puerto
Date: 2024-01-23 00:00:00

In this paper, we discuss portfolio selection strategies for Enhanced Indexation (EI), which are based on stochastic dominance relations. The goal is to select portfolios that stochastically dominate a given benchmark but that, at the same time, must generate some excess return with respect to a benchmark index. To achieve this goal, we propose a new methodology that selects portfolios using the ordered weighted average (OWA) operator, which generalizes previous approaches based on minimax selection rules and still leads to solving linear programming models. We also introduce a new type of approximate stochastic dominance rule and show that it implies the almost Second-order Stochastic Dominance (SSD) criterion proposed by Lizyayev and Ruszczynski (2012). We prove that our EI model based on OWA selects portfolios that dominate a given benchmark through this new form of stochastic dominance criterion. We test the performance of the obtained portfolios in an extensive empirical analysis based on real-world datasets. The computational results show that our proposed approach outperforms several SSD-based strategies widely used in the literature, as well as the global minimum variance portfolio.
Reference-dependent asset pricing with a stochastic consumption-dividend ratio
Authors: Luca De Gennaro Aquino, Xuedong He, Moris Simon Strub, Yuting Yang
Date: 2024-01-23 00:00:00

We study a discrete-time consumption-based capital asset pricing model under expectations-based reference-dependent preferences. More precisely, we consider an endowment economy populated by a representative agent who derives utility from current consumption and from gains and losses in consumption with respect to a forward-looking, stochastic reference point. First, we consider a general model in which the agent's preferences include both contemporaneous gain-loss utility, that is, utility from the difference between current consumption and previously held expectations about current consumption, and prospective gain-loss utility, that is, utility from the difference between intertemporal beliefs about future consumption. A semi-closed form solution for equilibrium asset prices is derived for this case. We then specialize to a model in which the agent derives contemporaneous gain-loss utility only, obtaining equilibrium asset prices in closed form. Extensive numerical experiments show that, with plausible values of risk aversion and loss aversion, our models can generate equity premia that match empirical estimates. Interestingly, the models turn out to be consistent with some well-known empirical facts, namely procyclical variation in the price-dividend ratio and countercyclical variation in the conditional expected equity premium and in the conditional volatility of the equity premium. Furthermore, we find that prospective gain-loss utility is necessary for the model to predict reasonable values of the price-dividend ratio.
Memorization in Self-Supervised Learning Improves Downstream Generalization
Authors: Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
Date: 2024-01-19 00:00:00
Tasks: Memorization, Self-Supervised Learning

Self-supervised learning (SSL) has recently received significant attention due to its ability to train high-performance encoders purely on unlabeled data-often scraped from the internet. This data can still be sensitive and empirical evidence suggests that SSL encoders memorize private information of their training data and can disclose them at inference time. Since existing theoretical definitions of memorization from supervised learning rely on labels, they do not transfer to SSL. To address this gap, we propose SSLMem, a framework for defining memorization within SSL. Our definition compares the difference in alignment of representations for data points and their augmented views returned by both encoders that were trained on these data points and encoders that were not. Through comprehensive empirical analysis on diverse encoder architectures and datasets we highlight that even though SSL relies on large datasets and strong augmentations-both known in supervised learning as regularization techniques that reduce overfitting-still significant fractions of training data points experience high memorization. Through our empirical results, we show that this memorization is essential for encoders to achieve higher generalization performance on different downstream tasks.
Multimodal Data Curation via Object Detection and Filter Ensembles
Authors: Tzu-Heng Huang, Changho Shin, Sui Jiet Tay, Dyah Adila, Frederic Sala
Date: 2024-01-05 00:00:00
Tasks: Object, object-detection, Object Detection, Position, Zero-Shot Object Detection

We propose an approach for curating multimodal data that we used for our entry in the 2023 DataComp competition filtering track. Our technique combines object detection and weak supervision-based ensembling. In the first of two steps in our approach, we employ an out-of-the-box zero-shot object detection model to extract granular information and produce a variety of filter designs. In the second step, we employ weak supervision to ensemble filtering rules. This approach results in a 4% performance improvement when compared to the best-performing baseline, producing the top-ranking position in the small scale track at the time of writing. Furthermore, in the medium scale track, we achieve a noteworthy 4.2% improvement over the baseline by simply ensembling existing baselines with weak supervision.
The Global Impact of AI-Artificial Intelligence: Recent Advances and Future Directions, A Review
Authors: Chandregowda Pachegowda
Date: 2023-12-22 00:00:00

Artificial intelligence (AI) is an emerging technology that has the potential to transform many aspects of society, including the economy, healthcare, and transportation. This article synthesizes recent research literature on the global impact of AI, exploring its potential benefits and risks. The article highlights the implications of AI, including its impact on economic, ethical, social, security & privacy, and job displacement aspects. It discusses the ethical concerns surrounding AI development, including issues of bias, security, and privacy violations. To ensure the responsible development and deployment of AI, collaboration between government, industry, and academia is essential. The article concludes by emphasizing the importance of public engagement and education to promote awareness and understanding of AI's impact on society at large.
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
Authors: Zhuoqun Huang, Neil G. Marchant, Keane Lucas, Lujo Bauer, Olga Ohrimenko, Benjamin I. P. Rubinstein
Date: 2023-01-31 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Binary Classification, Malware Detection

Randomized smoothing is a leading approach for constructing classifiers that are certifiably robust against adversarial examples. Existing work on randomized smoothing has focused on classifiers with continuous inputs, such as images, where $\ell_p$-norm bounded adversaries are commonly studied. However, there has been limited work for classifiers with discrete or variable-size inputs, such as for source code, which require different threat models and smoothing mechanisms. In this work, we adapt randomized smoothing for discrete sequence classifiers to provide certified robustness against edit distance-bounded adversaries. Our proposed smoothing mechanism randomized deletion (RS-Del) applies random deletion edits, which are (perhaps surprisingly) sufficient to confer robustness against adversarial deletion, insertion and substitution edits. Our proof of certification deviates from the established Neyman-Pearson approach, which is intractable in our setting, and is instead organized around longest common subsequences. We present a case study on malware detection--a binary classification problem on byte sequences where classifier evasion is a well-established threat model. When applied to the popular MalConv malware detection model, our smoothing mechanism RS-Del achieves a certified accuracy of 91% at an edit distance radius of 128 bytes.
Multi-label Emotion Analysis in Conversation via Multimodal Knowledge Distillation
Authors: Sidharth Anand, Naresh Kumar Devulapally, Sreyasee Das Bhattacharjee, Junsong Yuan
Date: 2023-10-27 00:00:00
Proceeding: ACM International Conference on Multimedia 2023 10
Tasks: Emotion Recognition, Knowledge Distillation, Multimodal Sentiment Analysis

Evaluating speaker emotion in conversations is crucial for various applications requiring human-computer interaction. However, co-occurrences of multiple emotional states (e.g. 'anger' and 'frustration' may occur together or one may influence the occurrence of the other) and their dynamic evolution may vary dramatically due to the speaker's internal (e.g., influence of their personalized socio-cultural-educational and demographic backgrounds) and external contexts. Thus far, the previous focus has been on evaluating only the dominant emotion observed in a speaker at a given time, which is susceptible to producing misleading classification decisions for difficult multi-labels during testing. In this work, we present Self-supervised Multi- Label Peer Collaborative Distillation (SeMuL-PCD) Learning via an efficient Multimodal Transformer Network, in which complementary feedback from multiple mode-specific peer networks (e.g.transcript, audio, visual) are distilled into a single mode-ensembled fusion network for estimating multiple emotions simultaneously. The proposed Multimodal Distillation Loss calibrates the fusion network by minimizing the Kullback-Leibler divergence with the peer networks. Additionally, each peer network is conditioned using a self-supervised contrastive objective to improve the generalization across diverse socio-demographic speaker backgrounds. By enabling peer collaborative learning that allows each network to independently learn their mode-specific discriminative patterns,SeMUL-PCD is effective across different conversation environments. In particular, the model not only outperforms the current state-of-the-art models on several large-scale public datasets (e.g., MOSEI, EmoReact and ElderReact), but with around 17% improved weighted F1-score in the cross-dataset experimental settings. The model also demonstrates an impressive generalization ability across age and demography-diverse populations.
LPNL: Scalable Link Prediction with Large Language Models
Authors: Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Xueqi Cheng
Date: 2024-01-24 00:00:00
Tasks: Graph Learning, Language Modelling, Large Language Model, Link Prediction, Prediction, Self-Supervised Learning

Exploring the application of large language models (LLMs) to graph learning is a emerging endeavor. However, the vast amount of information inherent in large graphs poses significant challenges to this process. This work focuses on the link prediction task and introduces $\textbf{LPNL}$ (Link Prediction via Natural Language), a framework based on large language models designed for scalable link prediction on large-scale heterogeneous graphs. We design novel prompts for link prediction that articulate graph details in natural language. We propose a two-stage sampling pipeline to extract crucial information from the graphs, and a divide-and-conquer strategy to control the input tokens within predefined limits, addressing the challenge of overwhelming information. We fine-tune a T5 model based on our self-supervised learning designed for link prediction. Extensive experimental results demonstrate that LPNL outperforms multiple advanced baselines in link prediction tasks on large-scale graphs.
DittoGym: Learning to Control Soft Shape-Shifting Robots
Authors: Suning Huang, Boyuan Chen, Huazhe Xu, Vincent Sitzmann
Date: 2024-01-24 00:00:00
Tasks: Reinforcement Learning (RL)

Robot co-design, where the morphology of a robot is optimized jointly with a learned policy to solve a specific task, is an emerging area of research. It holds particular promise for soft robots, which are amenable to novel manufacturing techniques that can realize learned morphologies and actuators. Inspired by nature and recent novel robot designs, we propose to go a step further and explore the novel reconfigurable robots, defined as robots that can change their morphology within their lifetime. We formalize control of reconfigurable soft robots as a high-dimensional reinforcement learning (RL) problem. We unify morphology change, locomotion, and environment interaction in the same action space, and introduce an appropriate, coarse-to-fine curriculum that enables us to discover policies that accomplish fine-grained control of the resulting robots. We also introduce DittoGym, a comprehensive RL benchmark for reconfigurable soft robots that require fine-grained morphology changes to accomplish the tasks. Finally, we evaluate our proposed coarse-to-fine algorithm on DittoGym and demonstrate robots that learn to change their morphology several times within a sequence, uniquely enabled by our RL algorithm. More results are available at https://suninghuang19.github.io/dittogym_page/.
Tyche: Stochastic In-Context Learning for Medical Image Segmentation
Authors: Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Ortiz, Beth Cimini, John Guttag, Adrian V. Dalca
Date: 2024-01-24 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Segmentation, In-Context Learning, Medical Image Segmentation, Segmentation, Semantic Segmentation

Existing learning-based solutions to medical image segmentation have two important shortcomings. First, for most new segmentation task, a new model has to be trained or fine-tuned. This requires extensive resources and machine learning expertise, and is therefore often infeasible for medical researchers and clinicians. Second, most existing segmentation methods produce a single deterministic segmentation mask for a given image. In practice however, there is often considerable uncertainty about what constitutes the correct segmentation, and different expert annotators will often segment the same image differently. We tackle both of these problems with Tyche, a model that uses a context set to generate stochastic predictions for previously unseen tasks without the need to retrain. Tyche differs from other in-context segmentation methods in two important ways. (1) We introduce a novel convolution block architecture that enables interactions among predictions. (2) We introduce in-context test-time augmentation, a new mechanism to provide prediction stochasticity. When combined with appropriate model design and loss functions, Tyche can predict a set of plausible diverse segmentation candidates for new or unseen medical images and segmentation tasks without the need to retrain.
The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations
Authors: Matthias Lehmann
Date: 2024-01-24 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Learning Theory

In recent years, various powerful policy gradient algorithms have been proposed in deep reinforcement learning. While all these algorithms build on the Policy Gradient Theorem, the specific design choices differ significantly across algorithms. We provide a holistic overview of on-policy policy gradient algorithms to facilitate the understanding of both their theoretical foundations and their practical implementations. In this overview, we include a detailed proof of the continuous version of the Policy Gradient Theorem, convergence results and a comprehensive discussion of practical algorithms. We compare the most prominent algorithms on continuous control environments and provide insights on the benefits of regularization. All code is available at https://github.com/Matt00n/PolicyGradientsJax.
Inadequacy of common stochastic neural networks for reliable clinical decision support
Authors: Adrian Lindenmeyer, Malte Blattmann, Stefan Franke, Thomas Neumuth, Daniel Schneider
Date: 2024-01-24 00:00:00
Tasks: Mortality Prediction

Widespread adoption of AI for medical decision making is still hindered due to ethical and safety-related concerns. For AI-based decision support systems in healthcare settings it is paramount to be reliable and trustworthy. Common deep learning approaches, however, have the tendency towards overconfidence under data shift. Such inappropriate extrapolation beyond evidence-based scenarios may have dire consequences. This highlights the importance of reliable estimation of local uncertainty and its communication to the end user. While stochastic neural networks have been heralded as a potential solution to these issues, this study investigates their actual reliability in clinical applications. We centered our analysis on the exemplary use case of mortality prediction for ICU hospitalizations using EHR from MIMIC3 study. For predictions on the EHR time series, Encoder-Only Transformer models were employed. Stochasticity of model functions was achieved by incorporating common methods such as Bayesian neural network layers and model ensembles. Our models achieve state of the art performance in terms of discrimination performance (AUC ROC: 0.868+-0.011, AUC PR: 0.554+-0.034) and calibration on the mortality prediction benchmark. However, epistemic uncertainty is critically underestimated by the selected stochastic deep learning methods. A heuristic proof for the responsible collapse of the posterior distribution is provided. Our findings reveal the inadequacy of commonly used stochastic deep learning approaches to reliably recognize OoD samples. In both methods, unsubstantiated model confidence is not prevented due to strongly biased functional posteriors, rendering them inappropriate for reliable clinical decision support. This highlights the need for approaches with more strictly enforced or inherent distance-awareness to known data points, e.g., using kernel-based techniques.
Graph-Instructed Neural Networks for Sparse Grid-Based Discontinuity Detectors
Authors: Francesco Della Santa, Sandra Pieraccini
Date: 2024-01-24 00:00:00

In this paper, we present a novel approach for detecting the discontinuity interfaces of a discontinuous function. This approach leverages Graph-Instructed Neural Networks (GINNs) and sparse grids to address discontinuity detection also in domains of dimension larger than 3. GINNs, trained to identify troubled points on sparse grids, exploit graph structures built on the grids to achieve efficient and accurate discontinuity detection performances. We also introduce a recursive algorithm for general sparse grid-based detectors, characterized by convergence properties and easy applicability. Numerical experiments on functions with dimensions n = 2 and n = 4 demonstrate the efficiency and robust generalization properties of GINNs in detecting discontinuity interfaces. Notably, the trained GINNs offer portability and versatility, allowing integration into various algorithms and sharing among users.