pwc-archive/papers-with-abstracts huggingface dataset:
Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset
Authors: HaoYi Wu, Wenyang Hui, Yezeng Chen, Weiqi Wu, Kewei Tu, Yi Zhou
Date: 2023-11-09 00:00:00
Tasks: Math, Natural Language Understanding

Mathematical understanding and reasoning are crucial tasks for assessing the capabilities of artificial intelligence (AI). However, existing benchmarks either require just a few steps of reasoning, or only contain a small amount of data in one specific topic, making it hard to analyse AI's behaviour with reference to different problems within a specific topic in detail. In this work, we propose Conic10K, a challenging math problem dataset on conic sections in Chinese senior high school education. Our dataset contains various problems with different reasoning depths, while only the knowledge from conic sections is required. Since the dataset only involves a narrow range of knowledge, it is easy to separately analyse the knowledge a model possesses and the reasoning ability it has. For each problem, we provide a high-quality formal representation, the reasoning steps, and the final solution. Experiments show that existing large language models, including GPT-4, exhibit weak performance on complex reasoning. We hope that our findings could inspire more advanced techniques for precise natural language understanding and reasoning. Our dataset and codes are available at https://github.com/whyNLP/Conic10K.
Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble Approaches
Authors: Juliana Resplande Santanna Gomes, Eduardo Augusto Santos Garcia, Adalberto Ferreira Barbosa Junior, Ruan Chaves Rodrigues, Diogo Fernandes Costa Silva, Dyonnatan Ferreira Maia, Nádia Félix Felipe da Silva, Arlindo Rodrigues Galvão Filho, Anderson da Silva Soares
Date: 2023-11-08 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Category Polarity, Aspect Extraction, Aspect-oriented  Opinion Extraction, Aspect Term Extraction and Sentiment Classification, Deep Learning, Sentence, Sentiment Analysis, Term Extraction

Aspect-based Sentiment Analysis (ABSA) is a task whose objective is to classify the individual sentiment polarity of all entities, called aspects, in a sentence. The task is composed of two subtasks: Aspect Term Extraction (ATE), identify all aspect terms in a sentence; and Sentiment Orientation Extraction (SOE), given a sentence and its aspect terms, the task is to determine the sentiment polarity of each aspect term (positive, negative or neutral). This article presents we present our participation in Aspect-Based Sentiment Analysis in Portuguese (ABSAPT) 2022 at IberLEF 2022. We submitted the best performing systems, achieving new state-of-the-art results on both subtasks.
DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text
Authors: Eduardo Garcia, Juliana Gomes, Adalberto Barbosa Júnior, Cardeque Borges, Nádia Da Silva
Date: 2023-11-08 00:00:00
Tasks: Depression Detection

In this paper, we delineate the strategy employed by our team, DeepLearningBrasil, which secured us the first place in the shared task DepSign-LT-EDI@RANLP-2023, achieving a 47.0% Macro F1-Score and a notable 2.4% advantage. The task was to classify social media texts into three distinct levels of depression - "not depressed," "moderately depressed," and "severely depressed." Leveraging the power of the RoBERTa and DeBERTa models, we further pre-trained them on a collected Reddit dataset, specifically curated from mental health-related Reddit's communities (Subreddits), leading to an enhanced understanding of nuanced mental health discourse. To address lengthy textual data, we used truncation techniques that retained the essence of the content by focusing on its beginnings and endings. Our model was robust against unbalanced data by incorporating sample weights into the loss. Cross-validation and ensemble techniques were then employed to combine our k-fold trained models, delivering an optimal solution. The accompanying code is made available for transparency and further development.
First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models
Authors: Naomi Saphra, Eve Fleisig, Kyunghyun Cho, Adam Lopez
Date: 2023-11-08 00:00:00
Tasks: Machine Translation

Many NLP researchers are experiencing an existential crisis triggered by the astonishing success of ChatGPT and other systems based on large language models (LLMs). After such a disruptive change to our understanding of the field, what is left to do? Taking a historical lens, we look for guidance from the first era of LLMs, which began in 2005 with large $n$-gram models for machine translation (MT). We identify durable lessons from the first era, and more importantly, we identify evergreen problems where NLP researchers can continue to make meaningful contributions in areas where LLMs are ascendant. We argue that disparities in scale are transient and researchers can work to reduce them; that data, rather than hardware, is still a bottleneck for many applications; that meaningful realistic evaluation is still an open problem; and that there is still room for speculative approaches.
On the steerability of large language models toward data-driven personas
Authors: Junyi Li, Ninareh Mehrabi, Charith Peris, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard Zemel, Rahul Gupta
Date: 2023-11-08 00:00:00
Tasks: Collaborative Filtering, Language Modelling, Large Language Model

Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented. Here, we present a novel approach to achieve controllable generation of specific viewpoints using LLMs, that can be leveraged to produce multiple perspectives and to reflect the diverse opinions. Moving beyond the traditional reliance on demographics like age, gender, or party affiliation, we introduce a data-driven notion of persona grounded in collaborative filtering, which is defined as either a single individual or a cohort of individuals manifesting similar views across specific inquiries. As individuals in the same demographic group may have different personas, our data-driven persona definition allows for a more nuanced understanding of different (latent) social groups present in the population. In addition to this, we also explore an efficient method to steer LLMs toward the personas that we define. We show that our data-driven personas significantly enhance model steerability, with improvements of between $57\%-77\%$ over our best performing baselines.
From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence
Authors: Mikhail Kiselev
Date: 2023-11-09 00:00:00
Tasks: Atari Games, Reinforcement Learning (RL)

In the reinforcement learning (RL) tasks, the ability to predict receiving reward in the near or more distant future means the ability to evaluate the current state as more or less close to the target state (labelled by the reward signal). In the present work, we utilize a spiking neural network (SNN) to predict time to the next target event (reward - in case of RL). In the context of SNNs, events are represented as spikes emitted by network neurons or input nodes. It is assumed that target events are indicated by spikes emitted by a special network input node. Using description of the current state encoded in the form of spikes from the other input nodes, the network should predict approximate time of the next target event. This research paper presents a novel approach to learning the corresponding predictive model by an SNN consisting of leaky integrate-and-fire (LIF) neurons. The proposed method leverages specially designed local synaptic plasticity rules and a novel columnar-layered SNN architecture. Similar to our previous works, this study places a strong emphasis on the hardware-friendliness of the proposed models, ensuring their efficient implementation on modern and future neuroprocessors. The approach proposed was tested on a simple reward prediction task in the context of one of the RL benchmark ATARI games, ping-pong. It was demonstrated that the SNN described in this paper gives superior prediction accuracy in comparison with precise machine learning techniques, such as decision tree algorithms and convolutional neural networks.
Rethinking Residual Connection in Training Large-Scale Spiking Neural Networks
Authors: Yudong Li, Yunlin Lei, Xu Yang
Date: 2023-11-09 00:00:00

Spiking Neural Network (SNN) is known as the most famous brain-inspired model, but the non-differentiable spiking mechanism makes it hard to train large-scale SNNs. To facilitate the training of large-scale SNNs, many training methods are borrowed from Artificial Neural Networks (ANNs), among which deep residual learning is the most commonly used. But the unique features of SNNs make prior intuition built upon ANNs not available for SNNs. Although there are a few studies that have made some pioneer attempts on the topology of Spiking ResNet, the advantages of different connections remain unclear. To tackle this issue, we analyze the merits and limitations of various residual connections and empirically demonstrate our ideas with extensive experiments. Then, based on our observations, we abstract the best-performing connections into densely additive (DA) connection, extend such a concept to other topologies, and propose four architectures for training large-scale SNNs, termed DANet, which brings up to 13.24% accuracy gain on ImageNet. Besides, in order to present a detailed methodology for designing the topology of large-scale SNNs, we further conduct in-depth discussions on their applicable scenarios in terms of their performance on various scales of datasets and demonstrate their advantages over prior architectures. At a low training expense, our best-performing ResNet-50/101/152 obtain 73.71%/76.13%/77.22% top-1 accuracy on ImageNet with 4 time steps. We believe that this work shall give more insights for future works to design the topology of their networks and promote the development of large-scale SNNs. The code will be publicly available.
Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients
Authors: Neil K. Chada, Benedict Leimkuhler, Daniel Paulin, Peter A. Whalley
Date: 2023-11-08 00:00:00
Tasks: regression

We present an unbiased method for Bayesian posterior means based on kinetic Langevin dynamics that combines advanced splitting methods with enhanced gradient approximations. Our approach avoids Metropolis correction by coupling Markov chains at different discretization levels in a multilevel Monte Carlo approach. Theoretical analysis demonstrates that our proposed estimator is unbiased, attains finite variance, and satisfies a central limit theorem. It can achieve accuracy $\epsilon>0$ for estimating expectations of Lipschitz functions in $d$ dimensions with $\mathcal{O}(d^{1/4}\epsilon^{-2})$ expected gradient evaluations, without assuming warm start. We exhibit similar bounds using both approximate and stochastic gradients, and our method's computational cost is shown to scale independently of the size of the dataset. The proposed method is tested using a multinomial regression problem on the MNIST dataset and a Poisson regression model for soccer scores. Experiments indicate that the number of gradient evaluations per effective sample is independent of dimension, even when using inexact gradients. For product distributions, we give dimension-independent variance bounds. Our results demonstrate that in large-scale applications, the unbiased algorithm we present can be 2-3 orders of magnitude more efficient than the ``gold-standard" randomized Hamiltonian Monte Carlo.
Consensus-based adaptive sampling and approximation for high-dimensional energy landscapes
Authors: Liyao Lyu, Huan Lei
Date: 2023-11-08 00:00:00
Tasks: Efficient Exploration

We present a consensus-based framework that unifies phase space exploration with posterior-residual-based adaptive sampling for surrogate construction in high-dimensional energy landscapes. Unlike standard approximation tasks where sampling points can be freely queried, physical systems with complex energy landscapes such as molecular dynamics (MD) do not have direct access to arbitrary sampling regions due to the physical constraints and energy barriers; the surrogate construction further relies on the dynamical exploration of phase space, posing a significant numerical challenge. We formulate the problem as a minimax optimization that jointly adapts both the surrogate approximation and residual-enhanced sampling. The construction of free energy surfaces (FESs) for high-dimensional collective variables (CVs) of MD systems is used as a motivating example to illustrate the essential idea. Specifically, the maximization step establishes a stochastic interacting particle system to impose adaptive sampling through both exploitation of a Laplace approximation of the max-residual region and exploration of uncharted phase space via temperature control. The minimization step updates the FES surrogate with the new sample set. Numerical results demonstrate the effectiveness of the present approach for biomolecular systems with up to 30 CVs. While we focus on the FES construction, the developed framework is general for efficient surrogate construction for complex systems with high-dimensional energy landscapes.
Sound field reconstruction using neural processes with dynamic kernels
Authors: Zining Liang, Wen Zhang, Thushara D. Abhayapala
Date: 2023-11-09 00:00:00
Tasks: Gaussian Processes

Accurately representing the sound field with the high spatial resolution is critical for immersive and interactive sound field reproduction technology. To minimize experimental effort, data-driven methods have been proposed to estimate sound fields from a small number of discrete observations. In particular, kernel-based methods using Gaussian Processes (GPs) with a covariance function to model spatial correlations have been used for sound field reconstruction. However, these methods have limitations due to the fixed kernels having limited expressiveness, requiring manual identification of optimal kernels for different sound fields. In this work, we propose a new approach that parameterizes GPs using a deep neural network based on Neural Processes (NPs) to reconstruct the magnitude of the sound field. This method has the advantage of dynamically learning kernels from simulated data using an attention mechanism, allowing for greater flexibility and adaptability to the acoustic properties of the sound field. Numerical experiments demonstrate that our proposed approach outperforms current methods in reconstructing accuracy, providing a promising alternative for sound field reconstruction.
Towards Fine-Grained Polyp Segmentation and Classification
Authors: Yael Tudela, Ana García-Rodríguez, Glória Fernández-Esparrach & Jorge Bernal
Date: 2023-10-09 00:00:00
Proceeding: Clinical Image-Based Procedures 2023 10
Tasks: Classification, Decoder, Lesion Detection

Colorectal cancer is one of the main causes of cancer death worldwide. Colonoscopy is the gold standard screening tool as it allows lesion detection and removal during the same procedure. During the last decades, several efforts have been made to develop CAD systems to assist clinicians in lesion detection and classification. Regarding the latter, and in order to be used in the exploration room as part of resect and discard or leave-in-situ strategies, these systems must identify correctly all different lesion types. This is a challenging task, as the data used to train these systems presents great inter-class similarity, high class imbalance, and low representation of clinically relevant histology classes such as serrated sessile adenomas.

In this paper, a new polyp segmentation and classification method, Swin-Expand, is introduced. Based on Swin-Transformer, it uses a simple and lightweight decoder. The performance of this method has been assessed on a novel dataset, comprising 1126 high-definition images representing the three main histological classes. Results show a clear improvement in both segmentation and classification performance, also achieving competitive results when tested in public datasets. These results confirm that both the method and the data are important to obtain more accurate polyp representations.
Operational modal analysis of under-determined system based on Bayesian CP decomposition
Authors: Sunao Tomita, Tomohiko Jimbo
Date: 2023-11-09 00:00:00
Tasks: Bayesian Inference, Structural Health Monitoring

Modal parameters such as natural frequencies, modal shapes, and the damping ratio are useful to understand structural dynamics of mechanical systems. Modal parameters need to be estimated under operational conditions for use in structural health monitoring. Therefore, operational modal analysis (OMA) without input signals has been proposed to easily extract modal parameters under operational conditions. Recently, OMA for under-determined systems with more active modes than measurement outputs has been investigated to reduce the number of sensors. This study proposes the OMA framework for under-determined systems based on Bayesian CP (CANDECOMP/PARAFAC) decomposition of second-order statistical data. The proposed method enables us to extract the modal parameters from under-determined systems without tuning the number of active modes, because the rank of the tensor data corresponding to the number of active modes is automatically determined via Bayesian inference. The effectiveness of this method is demonstrated using artificial vibration data of a mass-spring system under operational and under-determined conditions.
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
Authors: Xiao-Cong Zhong, Qisong Wang, Dan Liu, Zhihuang Chen, Jing-Xiao Liao, Jinwei Sun, Yudong Zhang, Feng-Lei Fan
Date: 2023-11-09 00:00:00
Tasks: Brain Computer Interface, Classification, Domain Adaptation, Domain Generalization, EEG, Motor Imagery

Motor imagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the classification is affected by the non-stationarity and individual variations of EEG signals. Simply pooling EEG data with different statistical distributions to train a classification model can severely degrade the generalization performance. To address this issue, the existing methods primarily focus on domain adaptation, which requires access to the target data during training. This is unrealistic in many EEG application scenarios. In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data. We optimize both the marginal and conditional distributions to ensure the stability of the joint distribution across source domains and extend it to a multi-source domain generalization framework to achieve domain-invariant feature representation, thereby alleviating calibration efforts. Systematic experiments on a simulative dataset and BCI competition datasets IV-2a and IV-2b demonstrate the superiority of our proposed EEG-DG over state-of-the-art methods. Specifically, EEG-DG achieves an average classification accuracy/kappa value of 81.79%/0.7572 and 87.12%/0.7424 on datasets IV-2a and IV-2b, respectively, which even outperforms some domain adaptation methods. Our code is available at https://github.com/XC-ZhongHIT/EEG-DG for free download and evaluation.
An Interdisciplinary Outlook on Large Language Models for Scientific Research
Authors: James Boyko, Joseph Cohen, Nathan Fox, Maria Han Veiga, Jennifer I-Hsiu Li, Jing Liu, Bernardo Modenesi, Andreas H. Rauch, Kenneth N. Reid, Soumi Tribedi, Anastasia Visheratina, Xin Xie
Date: 2023-11-03 00:00:00

In this paper, we describe the capabilities and constraints of Large Language Models (LLMs) within disparate academic disciplines, aiming to delineate their strengths and limitations with precision. We examine how LLMs augment scientific inquiry, offering concrete examples such as accelerating literature review by summarizing vast numbers of publications, enhancing code development through automated syntax correction, and refining the scientific writing process. Simultaneously, we articulate the challenges LLMs face, including their reliance on extensive and sometimes biased datasets, and the potential ethical dilemmas stemming from their use. Our critical discussion extends to the varying impacts of LLMs across fields, from the natural sciences, where they help model complex biological sequences, to the social sciences, where they can parse large-scale qualitative data. We conclude by offering a nuanced perspective on how LLMs can be both a boon and a boundary to scientific progress.
Ontology-Driven Processing of Transdisciplinary Domain Knowledge
Authors: Oleksandr Palagin, Mykola Petrenko, Sergii Kryvyi, Mykola Boyko, Kyrylo Malakhov
Date: 2023-11-01 00:00:00
Tasks: valid

The monograph discusses certain aspects of modern real-world problems facing humanity, which are much more challenging than scientific ones. Modern science is unable to solve them in a fundamental way. Vernadsky's noosphere thesis, in fact, appeals to the scientific worldview that needs to be built in a way that overcomes the interdisciplinary barriers and increases the effectiveness of interdisciplinary interaction and modern science overall. We are talking about the general transdisciplinary knowledge. In world practice, there is still no systematic methodology and a specific form of generally accepted valid scientific theory that would provide transdisciplinary knowledge. Non-linear interdisciplinary interaction is the standard of evolution of modern science. At the same time, a new transdisciplinary theory (domain of scientific research) is being de facto created and the process is repeated many times: from an individual or group of disciplines, through interdisciplinary interaction, in a direction that brings us closer to creating a holistic general scientific worldview.
In nomine patris... Elements for a semantics of medieval paternity
Authors: Nicolas Perreaux
Date: 2023-10-06 00:00:00

This article examines medieval concepts of paternity and father-son relationships through the digital analysis of medieval textual corpora. Although historians have access to enormous digital collections in 2023, they have rarely fully exploited these resources. The author proposes a historical semantic approach to this theme, using modeling tools and text mining in general, to analyze the evolution of terms related to paternity. The study proposes three conclusions: 1. a semantic break occurred in the semantic field of paternity at the turn of Antiquity and the Early Middle Ages. The meaning of pater and its derivatives changed radically over the course of the 4th-6th centuries, particularly as a result of the influence of the dogma of the Christian Trinity. Medieval fatherhood was multidimensional, encompassing both biological and spiritual aspects, in other words, complex relationships between multiple carnal and spiritual (i.e. divine) fathers. 2. The role of spiritual kinship is crucial to understanding medieval fatherhood, as the work of Anita Guerreau-Jalabert and J{\'e}r{\^o}me Baschet has already shown. Initially attributed to God, this ''ideal paternity'' (paternitas) gradually extended to members of the Church (popes, bishops, abbots), underlining at the same time the growing importance of spiritual kinship over biological kinship over the centuries studied. 3. To reveal these structures, invisible to the naked eye, an interdisciplinary approach is rigorously required. Complementary investigations into the lemmas mater, filia, frater and other family terms are required. The use of digital tools and historical semantic analysis opens up new perspectives for researchers in history, anthropology, linguistics and data mining, enabling them to explore the representation systems of ancient societies in depth and with nuance.