pwc-archive/papers-with-abstracts huggingface dataset:
Scaling Laws And Statistical Properties of The Transaction Flows And Holding Times of Bitcoin
Authors: Didier Sornette, Yu Zhang
Date: 2024-01-09 00:00:00

We study the temporal evolution of the holding-time distribution of bitcoins and find that the average distribution of holding-time is a heavy-tailed power law extending from one day to over at least $200$ weeks with an exponent approximately equal to $0.9$, indicating very long memory effects. We also report significant sample-to-sample variations of the distribution of holding times, which can be best characterized as multiscaling, with power-law exponents varying between $0.3$ and $2.5$ depending on bitcoin price regimes. We document significant differences between the distributions of book-to-market and of realized returns, showing that traders obtain far from optimal performance. We also report strong direct qualitative and quantitative evidence of the disposition effect in the Bitcoin Blockchain data. Defining age-dependent transaction flows as the fraction of bitcoins that are traded at a given time and that were born (last traded) at some specific earlier time, we document that the time-averaged transaction flow fraction has a power law dependence as a function of age, with an exponent close to $-1.5$, a value compatible with priority queuing theory. We document the existence of multifractality on the measure defined as the normalized number of bitcoins exchanged at a given time.
Working with Trouble and Failures in Conversation between Humans and Robots (WTF 2023) & Is CUI Design Ready Yet?
Authors: Frank Förster, Marta Romeo, Patrick Holthaus, Maria Jose Galvez Trigo, Joel E. Fischer, Birthe Nesset, Christian Dondrup, Christine Murad, Cosmin Munteanu, Benjamin R. Cowan, Leigh Clark, Martin Porcheron, Heloisa Candello, Raina Langevin
Date: 2023-09-04 00:00:00

Workshop proceedings of two co-located workshops "Working with Troubles and Failures in Conversation with Humans and Robots" (WTF 2023) and "Is CUI Design Ready Yet?", both of which were part of the ACM conference on conversational user interfaces 2023. WTF 23 aimed at bringing together researchers from human-robot interaction, dialogue systems, human-computer interaction, and conversation analysis. Despite all progress, robotic speech interfaces continue to be brittle in a number of ways and the experience of failure of such interfaces is commonplace amongst roboticists. However, the technical literature is positively skewed toward their good performance. The workshop aims to provide a platform for discussing communicative troubles and failures in human-robot interactions and related failures in non-robotic speech interfaces. Aims include a scrupulous investigation into communicative failures, to begin working on a taxonomy of such failures, and enable a preliminary discussion on possible mitigating strategies. Workshop website: https://sites.google.com/view/wtf2023/overview Is CUI Design Ready Yet? As CUIs become more prevalent in both academic research and the commercial market, it becomes more essential to design usable and adoptable CUIs. While research has been growing on the methods for designing CUIs for commercial use, there has been little discussion on the overall community practice of developing design resources to aid in practical CUI design. The aim of this workshop, therefore, is to bring the CUI community together to discuss the current practices for developing tools and resources for practical CUI design, the adoption (or non-adoption) of these tools and resources, and how these resources are utilized in the training and education of new CUI designers entering the field. Workshop website: https://speech-interaction.org/cui2023_design_workshop/index.html
CCNETS: A Novel Brain-Inspired Approach for Enhanced Pattern Recognition in Imbalanced Datasets
Authors: Hanbeot Park, Yunjeong Cho, Hoon-Hee Kim
Date: 2024-01-07 00:00:00

This study introduces CCNETS (Causal Learning with Causal Cooperative Nets), a novel generative model-based classifier designed to tackle the challenge of generating data for imbalanced datasets in pattern recognition. CCNETS is uniquely crafted to emulate brain-like information processing and comprises three main components: Explainer, Producer, and Reasoner. Each component is designed to mimic specific brain functions, which aids in generating high-quality datasets and enhancing classification performance. The model is particularly focused on addressing the common and significant challenge of handling imbalanced datasets in machine learning. CCNETS's effectiveness is demonstrated through its application to a "fraud dataset," where normal transactions significantly outnumber fraudulent ones (99.83% vs. 0.17%). Traditional methods often struggle with such imbalances, leading to skewed performance metrics. However, CCNETS exhibits superior classification ability, as evidenced by its performance metrics. Specifically, it achieved an F1-score of 0.7992, outperforming traditional models like Autoencoders and Multi-layer Perceptrons (MLP) in the same context. This performance indicates CCNETS's proficiency in more accurately distinguishing between normal and fraudulent patterns. The innovative structure of CCNETS enhances the coherence between generative and classification models, helping to overcome the limitations of pattern recognition that rely solely on generative models. This study emphasizes CCNETS's potential in diverse applications, especially where quality data generation and pattern recognition are key. It proves effective in machine learning, particularly for imbalanced datasets. CCNETS overcomes current challenges in these datasets and advances machine learning with brain-inspired approaches.
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
Authors: Hussein Mozannar, Jimin J Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag
Date: 2023-11-02 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Language Modeling, Language Modelling, Large Language Model, object-detection, Object Detection, Question Answering

People are relying on AI agents to assist them with various tasks. The human must know when to rely on the agent, collaborate with the agent, or ignore its suggestions. In this work, we propose to learn rules, grounded in data regions and described in natural language, that illustrate how the human should collaborate with the AI. Our novel region discovery algorithm finds local regions in the data as neighborhoods in an embedding space where prior human behavior should be corrected. Each region is then described using a large language model in an iterative and contrastive procedure. We then teach these rules to the human via an onboarding stage. Through user studies on object detection and question-answering tasks, we show that our method can lead to more accurate human-AI teams. We also evaluate our region discovery and description algorithms separately.
Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses
Authors: Elena Sizikova, Niloufar Saharkhiz, Diksha Sharma, Miguel Lago, Berkman Sahiner, Jana G. Delfino, Aldo Badano
Date: 2023-10-27 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Anatomy

To generate evidence regarding the safety and efficacy of artificial intelligence (AI) enabled medical devices, AI models need to be evaluated on a diverse population of patient cases, some of which may not be readily available. We propose an evaluation approach for testing medical imaging AI models that relies on in silico imaging pipelines in which stochastic digital models of human anatomy (in object space) with and without pathology are imaged using a digital replica imaging acquisition system to generate realistic synthetic image datasets. Here, we release M-SYNTH, a dataset of cohorts with four breast fibroglandular density distributions imaged at different exposure levels using Monte Carlo x-ray simulations with the publicly available Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) toolkit. We utilize the synthetic dataset to analyze AI model performance and find that model performance decreases with increasing breast density and increases with higher mass density, as expected. As exposure levels decrease, AI model performance drops with the highest performance achieved at exposure levels lower than the nominal recommended dose for the breast type.
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
Authors: Jiangyan Ma, Yifei Wang, Yisen Wang
Date: 2023-10-28 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph Classification, Graph Embedding, Graph Regression

Spectral embedding is a powerful graph embedding technique that has received a lot of attention recently due to its effectiveness on Graph Transformers. However, from a theoretical perspective, the universal expressive power of spectral embedding comes at the price of losing two important invariance properties of graphs, sign and basis invariance, which also limits its effectiveness on graph data. To remedy this issue, many previous methods developed costly approaches to learn new invariants and suffer from high computation complexity. In this work, we explore a minimal approach that resolves the ambiguity issues by directly finding canonical directions for the eigenvectors, named Laplacian Canonization (LC). As a pure pre-processing method, LC is light-weighted and can be applied to any existing GNNs. We provide a thorough investigation, from theory to algorithm, on this approach, and discover an efficient algorithm named Maximal Axis Projection (MAP) that works for both sign and basis invariance and successfully canonizes more than 90% of all eigenvectors. Experiments on real-world benchmark datasets like ZINC, MOLTOX21, and MOLPCBA show that MAP consistently outperforms existing methods while bringing minimal computation overhead. Code is available at https://github.com/PKU-ML/LaplacianCanonization.
Describing Clothing by Semantic Attributes
Authors: Huizhong Chen, Andrew Gallagher, Bernd Girod
Date: 2012-10-07 00:00:00
Proceeding: ECCV 2012 10
Tasks: Attribute, Clothing Attribute Recognition

Describing clothing appearance with semantic attributes is an appealing technique for many important applications. In this paper, we propose a
fully automated system that is capable of generating a list of nameable attributes
for clothes on human body in unconstrained images. We extract low-level features
in a pose-adaptive manner, and combine complementary features for learning attribute classifiers. Mutual dependencies between the attributes are then explored
by a Conditional Random Field to further improve the predictions from independent classifiers. We validate the performance of our system on a challenging
clothing attribute dataset, and introduce a novel application of dressing style
analysis that utilizes the semantic attributes produced by our system.
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
Authors: Rasmus Rothe, Radu Timofte, Luc van Gool
Date: 2016-08-10 00:00:00
Proceeding: IJCV 2016 8
Tasks: Age Estimation, Face Alignment, image-classification, Image Classification, Robust Face Alignment

In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.
Dating historical color images
Authors: Frank Palermo, James Hays, Alexei A. Efros
Date: 2012-10-07 00:00:00
Proceeding: ECCV 2012 10
Tasks: Age Estimation

We introduce the task of automatically estimating the age
of historical color photographs. We suggest features which attempt to
capture temporally discriminative information based on the evolution of
color imaging processes over time and evaluate the performance of both
these novel features and existing features commonly utilized in other
problem domains on a novel historical image data set. For the challenging
classification task of sorting historical color images into the decade during which they were photographed, we demonstrate significantly greater
accuracy than that shown by untrained humans on the same data set.
Additionally, we apply the concept of data-driven camera response function estimation to historical color imagery, demonstrating its relevance
to both the age estimation task and the popular application of imitating
the appearance of vintage color photography.
AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation
Authors: Xin Liu, Shaoxin Li, Meina Kan, Jie Zhang, Shuzhe Wu, Wenxian Liu, Hu Han, Shiguang Shan, Xilin Chen
Date: 2015-11-13 00:00:00
Proceeding: ICCV Workshop 2015 11
Tasks: Age Estimation, Transfer Learning

Apparent age estimation from face image has attracted
more and more attentions as it is favorable in some realworld applications. In this work, we propose an end-toend learning approach for robust apparent age estimation,
named by us AgeNet. Specifically, we address the apparent age estimation problem by fusing two kinds of models,
i.e., real-value based regression models and Gaussian label distribution based classification models. For both kind
of models, large-scale deep convolutional neural network is
adopted to learn informative age representations. Another
key feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme. Technically, the AgeNet is first pre-trained on a large-scale webcollected face dataset with identity label, and then it is finetuned on a large-scale real age dataset with noisy age label.
Finally, it is fine-tuned on a small training set with apparent age label. The experimental results on the ChaLearn
2015 Apparent Age Competition demonstrate that our AgeNet achieves the state-of-the-art performance in apparent
age estimation.
Neighborhood Repulsed Metric Learning for Kinship Verification
Authors: Jiwen Lu, Xiuzhuang Zhou, Yap-Pen Tan, Yuanyuan Shang, Jie zhou
Date: 2013-07-17 00:00:00
Proceeding: TPAMI 2013 7
Tasks: Kinship Verification, Metric Learning, Relation

Kinship verification from facial images is an interesting and challenging problem in computer vision, and there are very limited attempts on tackle this problem in the literature. In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for kinship verification. Motivated by the fact that interclass samples (without a kinship relation) with higher similarity usually lie in a neighborhood and are more easily misclassified than those with lower similarity, we aim to learn a distance metric under which the intraclass samples (with a kinship relation) are pulled as close as possible and interclass samples lying in a neighborhood are repulsed and pushed away as far as possible, simultaneously, such that more discriminative information can be exploited for verification. To make better use of multiple feature descriptors to extract complementary information, we further propose a multiview NRML (MNRML) method to seek a common distance metric to perform multiple feature fusion to improve the kinship verification performance. Experimental results are presented to demonstrate the efficacy of our proposed methods. Finally, we also test human ability in kinship verification from facial images and our experimental results show that our methods are comparable to that of human observers.
Full-frequency dynamic convolution: a physical frequency-dependent convolution for sound event detection
Authors: Haobo Yue, Zhicheng Zhang, Da Mu, Yonghao Dang, Jianqin Yin, Jin Tang
Date: 2024-01-10 00:00:00
Tasks: Event Detection, Sound Event Detection

Recently, 2D convolution has been found unqualified in sound event detection (SED). It enforces translation equivariance on sound events along frequency axis, which is not a shift-invariant dimension. To address this issue, dynamic convolution is used to model the frequency dependency of sound events. In this paper, we proposed the first full-dynamic method named full-frequency dynamic convolution (FFDConv). FFDConv generates frequency kernels for every frequency band, which is designed directly in the structure for frequency-dependent modeling. It physically furnished 2D convolution with the capability of frequency-dependent modeling. FFDConv outperforms not only the baseline by 6.6% in DESED real validation dataset in terms of PSDS1, but outperforms the other full-dynamic methods. In addition, by visualizing features of sound events, we observed that FFDConv could effectively extract coherent features in specific frequency bands, consistent with the vocal continuity of sound events. This proves that FFDConv has great frequency-dependent perception ability.
Segment anything model (SAM) for brain extraction in fMRI studies
Authors: Dwith Chenna, Suyash Bhogawar
Date: 2024-01-09 00:00:00
Tasks: Brain Segmentation, Segmentation

Brain extraction and removal of skull artifacts from magnetic resonance images (MRI) is an important preprocessing step in neuroimaging analysis. There are many tools developed to handle human fMRI images, which could involve manual steps for verifying results from brain segmentation that makes it time consuming and inefficient. In this study, we will use the segment anything model (SAM), a freely available neural network released by Meta[4], which has shown promising results in many generic segmentation applications. We will analyze the efficiency of SAM for neuroimaging brain segmentation by removing skull artifacts. The results of the experiments showed promising results that explore using automated segmentation algorithms for neuroimaging without the need to train on custom medical imaging dataset.
Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?
Authors: Mudit Verma, Siddhant Bhambri, Subbarao Kambhampati
Date: 2024-01-10 00:00:00
Tasks: Language Modelling, Large Language Model

Large Language Models have shown exceptional generative abilities in various natural language and generation tasks. However, possible anthropomorphization and leniency towards failure cases have propelled discussions on emergent abilities of Large Language Models especially on Theory of Mind (ToM) abilities in Large Language Models. While several false-belief tests exists to verify the ability to infer and maintain mental models of another entity, we study a special application of ToM abilities that has higher stakes and possibly irreversible consequences : Human Robot Interaction. In this work, we explore the task of Perceived Behavior Recognition, where a robot employs a Large Language Model (LLM) to assess the robot's generated behavior in a manner similar to human observer. We focus on four behavior types, namely - explicable, legible, predictable, and obfuscatory behavior which have been extensively used to synthesize interpretable robot behaviors. The LLMs goal is, therefore to be a human proxy to the agent, and to answer how a certain agent behavior would be perceived by the human in the loop, for example "Given a robot's behavior X, would the human observer find it explicable?". We conduct a human subject study to verify that the users are able to correctly answer such a question in the curated situations (robot setting and plan) across five domains. A first analysis of the belief test yields extremely positive results inflating ones expectations of LLMs possessing ToM abilities. We then propose and perform a suite of perturbation tests which breaks this illusion, i.e. Inconsistent Belief, Uninformative Context and Conviction Test. We conclude that, the high score of LLMs on vanilla prompts showcases its potential use in HRI settings, however to possess ToM demands invariance to trivial or irrelevant perturbations in the context which LLMs lack.
I am a Strange Dataset: Metalinguistic Tests for Language Models
Authors: Tristan Thrush, Jared Moore, Miguel Monares, Christopher Potts, Douwe Kiela
Date: 2024-01-10 00:00:00
Tasks: Sentence

Statements involving metalinguistic self-reference ("This paper has six sections.") are prevalent in many domains. Can current large language models (LLMs) handle such language? In this paper, we present "I am a Strange Dataset", a new dataset for addressing this question. There are two subtasks: generation and verification. In generation, models continue statements like "The penultimate word in this sentence is" (where a correct continuation is "is"). In verification, models judge the truth of statements like "The penultimate word in this sentence is sentence." (false). We also provide minimally different metalinguistic non-self-reference examples to complement the main dataset by probing for whether models can handle metalinguistic language at all. The dataset is hand-crafted by experts and validated by non-expert annotators. We test a variety of open-source LLMs (7B to 70B parameters) as well as closed-source LLMs through APIs. All models perform close to chance across both subtasks and even on the non-self-referential metalinguistic control data, though we find some steady improvement with model scale. GPT 4 is the only model to consistently do significantly better than chance, and it is still only in the 60% range, while our untrained human annotators score well in the 89-93% range. The dataset and evaluation toolkit are available at https://github.com/TristanThrush/i-am-a-strange-dataset.
INACIA: Integrating Large Language Models in Brazilian Audit Courts: Opportunities and Challenges
Authors: Jayr Pereira, Andre Assumpcao, Julio Trecenti, Luiz Airosa, Caio Lente, Jhonatan Cléto, Guilherme Dobins, Rodrigo Nogueira, Luis Mitchell, Roberto Lotufo
Date: 2024-01-10 00:00:00
Tasks: Decision Making, Fairness

This paper introduces INACIA (Instru\c{c}\~ao Assistida com Intelig\^encia Artificial), a groundbreaking system designed to integrate Large Language Models (LLMs) into the operational framework of Brazilian Federal Court of Accounts (TCU). The system automates various stages of case analysis, including basic information extraction, admissibility examination, Periculum in mora and Fumus boni iuris analyses, and recommendations generation. Through a series of experiments, we demonstrate INACIA's potential in extracting relevant information from case documents, evaluating its legal plausibility, and formulating propositions for judicial decision-making. Utilizing a validation dataset alongside LLMs, our evaluation methodology presents a novel approach to assessing system performance, correlating highly with human judgment. These results underscore INACIA's potential in complex legal task handling while also acknowledging the current limitations. This study discusses possible improvements and the broader implications of applying AI in legal contexts, suggesting that INACIA represents a significant step towards integrating AI in legal systems globally, albeit with cautious optimism grounded in the empirical findings.