pwc-archive/papers-with-abstracts huggingface dataset:
An open-source, three-dimensional growth model of the mandible
Authors: Cornelis Klop, Ruud Schreurs, Guido A De Jong, Edwin TM Klinkenberg, Valeria Vespasiano, Naomi L Rood, Valerie G Niehe, Vidija Soerdjbalie-Maikoe, Alexia Van Goethem, Bernadette S De Bakker, Thomas JJ Maal, Jitske W Nolte, Alfred G Becking
Date: 2023-11-01 00:00:00
Tasks: Decision Making, Diagnostic

The available reference data for the mandible and mandibular growth consists primarily of two-dimensional linear or angular measurements. The aim of this study was to create the first open-source, three-dimensional statistical shape model of the mandible that spans the complete growth period. Computed tomography scans of 678 mandibles from children and young adults between 0 and 22 years old were included in the model. The mandibles were segmented using a semi-automatic or automatic (artificial intelligence-based) segmentation method. Point correspondence among the samples was achieved by rigid registration, followed by non-rigid registration of a symmetrical template onto each sample. The registration process was validated with adequate results. Principal component analysis was used to gain insight in the variation within the dataset and to investigate age-related changes and sexual dimorphism. The presented growth model is accessible globally and free-of-charge for scientists, physicians and forensic investigators for any kind of purpose deemed suitable. The versatility of the model opens up new possibilities in the fields of oral and maxillofacial surgery, forensic sciences or biological anthropology. In clinical settings, the model may aid diagnostic decision-making, treatment planning and treatment evaluation.
Impact of Investing Characteristics on Financial Performance of Individual Investors: An Exploratory Study
Authors: Poompak Kusawat, Nopadol Rompho
Date: 2023-11-01 00:00:00
Tasks: regression

This exploratory study examines which investing characteristics determine success in an equity market. Based on data from 403 respondents, exploratory factor analysis results in 13 factors: middle/long time horizon, qualitative analyst, open-minded/disciplined, organized, emotional stability, na\"ive, growth stock, concentrated portfolio, contrarian, value stock, globalized, intrinsic value, and price-independent. Multiple linear regression of individual investors' excess return on these factors show statistically significant relationship. These results deepen our knowledge on what sort of investing characteristics are required to survive in equity markets.
Estimation of VaR with jump process: application in corn and soybean markets
Authors: Minglian Lin, Indranil SenGupta, William Wilson
Date: 2023-11-01 00:00:00

Value at Risk (VaR) is a quantitative measure used to evaluate the risk linked to the potential loss of investment or capital. Estimation of the VaR entails the quantification of prospective losses in a portfolio of investments, using a certain likelihood, under normal market conditions within a specific time period. The objective of this paper is to construct a model and estimate the VaR for a diversified portfolio consisting of multiple cash commodity positions driven by standard Brownian motions and jump processes. Subsequently, a thorough analytical estimation of the VaR is conducted for the proposed model. The results are then applied to two distinct commodities -- corn and soybean -- enabling a comprehensive comparison of the VaR values in the presence and absence of jumps.
Can AI Mitigate Human Perceptual Biases? A Pilot Study
Authors: Ross Geuy, Nate Rising, Tiancheng Shi, Meng Ling, Jian Chen
Date: 2023-10-10 00:00:00

We present results from a pilot experiment to measure if machine recommendations can debias human perceptual biases in visualization tasks. We specifically studied the ``pull-down'' effect, i.e., people underestimate the average position of lines, for the task of estimating the ensemble average of data points in line charts. These line charts can show for example temperature or precipitation in 12 months. Six participants estimated ensemble averages with or without an AI assistant. The assistant, when available, responded at three different speeds to assemble the conditions of a human collaborator who may delay his or her responses. Our pilot study showed that participants were faster with AI assistance in ensemble tasks, compared to the baseline without AI assistance. Although ``pull-down'' biases were reduced, the effect of AI assistance was not statistically significant. Also, delaying AI responses had no significant impact on human decision accuracy. We discuss the implications of these preliminary results for subsequent studies.
A Non-Recursive Space-Efficient Blind Approach to Find All Possible Solutions to the N-Queens Problem
Authors: Suklav Ghosh, Sarbajit Manna
Date: 2023-06-01 00:00:00
Proceeding: International Conference on Innovations in Data Analytics: ICIDA 2023 6
Tasks: All, N-Queens Problem - All Possible Solutions

N-Queen’s problem is the problem of placing N number of chess
queens on an NxN chessboard such that none of them attack each other. A chess
queen can move horizontally, vertically, and diagonally. So, the neighbours of a
queen have to be placed in such a way so that there is no clash in these three directions. Scientists accepts the fact that the branching factor increases in a nearly
linear fashion. With the use of artificial intelligence search patterns like Breadth
First Search (BFS), Depth First Search (DFS) and backtracking algorithms, many
academics have identified the problem and found out a number of techniques to
compute possible solutions to n-queen’s problem. The solutions using a blind
approach, that is, uninformed searches like BFS and DFS, use recursion. Also,
backtracking uses recursion for the solution of this problem. All these recursive
algorithms use a system stack which is limited. So, for a small value of N, it exhausts the memory quickly though it depends on machine. This paper deals with
the above problem and proposes a non-recursive DFS search-based approach to
solve the problem to save system memory. In this work, Depth First Search (DFS)
is used as a blind approach or uninformed search. This experimental study yields
a noteworthy result in terms of time and space.
CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection
Authors: Jiajin Tang, Ge Zheng, Jingyi Yu, Sibei Yang
Date: 2023-09-03 00:00:00
Proceeding: ICCV 2023 1
Tasks: Object, object-detection, Object Detection, Object Recognition

Task driven object detection aims to detect object instances suitable for affording a task in an image. Its challenge lies in object categories available for the task being too diverse to be limited to a closed set of object vocabulary for traditional object detection. Simply mapping categories and visual features of common objects to the task cannot address the challenge. In this paper, we propose to explore fundamental affordances rather than object categories, i.e., common attributes that enable different objects to accomplish the same task. Moreover, we propose a novel multi-level chain-of-thought prompting (MLCoT) to extract the affordance knowledge from large language models, which contains multi-level reasoning steps from task to object examples to essential visual attributes with rationales. Furthermore, to fully exploit knowledge to benefit object recognition and localization, we propose a knowledge-conditional detection framework, namely CoTDet. It conditions the detector from the knowledge to generate object queries and regress boxes. Experimental results demonstrate that our CoTDet outperforms state-of-the-art methods consistently and significantly (+15.6 box AP and +14.8 mask AP) and can generate rationales for why objects are detected to afford the task.
Improving the Performance of Object Detection by Preserving Balanced Class Distribution
Authors: Heewon Lee, Sangtae Ahn
Date: 2023-10-27 00:00:00
Proceeding: Mathematics 2023 10
Tasks: Object, object-detection, Object Detection

Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class, is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on private datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.
Enabling stability analysis of tubular reactor models using PDE/PDAE integrators
Authors: E.D. Koronaki, A.G. Boudouvis, I.G. Kevrekidis
Date: 2003-07-15 00:00:00
Proceeding: Computers & Chemical Engineering 2003 7

We discuss the construction of computational superstructures enabling time-dependent process simulation codes to perform. stability, continuation and bifurcation calculations tasks in principle not accessible to them directly. The basis of the approach is the so-called Recursive Projection Method of Shroff and Keller (SIAM Journal of Numerical Analysis 31). We discuss its implementation and performance for the detection of different types of bifurcations (with emphasis on Hopf bifurcations) as well as slight modifications appropriate for index 1 partial differential/algebraic equations (PDAE) simulators. Tests that help discriminate between physical and numerical (spurious) bifurcations detected in the process are discussed and illustrated through the standard example of a tubular reactor with a single irreversible exothermic reaction.
Amide Proton Transfer (APT) imaging in tumor with a machine learning approach using partially synthetic data
Authors: Malvika Viswanathan, Leqi Yin, Yashwant Kurmi, Zhongliang Zu
Date: 2023-11-03 00:00:00

Machine learning (ML) has been increasingly used to quantify chemical exchange saturation transfer (CEST) effect. ML models are typically trained using either measured data or fully simulated data. However, training with measured data often lacks sufficient training data, while training with fully simulated data may introduce bias due to limited simulations pools. This study introduces a new platform that combines simulated and measured components to generate partially synthetic CEST data, and to evaluate its feasibility for training ML models to predict amide proton transfer (APT) effect. Partially synthetic CEST signals were created using an inverse summation of APT effects from simulations and the other components from measurements. Training data were generated by varying APT simulation parameters and applying scaling factors to adjust the measured components, achieving a balance between simulation flexibility and fidelity. First, tissue-mimicking CEST signals along with ground truth information were created using multiple-pool model simulations to validate this method. Second, an ML model was trained individually on partially synthetic data, in vivo data, and fully simulated data, to predict APT effect in rat brains bearing 9L tumors. Experiments on tissue-mimicking data suggest that the ML method using the partially synthetic data is accurate in predicting APT. In vivo experiments suggest that our method provides more accurate and robust prediction than the training using in vivo data and fully synthetic data. Partially synthetic CEST data can address the challenges in conventional ML methods.
E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification
Authors: Sneh Pandya, Purvik Patel, Franc O, Jonathan Blazek
Date: 2023-11-02 00:00:00
Tasks: Inductive Bias, Morphology classification

We propose the use of group convolutional neural network architectures (GCNNs) equivariant to the 2D Euclidean group, $E(2)$, for the task of galaxy morphology classification by utilizing symmetries of the data present in galaxy images as an inductive bias in the architecture. We conduct robustness studies by introducing artificial perturbations via Poisson noise insertion and one-pixel adversarial attacks to simulate the effects of limited observational capabilities. We train, validate, and test GCNNs equivariant to discrete subgroups of $E(2)$ - the cyclic and dihedral groups of order $N$ - on the Galaxy10 DECals dataset and find that GCNNs achieve higher classification accuracy and are consistently more robust than their non-equivariant counterparts, with an architecture equivariant to the group $D_{16}$ achieving a $95.52 \pm 0.18\%$ test-set accuracy. We also find that the model loses $<6\%$ accuracy on a $50\%$-noise dataset and all GCNNs are less susceptible to one-pixel perturbations than an identically constructed CNN. Our code is publicly available at https://github.com/snehjp2/GCNNMorphology.
DropCov: A Simple yet Effective Method for Improving Deep Architectures
Authors: Qilong Wang, Mingze Gao, Zhaolin Zhang, Jiangtao Xie, Peihua Li, QinGhua Hu
Date: 2023-01-10 00:00:00
Proceeding: NeurIPS 2022 Conference 2023 1

Previous works show global covariance pooling (GCP) has great potential to improve deep architectures especially on visual recognition tasks, where post-normalization of GCP plays a very important role in final performance. Although several post-normalization strategies have been studied, these methods pay more close attention to effect of normalization on covariance representations rather than the whole GCP networks, and their effectiveness requires further understanding. Meanwhile, existing effective post-normalization strategies (e.g., matrix power normalization) usually suffer from high computational complexity (e.g., $O(d^{3})$ for $d$-dimensional inputs). To handle above issues, this work first analyzes the effect of post-normalization from the perspective of training GCP networks. Particularly, we for the first time show that \textit{effective post-normalization can make a good trade-off between representation decorrelation and information preservation for GCP, which are crucial to alleviate over-fitting and increase representation ability of deep GCP networks, respectively}. Based on this finding, we can improve existing post-normalization methods with some small modifications, providing further support to our observation. Furthermore, this finding encourages us to propose a novel pre-normalization method for GCP (namely DropCov), which develops an adaptive channel dropout on features right before GCP, aiming to reach trade-off between representation decorrelation and information preservation in a more efficient way. Our DropCov only has a linear complexity of $O(d)$, while being free for inference. Extensive experiments on various benchmarks (i.e., ImageNet-1K, ImageNet-C, ImageNet-A, Stylized-ImageNet, and iNat2017) show our DropCov is superior to the counterparts in terms of efficiency and effectiveness, and provides a simple yet effective method to improve performance of deep architectures involving both deep convolutional neural networks (CNNs) and vision transformers (ViTs).
Post Turing: Mapping the landscape of LLM Evaluation
Authors: Alexey Tikhonov, Ivan P. Yamshchikov
Date: 2023-11-03 00:00:00
Tasks: Fairness

In the rapidly evolving landscape of Large Language Models (LLMs), introduction of well-defined and standardized evaluation methodologies remains a crucial challenge. This paper traces the historical trajectory of LLM evaluations, from the foundational questions posed by Alan Turing to the modern era of AI research. We categorize the evolution of LLMs into distinct periods, each characterized by its unique benchmarks and evaluation criteria. As LLMs increasingly mimic human-like behaviors, traditional evaluation proxies, such as the Turing test, have become less reliable. We emphasize the pressing need for a unified evaluation system, given the broader societal implications of these models. Through an analysis of common evaluation methodologies, we advocate for a qualitative shift in assessment approaches, underscoring the importance of standardization and objective criteria. This work serves as a call for the AI community to collaboratively address the challenges of LLM evaluation, ensuring their reliability, fairness, and societal benefit.
Quantum circuit synthesis with diffusion models
Authors: Florian Fürrutter, Gorka Muñoz-Gil, Hans J. Briegel
Date: 2023-11-03 00:00:00
Tasks: Denoising, Quantum Circuit Generation

Quantum computing has recently emerged as a transformative technology. Yet, its promised advantages rely on efficiently translating quantum operations into viable physical realizations. In this work, we use generative machine learning models, specifically denoising diffusion models (DMs), to facilitate this transformation. Leveraging text-conditioning, we steer the model to produce desired quantum operations within gate-based quantum circuits. Notably, DMs allow to sidestep during training the exponential overhead inherent in the classical simulation of quantum dynamics -- a consistent bottleneck in preceding ML techniques. We demonstrate the model's capabilities across two tasks: entanglement generation and unitary compilation. The model excels at generating new circuits and supports typical DM extensions such as masking and editing to, for instance, align the circuit generation to the constraints of the targeted quantum device. Given their flexibility and generalization abilities, we envision DMs as pivotal in quantum circuit synthesis, enhancing both practical applications but also insights into theoretical quantum computation.
VQPy: An Object-Oriented Approach to Modern Video Analytics
Authors: Shan Yu, Zhenting Zhu, Yu Chen, Hanchen Xu, Pengzhan Zhao, Yang Wang, Arthi Padmanabhan, Hugo Latapie, Harry Xu
Date: 2023-11-03 00:00:00
Tasks: Object

Video analytics is widely used in contemporary systems and services. At the forefront of video analytics are video queries that users develop to find objects of particular interest. Building upon the insight that video objects (e.g., human, animals, cars, etc.), the center of video analytics, are similar in spirit to objects modeled by traditional object-oriented languages, we propose to develop an object-oriented approach to video analytics. This approach, named VQPy, consists of a frontend$\unicode{x2015}$a Python variant with constructs that make it easy for users to express video objects and their interactions$\unicode{x2015}$as well as an extensible backend that can automatically construct and optimize pipelines based on video objects. We have implemented and open-sourced VQPy, which has been productized in Cisco as part of its DeepVision framework.
APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model
Authors: Miguel Contreras, Brandon Silva, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Yuanfang Ren, Ziyuan Guan, Jeremy Balch, Jiaqing Zhang, Sabyasachi Bandyopadhyay, Kia Khezeli, Azra Bihorac, Parisa Rashidi
Date: 2023-11-03 00:00:00
Tasks: Mamba, Mortality Prediction

The acuity state of patients in the intensive care unit (ICU) can quickly change from stable to unstable. Early detection of deteriorating conditions can result in providing timely interventions and improved survival rates. In this study, we propose APRICOT-M (Acuity Prediction in Intensive Care Unit-Mamba), a 150k-parameter state space-based neural network to predict acuity state, transitions, and the need for life-sustaining therapies in real-time in ICU patients. The model uses data obtained in the prior four hours in the ICU and patient information obtained at admission to predict the acuity outcomes in the next four hours. We validated APRICOT-M externally on data from hospitals not used in development (75,668 patients from 147 hospitals), temporally on data from a period not used in development (12,927 patients from one hospital from 2018-2019), and prospectively on data collected in real-time (215 patients from one hospital from 2021-2023) using three large datasets: the University of Florida Health (UFH) dataset, the electronic ICU Collaborative Research Database (eICU), and the Medical Information Mart for Intensive Care (MIMIC)-IV. The area under the receiver operating characteristic curve (AUROC) of APRICOT-M for mortality (external 0.94-0.95, temporal 0.97-0.98, prospective 0.96-1.00) and acuity (external 0.95-0.95, temporal 0.97-0.97, prospective 0.96-0.96) shows comparable results to state-of-the-art models. Furthermore, APRICOT-M can predict transitions to instability (external 0.81-0.82, temporal 0.77-0.78, prospective 0.68-0.75) and need for life-sustaining therapies, including mechanical ventilation (external 0.82-0.83, temporal 0.87-0.88, prospective 0.67-0.76), and vasopressors (external 0.81-0.82, temporal 0.73-0.75, prospective 0.66-0.74). This tool allows for real-time acuity monitoring in critically ill patients and can help clinicians make timely interventions.
Active Reasoning in an Open-World Environment
Date: 2023-11-03 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Instruction Following, Minecraft, Question Answering, World Knowledge

Recent advances in vision-language learning have achieved notable success on complete-information question-answering datasets through the integration of extensive world knowledge. Yet, most models operate passively, responding to questions based on pre-stored knowledge. In stark contrast, humans possess the ability to actively explore, accumulate, and reason using both newfound and existing information to tackle incomplete-information questions. In response to this gap, we introduce $Conan$, an interactive open-world environment devised for the assessment of active reasoning. $Conan$ facilitates active exploration and promotes multi-round abductive inference, reminiscent of rich, open-world settings like Minecraft. Diverging from previous works that lean primarily on single-round deduction via instruction following, $Conan$ compels agents to actively interact with their surroundings, amalgamating new evidence with prior knowledge to elucidate events from incomplete observations. Our analysis on $Conan$ underscores the shortcomings of contemporary state-of-the-art models in active exploration and understanding complex scenarios. Additionally, we explore Abduction from Deduction, where agents harness Bayesian rules to recast the challenge of abduction as a deductive process. Through $Conan$, we aim to galvanize advancements in active reasoning and set the stage for the next generation of artificial intelligence agents adept at dynamically engaging in environments.