pwc-archive/papers-with-abstracts huggingface dataset:
Towards Generalizable AI-Assisted Misinformation Inoculation: Protecting Confidence Against False Election Narratives
Authors: Mitchell Linegar, Betsy Sinclair, Sander van der Linden, R. Michael Alvarez
Date: 2024-10-24 00:00:00
Tasks: Misinformation

We present a generalizable AI-assisted framework for rapidly generating effective "prebunking" interventions against misinformation. Like mRNA vaccine platforms, our approach uses a stable template structure that can be quickly adapted to counter emerging false narratives. In a preregistered two-wave experiment with 4,293 U.S. registered voters, we test this framework against politically-charged election misinformation -- one of the most challenging domains for misinformation intervention. Our design directly tests scalability by comparing human-reviewed and purely AI-generated inoculation messages. We find that LLM-generated prebunking significantly reduced belief in election rumors (persisting for at least one week) and increased confidence in election integrity across partisan lines. Purely AI-generated messages proved as effective as human-reviewed versions, with some achieving larger protective effects, demonstrating that effective misinformation inoculation can be achieved at machine speed without proportional human effort, offering a scalable defense against the accelerating threat of false narratives across all domains.
Generating long-horizon stock "buy" signals with a neural language model
Authors: Joel R. Bock
Date: 2024-10-09 00:00:00
Tasks: Language Modeling, Language Modelling, Small Language Model

This paper describes experiments on fine-tuning a small language model to generate forecasts of long-horizon stock price movements. Inputs to the model are narrative text from 10-K reports of large market capitalization companies in the S&P 500 index; the output is a forward-looking buy or sell decision. Price direction is predicted at discrete horizons up to 12 months after the report filing date. The results reported here demonstrate good out-of-sample statistical performance (F1-macro= 0.62) at medium to long investment horizons. In particular, the buy signals generated from 10-K text are found most precise at 6 and 9 months in the future. As measured by the F1 score, the buy signal provides between 4.8 and 9 percent improvement against a random stock selection model. In contrast, sell signals generated by the models do not perform well. This may be attributed to the highly imbalanced out-of-sample data, or perhaps due to management drafting annual reports with a bias toward positive language. Cross-sectional analysis of performance by economic sector suggests that idiosyncratic reporting styles within industries are correlated with varying degrees and time scales of price movement predictability.
Information Sharing with Social Image Concerns and the Spread of Fake News
Authors: Dana Sisak, Philipp Denter
Date: 2024-10-25 00:00:00

We study how social image concerns influence information sharing between peers. Individuals receive a signal about a binary state of the world, characterized by a direction and a veracity status. While the direction is freely observable, verifying veracity is costly and type-dependent. We examine two types of social image motives: a desire to appear talented -- i.e., able to distinguish real from fake news -- and a desire to signal one's worldview. For each motive, we characterize equilibrium sharing patterns and derive implications for the quality of shared information. We show that fake news may be shared more frequently than factual news (e.g., Vosoughi et al., 2018}). Both ability- and worldview-driven motives can rationalize this behavior, though they lead to empirically distinct sharing patterns and differing welfare implications.
Performance Rating Equilibrium
Authors: Mehmet S. Ismail
Date: 2024-10-21 00:00:00

In this note, I introduce a novel performance rating system called Performance Rating Equilibrium (PRE). A PRE is a vector of hypothetical ratings for each player, such that if these ratings were each player's initial rating at the start of a tournament, scoring the same points against the same opponents would leave each player's initial rating unchanged. In other words, all players' initial ratings perfectly predict their actual scores in the tournament. This property, however, does not hold for the well-known Tournament Performance Rating. PRE is defined as a fixed point of a multidimensional rating function. I show that such a fixed point, and hence a PRE, exists under mild conditions. I provide an implementation of PRE along with several empirical applications. PREs have broad applicability, from sports competitions to the evaluation of large language models.
Stochastic cooperative games of risk averse players and application to multiple newsvendors problem
Authors: David Ryzák, Martin Černý
Date: 2024-10-20 00:00:00

This paper studies the stochastic setting in cooperative games and suggests a solution concept based on second order stochastic dominance (SSD), which is often applied to robustly model risk averse behaviour of players in different economic and game theoretic models as it enables to model not specified levels of risk aversion among players. The main result of the paper connects this solution concept, \emph{SSD-core}, in case of uniform distribution of the game to cores of two deterministic cooperative games. Interestingly, balancedness of both of these games and convexity of one of these implies non-emptiness of the SSD-core. The opposite implication does not, in general, hold and leads to questions about intersections of cores of two games and their relations. Finally, we present an application of the SSD-core to the multiple newsvendors problem, where we provide a characterization of risk averse behaviour of players with an interpretation in terms of the model.
SDR-Based Metal Classification using Spectrogram Images from Micro-Doppler Signatures
Authors: Salman Liaquat, Faran Awais Butt, Faryal Aurooj Nasir, Ijaz Haider Naqvi, Nor Muzlifah Mahyuddin, Ali Hussein Muqaibel, Saleh Alawsh
Date: 2024-10-25 00:00:00

Metallic materials such as brass, copper, and aluminum are used in numerous applications, including industrial manufacturing. The vibration characteristics of these objects are unique and can be used to identify these objects from a distance. This research presents a methodology for detecting and classifying these metallic objects using the vibration dynamics induced by their micro-Doppler signatures. The proposed approach utilizes image processing techniques to extract pivotal features from spectrograms. These spectrograms originate from micro-Doppler signatures of data collected during controlled laboratory experiments where signals were transmitted towards vibrating metal sheets, and the ensuing reflections were recorded using a software-defined radio (SDR). The spectrogram data was augmented using geometric transformation to train a convolutional neural network (CNN) based machine learning model for object classification. The results indicate that the proposed CNN model achieved an accuracy of more than 95% in classifying metals into brass, copper, and aluminum. This research could be used to understand the foundations of classifying spectrogram images using micro-Doppler signatures for its applications towards enhancing the sensing capabilities in industrial and defense applications.
Teach Multimodal LLMs to Comprehend Electrocardiographic Images
Authors: Ruoqi Liu, Yuelin Bai, Xiang Yue, Ping Zhang
Date: 2024-10-21 00:00:00
Tasks: Diagnostic, Image Comprehension

The electrocardiogram (ECG) is an essential non-invasive diagnostic tool for assessing cardiac conditions. Existing automatic interpretation methods suffer from limited generalizability, focusing on a narrow range of cardiac conditions, and typically depend on raw physiological signals, which may not be readily available in resource-limited settings where only printed or digital ECG images are accessible. Recent advancements in multimodal large language models (MLLMs) present promising opportunities for addressing these challenges. However, the application of MLLMs to ECG image interpretation remains challenging due to the lack of instruction tuning datasets and well-established ECG image benchmarks for quantitative evaluation. To address these challenges, we introduce ECGInstruct, a comprehensive ECG image instruction tuning dataset of over one million samples, covering a wide range of ECG-related tasks from diverse data sources. Using ECGInstruct, we develop PULSE, an MLLM tailored for ECG image comprehension. In addition, we curate ECGBench, a new evaluation benchmark covering four key ECG image interpretation tasks across nine different datasets. Our experiments show that PULSE sets a new state-of-the-art, outperforming general MLLMs with an average accuracy improvement of 15% to 30%. This work highlights the potential of PULSE to enhance ECG interpretation in clinical practice.
A Modulo Sampling Hardware Prototype and Reconstruction Algorithm Evaluation
Authors: Jiang Zhu, Junnan Ma, Zhenlong Liu, Fengzhong Qu, Zheng Zhu, Qi Zhang
Date: 2024-10-25 00:00:00

Analog-to-digital converters (ADCs) play a vital important role in any devices via manipulating analog signals in a digital manner. Given that the amplitude of the signal exceeds the dynamic range of the ADCs, clipping occurs and the quality of the digitized signal degrades significantly. In this paper, we design a joint modulo sampling hardware and processing prototype which improves the ADCs' dynamic range by folding the signal before sampling. Both the detailed design of the hardware and the recovery results of various state-of-the-art processing algorithms including our proposed unlimited sampling line spectral estimation (USLSE) algorithm are presented. Additionally, key issues that arise during implementation are also addressed. It is demonstrated that the USLSE algorithm successfully recovers the original signal with a frequency of 2.5 kHz and an amplitude 10 times the ADC's dynamic range, and the linear prediction (LP) algorithm successfully recovers the original signal with a frequency of 3.5 kHz and an amplitude 10 times the ADC's dynamic range.
Joint Beamforming Design and Satellite Selection for Integrated Communication and Navigation in LEO Satellite Networks
Authors: Jiajing Li, Binghong Liu, Mugen Peng
Date: 2024-10-25 00:00:00

Relying on the powerful communication capabilities and rapidly changing geometric configuration, the Low Earth Orbit (LEO) satellites have the potential to offer integrated communication and navigation (ICAN) services. However, the isolated resource utilization in the traditional satellite communication and navigation systems has led to a compromised system performance. Against this backdrop, this paper formulates a joint beamforming design and satellite selection optimization problem for the LEO-ICAN network to maximize the sum rate, while simultaneously reconciling the positioning performance. A two-layer algorithm is proposed, where the beamforming design in the inner layer is solved by the difference-of-convex programming method to maximize the sum rate, and the satellite selection in the outer layer is modeled as a coalition formation game to simultaneously reconcile the positioning performance. Simulation results verify the superiority of our proposed algorithms by increasing the sum rate by 16.6% and 29.3% compared with the conventional beamforming and satellite selection schemes, respectively.
Deterministic Fokker-Planck Transport -- With Applications to Sampling, Variational Inference, Kernel Mean Embeddings & Sequential Monte Carlo
Authors: Ilja Klebanov
Date: 2024-10-11 00:00:00
Tasks: Density Estimation, Variational Inference

The Fokker-Planck equation can be reformulated as a continuity equation, which naturally suggests using the associated velocity field in particle flow methods. While the resulting probability flow ODE offers appealing properties - such as defining a gradient flow of the Kullback-Leibler divergence between the current and target densities with respect to the 2-Wasserstein distance - it relies on evaluating the current probability density, which is intractable in most practical applications. By closely examining the drawbacks of approximating this density via kernel density estimation, we uncover opportunities to turn these limitations into advantages in contexts such as variational inference, kernel mean embeddings, and sequential Monte Carlo.
Hypothalamic expression analysis of m6A RNA methylation associated genes suggests a potential role of epitransciptomics in sexual maturation of Atlantic salmon
Authors: Ehsan Pashay Ahi, Morgane Frapin, Mikaela Hukkanen, Craig R. Primmer
Date: 2024-10-25 00:00:00

Better understanding the molecular processes contributing to variation in maturation timing is important for Atlantic salmon aquaculture, as early maturation causes considerable financial losses. The m6A RNA methylation is a conserved and dynamically reversible mechanism controlling gene expression in a myriad of biological processes. The role of m6A methylation in sexual maturation, however, has remained largely unexplored and has never been studied in Atlantic salmon. While the maturation process is known to be affected by many genetic and environmental factors, the molecular mechanisms causing variation in the timing of maturation are still poorly understood. Hence, investigation of whether a widespread mechanism like m6A methylation could be involved in controlling of the maturation timing is warranted. In salmon, two genes, also associated with age at maturity in humans, vgll3 and six6, have been shown to play an important role in maturation timing. In this study, we investigated the expression of 16 genes involved in the regulation of m6A RNA methylation in the hypothalamus of Atlantic salmon with different homozygous combinations of late and early alleles for vgll3 and six6 genes. We found differential expression of ythdf2.2, an m6A reader promoting mRNA degradation, with higher expression in six6*LL compared to other genotypes as well as in immature compared to mature males. In addition, we found that the expression levels of genes coding for an eraser, alkbh5, and for a reader, ythdf1, were higher in the hypothalamus of females than in males across all the different genotypes studied. However the total m6A levels between the whole hypothalamus of males and females were similar. Our results indicate a potential role of the m6A methylation process in sexual maturation of salmon, and provide the first evidence for such regulatory mechanism in the hypothalamus of any vertebrate
What Drives Liquidity on Decentralized Exchanges? Evidence from the Uniswap Protocol
Authors: Brian Z. Zhu, Dingyue Liu, Xin Wan, Gordon Liao, Ciamac C. Moallemi, Brad Bachu
Date: 2024-10-24 00:00:00
Tasks: counterfactual

We study liquidity on decentralized exchanges (DEXs), identifying factors at the platform, blockchain, token pair, and liquidity pool levels with predictive power for market depth metrics. We introduce the v2 counterfactual spread metric, a novel criterion which assesses the degree of liquidity concentration in pools using the ``concentrated liquidity'' mechanism, allowing us to decompose the effect of a factor on market depth into two channels: total value locked (TVL) and concentration. We further explore how external liquidity from competing DEXs and private inventory on DEX aggregators influence market depth. We find that (i) gas prices, returns, and a DEX's share of trading volume affect liquidity through concentration, (ii) internalization of order flow by private market makers affects TVL but not the overall market depth, and (iii) volatility, fee revenue, and markout affect liquidity through both channels.
Very High-Resolution Bridge Deformation Monitoring Using UAV-based Photogrammetry
Authors: Mehdi Maboudi, Jan Backhaus, Yahya Ghassoun, Yogesh Khedar, Dirk Lowke, Inka Mai, Bjoern Riedel, Ulf Bestmann, Markus Gerke
Date: 2024-10-09 00:00:00
Tasks: Structural Health Monitoring

Accurate and efficient structural health monitoring of infrastructure objects such as bridges is a vital task, as many existing constructions have already reached or are approaching their planned service life. In this contribution, we address the question of the suitability of UAV-based monitoring for SHM, in particular focusing on the geometric deformation under load. Such an advanced technology is becoming increasingly popular due to its ability to decrease the cost and risk of tedious traditional inspection methods. To this end, we performed extensive tests employing a research reinforced concrete bridge that can be exposed to a predefined load via ground anchors. Very high-resolution image blocks have been captured before, during, and after the application of controlled loads. From those images, the motion of distinct points on the bridge has been monitored, and in addition, dense image point clouds were computed to evaluate the performance of surface-based data acquisition. Moreover, a geodetic control network in stable regions is used as control information for bundle adjustment. We applied different sensing technologies in order to be able to judge the image-based deformation results: displacement transducers, tachymetry, and laser profiling. As a platform for the photogrammetric measurements, a multi-rotor UAV DJI Matrice 600 Pro was employed, equipped with two RTK-GNSS receivers. The mounted camera was a PhaseOne iXM-100 (100MP) with an 80 mm lens. With a flying height of 30 m above the terrain, this resulted in a GSD of 1.3 mm while a forward and sideward overlap of 80% was maintained. The comparison with reference data (displacement transducers) reveals a difference of less than 1 mm. We show that by employing the introduced UAV-based monitoring approach, a full area-wide quantification of deformation is possible in contrast to classical point or profile measurements.
VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling
Authors: Hayata Morita, Kohei Shintani, Chenyang Yuan, Frank Permenter
Date: 2024-10-09 00:00:00

A main challenge in mechanical design is to efficiently explore the design space while satisfying engineering constraints. This work explores the use of 3D generative models to explore the design space in the context of vehicle development, while estimating and enforcing engineering constraints. Specifically, we generate diverse 3D models of cars that meet a given set of geometric specifications, while also obtaining quick estimates of performance parameters such as aerodynamic drag. For this, we employ a data-driven approach (using the ShapeNet dataset) to train VehicleSDF, a DeepSDF based model that represents potential designs in a latent space witch can be decoded into a 3D model. We then train surrogate models to estimate engineering parameters from this latent space representation, enabling us to efficiently optimize latent vectors to match specifications. Our experiments show that we can generate diverse 3D models while matching the specified geometric parameters. Finally, we demonstrate that other performance parameters such as aerodynamic drag can be estimated in a differentiable pipeline.
Generative Topology for Shape Synthesis
Authors: Ernst Röell, Bastian Rieck
Date: 2024-10-09 00:00:00

The Euler Characteristic Transform (ECT) is a powerful invariant for assessing geometrical and topological characteristics of a large variety of objects, including graphs and embedded simplicial complexes. Although the ECT is invertible in theory, no explicit algorithm for general data sets exists. In this paper, we address this lack and demonstrate that it is possible to learn the inversion, permitting us to develop a novel framework for shape generation tasks on point clouds. Our model exhibits high quality in reconstruction and generation tasks, affords efficient latent-space interpolation, and is orders of magnitude faster than existing methods.
O1 Replication Journey: A Strategic Progress Report -- Part 1
Authors: Yiwei Qin, Xuefeng Li, Haoyang Zou, Yixiu Liu, Shijie Xia, Zhen Huang, Yixin Ye, Weizhe Yuan, Hector Liu, Yuanzhi Li, PengFei Liu
Date: 2024-10-08 00:00:00
Tasks: Math, scientific discovery

This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey. In response to the announcement of OpenAI's groundbreaking O1 model, we embark on a transparent, real-time exploration to replicate its capabilities while reimagining the process of conducting and communicating AI research. Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects, delayed information sharing, and the lack of recognition for diverse contributions. By providing comprehensive, real-time documentation of our replication efforts, including both successes and failures, we aim to foster open science, accelerate collective advancement, and lay the groundwork for AI-driven scientific discovery. Our research progress report diverges significantly from traditional research papers, offering continuous updates, full process transparency, and active community engagement throughout the research journey. Technologically, we proposed the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process, including trial and error, reflection, and backtracking. With only 327 training samples and without any additional tricks, journey learning outperformed conventional supervised learning by over 8\% on the MATH dataset, demonstrating its extremely powerful potential. We believe this to be the most crucial component of O1 technology that we have successfully decoded. We share valuable resources including technical hypotheses and insights, cognitive exploration maps, custom-developed tools, etc at https://github.com/GAIR-NLP/O1-Journey.
rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG
Authors: Shreya Srivastava, Durgesh Kumar, Jatin Bedi, Sandeep Seth, Deepak Sharma
Date: 2024-10-09 00:00:00
Tasks: Anomaly Detection, Arrhythmia Detection, Transfer Learning

A substantial amount of variability in ECG manifested due to patient characteristics hinders the adoption of automated analysis algorithms in clinical practice. None of the ECG annotators developed till date consider the characteristics of the patients in a multi-modal architecture. We employed the XGBoost model to analyze the UCI Arrhythmia dataset, linking patient characteristics to ECG morphological changes. The model accurately classified patient gender using discriminative ECG features with 87.75% confidence. We propose a novel multi-modal methodology for ECG analysis and arrhythmia classification that can help defy the variability in ECG related to patient-specific conditions. This deep learning algorithm, named rECGnition_v1.0 (robust ECG abnormality detection Version 1), fuses Beat Morphology with Patient Characteristics to create a discriminative feature map that understands the internal correlation between both modalities. A Squeeze and Excitation based Patient characteristic Encoding Network (SEPcEnet) has been introduced, considering the patient's demographics. The trained model outperformed the various existing algorithms by achieving the overall F1-score of 0.986 for the ten arrhythmia class classification in the MITDB and achieved near perfect prediction scores of ~0.99 for LBBB, RBBB, Premature ventricular contraction beat, Atrial premature beat and Paced beat. Subsequently, the methodology was validated across INCARTDB, EDB and different class groups of MITDB using transfer learning. The generalizability test provided F1-scores of 0.980, 0.946, 0.977, and 0.980 for INCARTDB, EDB, MITDB AAMI, and MITDB Normal vs. Abnormal Classification, respectively. Therefore, with a more enhanced and comprehensive understanding of the patient being examined and their ECG for diverse CVD manifestations, the proposed rECGnition_v1.0 algorithm paves the way for its deployment in clinics.