pwc-archive/papers-with-abstracts huggingface dataset:
A Query-Response Causal Analysis of Reaction Events in Biochemical Reaction Networks
Authors: Pavel Loskot
Date: 2022-12-21 00:00:00
Tasks: Time Series, Time Series Analysis

The stochastic kinetics of BRN are described by a chemical master equation (CME) and the underlying laws of mass action. The CME must be usually solved numerically by generating enough traces of random reaction events. The resulting event-time series can be evaluated statistically to identify, for example, the reaction clusters, rare reaction events, and the periods of increased or steady-state activity. The aim of this paper is to newly exploit the empirical statistics of the reaction events in order to obtain causally and anti-causally related sub-sequences of reactions. This allows discovering some of the causal dynamics of the reaction networks as well as uncovering their more deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related or unrelated, respectively. Moreover, since time-ordering of reactions is locally irrelevant, the reaction sub-sequences can be transformed into the reaction event sets or multi-sets. The appropriately defined distance metrics can be then used to define equivalences between the reaction sub-sequences. The proposed framework for identifying causally associated reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The framework was evaluated assuming five selected models of genetic reaction networks in seven defined numerical experiments. The models were simulated in BioNetGen using NFsim, which had to be modified to allow recording of the traces of reaction events. The generated event time-series were analyzed by Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts.
Estimating and Assessing Differential Equation Models with Time-Course Data
Authors: Samuel W. K. Wong, Shihao Yang, S. C. Kou
Date: 2022-12-20 00:00:00
Tasks: Numerical Integration, Uncertainty Quantification

Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy and some components of the system may not be observed. Furthermore, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories, including unobserved components, with appropriate uncertainty quantification. Second, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI's efficient computation of model predictions. Overall, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models, which bypasses the need for any numerical integration.
Memory recall by controlling chaos
Authors: Fan Zhang
Date: 2022-11-10 00:00:00

By incorporating feedback loops, that engender amplification and damping so that output is not proportional to input, the biological neural networks become highly nonlinear and thus very likely chaotic in nature. Research in control theory reveals that strange attractors can be approximated by collection of cycles, and be collapsed into a more coherent state centered on one of them if we exert control. We speculate that human memories are encoded by such cycles, and can be retrieved once sensory or virtual cues, acting as references, enable feedback controls that nucleates the otherwise chaotic wandering mind.
Statistical Comparison among Brain Networks with Popular Network Measurement Algorithms
Authors: Rakib Hassan Pran
Date: 2022-10-22 00:00:00

In this research, a number of popular network measurement algorithms have been applied to several brain networks (based on applicability of algorithms) for finding out statistical correlation among these popular network measurements which will help scientists to understand these popular network measurement algorithms and their applicability to brain networks. By analysing the results of correlations among these network measurement algorithms, statistical comparison among selected brain networks has also been summarized. Besides that, to understand each brain network, the visualization of each brain network and each brain network degree distribution histogram have been extrapolated. Six network measurement algorithms have been chosen to apply time to time on sixteen brain networks based on applicability of these network measurement algorithms and the results of these network measurements are put into a correlation method to show the relationship among these six network measurement algorithms for each brain network. At the end, the results of the correlations have been summarized to show the statistical comparison among these sixteen brain networks.
Biophysical Simulation Reveals the Mechanics of the Avian Lumbosacral Organ
Authors: An Mo, Viktoriia Kamska, Fernanda Bribiesca-Contreras, Janet Hauptmann, Monica Daley, Alexander Badri-Spröwitz
Date: 2022-12-22 00:00:00

The lumbosacral organ (LSO) is a lumbosacral spinal canal morphology that is universally and uniquely found in birds. Recent studies suggested an intraspinal mechanosensor function that relies on the compliant motion of soft tissue in the spinal cord fluid. It has not yet been possible to observe LSO soft tissue motion in vivo due to limitations of imaging technologies. As an alternative approach, we developed an artificial biophysical model of the LSO, and characterize the dynamic responses of this model when entrained by external motion. The parametric model incorporates morphological and material properties of the LSO. We varied the model's parameters to study the influence of individual features on the system response. We characterized the system in a locomotion simulator, producing vertical oscillations similar to the trunk motions. We show how morphological and material properties effectively shape the system's oscillation characteristics. We conclude that external oscillations could entrain the soft tissue of the intraspinal lumbosacral organ during locomotion, consistent with recently proposed sensing mechanisms.
The quintic Ornstein-Uhlenbeck volatility model that jointly calibrates SPX & VIX smiles
Authors: Eduardo Abi Jaber, Camille Illand, Shaun, Li
Date: 2022-12-21 00:00:00

The quintic Ornstein-Uhlenbeck volatility model is a stochastic volatility model where the volatility process is a polynomial function of degree five of a single Ornstein-Uhlenbeck process with fast mean reversion and large vol-of-vol. The model is able to achieve remarkable joint fits of the SPX-VIX smiles with only 6 effective parameters and an input curve that allows to match certain term structures. We provide several practical specifications of the input curve, study their impact on the joint calibration problem and consider additionally time-dependent parameters to help achieve better fits for longer maturities going beyond 1 year. Even better, the model remains very simple and tractable for pricing and calibration: the VIX squared is again polynomial in the Ornstein-Uhlenbeck process, leading to efficient VIX derivative pricing by a simple integration against a Gaussian density; simulation of the volatility process is exact; and pricing SPX products derivatives can be done efficiently and accurately by standard Monte Carlo techniques with suitable antithetic and control variates.
The Impact of Regulation Regime Changes on ChiNext IPOs: Effects of 2013 and 2020 Reforms on Pricing and Overreaction
Authors: Qi Deng, Lunge Dai, Zixin Yang, Zhong-guo Zhou, Monica Hussein, Dingyi Chen, Mick Swartz
Date: 2022-11-19 00:00:00

Since its inauguration, ChiNext has gone through three time periods with two different regulation regimes and three different sets of listing day trading restrictions. This paper studies the impact of regulation regimes and listing day trading restrictions on the initial return of ChiNext IPOs. We hypothesize that the initial return of a ChiNext IPO contains the issuers intrinsic value and the investors overreaction. The intrinsic value is represented by the IPOs 21st day return (monthly return), and the difference between the monthly and initial returns (intramonth return) is a proxy of the overreaction. We find that all significant variables for all three returns in all three time periods fall into four categories: pre-listing demand, post-listing demand, market condition and pre-listing issuer value. We observe stark contrasts among variable categories for each of the returns in the three time periods, which reveals an evolution of the investors behavior with regard to the progression of regulation regimes. Based on our findings, we argue that the differences among the levels and determinants of initial return, monthly return (intrinsic value) and intramonth return (overreaction) in different time periods can be largely explained by regulation regime changes along two dimensions: 1) approval vs. registration and 2) listing day trading curbs and return limits. We find that IPO pricing is demand-driven under the approval regime, but value-driven under the registration regime. We further compare the impact of regulation regime changes on ChiNext IPO pricing practice, and propose a future research plan on ChiNext IPO pricing efficiency with policy implication.
Long bet will lose: demystifying seemingly fair gambling via two-armed Futurity bandit
Authors: Zengjing Chen, Huaijin Liang, Wei Wang, Xiaodong Yan
Date: 2022-11-12 00:00:00
Tasks: Fairness, Marketing

No matter how much some gamblers occasionally win, as long as they continue to gamble, sooner or later they will lose more to the casino, which is the so-called long bet will lose. Our results demonstrate the counter-intuitive phenomenon, that gamblers involved in long bets will lose but casinos always advertise their unprofitable circumstances. Here we expose the law of inevitability behind long bet will loss by theoretically and experimentally demystifying the profitable mystery behind casinos under two-armed antique Mills Futurity slot machine. The main results straightforwardly elucidate that all casino projects are seemingly a fair gamble but essentially unfair, i.e., the casino's win rate is greater than 50%. We anticipate our assay to be a starting point for studying the fairness of more sophisticated multi-armed Futurity bandits based on the mathematical tool. In application, a fairness study of the Futurity bandits not only exposes the fraud of casinos for gamblers but also discloses discount marketing, bundled sales, or other induced consumption tactics.
The Quantitative Finance Aspects of Automated Market Markers in DeFi
Authors: Stefan Loesch
Date: 2022-12-21 00:00:00

Automated Market Makers (AMMs) are a class of smart contracts on Ethereum and other blockchains that "make markets" autonomously. In other words, AMMs stand ready to trade with other market participants that interact with them, at the conditions determined by the AMM. In this this paper, which relies on the existing and growing corpus of literature available, we review and present the key mathematical and quantitative finance aspects that underpin their operations, including the interesting relationship between AMMs and derivatives pricing and hedging.
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences
Authors: Kaicheng Yang, Ruxuan Zhang, Hua Xu, Kai Gao
Date: 2022-11-12 00:00:00
Tasks: Multimodal Sentiment Analysis, Representation Learning, Sentiment Analysis

Inter-modal interaction plays an indispensable role in multimodal sentiment analysis. Due to different modalities sequences are usually non-alignment, how to integrate relevant information of each modality to learn fusion representations has been one of the central challenges in multimodal learning. In this paper, a Self-Adjusting Fusion Representation Learning Model (SA-FRLM) is proposed to learn robust crossmodal fusion representations directly from the unaligned text and audio sequences. Different from previous works, our model not only makes full use of the interaction between different modalities but also maximizes the protection of the unimodal characteristics. Specifically, we first employ a crossmodal alignment module to project different modalities features to the same dimension. The crossmodal collaboration attention is then adopted to model the inter-modal interaction between text and audio sequences and initialize the fusion representations. After that, as the core unit of the SA-FRLM, the crossmodal adjustment transformer is proposed to protect original unimodal characteristics. It can dynamically adapt the fusion representations by using single modal streams. We evaluate our approach on the public multimodal sentiment analysis datasets CMU-MOSI and CMU-MOSEI. The experiment results show that our model has significantly improved the performance of all the metrics on the unaligned text-audio sequences.
CatlNet: Learning Communication and Coordination Policies from CaTL+ Specifications
Authors: Wenliang Liu, Kevin Leahy, Zachary Serlin, Calin Belta
Date: 2022-11-30 00:00:00

In this paper, we propose a learning-based framework to simultaneously learn the communication and distributed control policies for a heterogeneous multi-agent system (MAS) under complex mission requirements from Capability Temporal Logic plus (CaTL+) specifications. Both policies are trained, implemented, and deployed using a novel neural network model called CatlNet. Taking advantage of the robustness measure of CaTL+, we train CatlNet centrally to maximize it where network parameters are shared among all agents, allowing CatlNet to scale to large teams easily. CatlNet can then be deployed distributedly. A plan repair algorithm is also introduced to guide CatlNet's training and improve both training efficiency and the overall performance of CatlNet. The CatlNet approach is tested in simulation and results show that, after training, CatlNet can steer the decentralized MAS system online to satisfy a CaTL+ specification with a high success rate.
A topological analysis of cointegrated data: a Z24 Bridge case study
Authors: Tristan Gowdridge, Elizabeth Cross, Nikolaos Dervilis, Keith Worden
Date: 2022-09-12 00:00:00
Tasks: Structural Health Monitoring, Time Series, Time Series Analysis, Topological Data Analysis

The paper studies the topological changes from before and after cointegration, for the natural frequencies of the Z24 Bridge. The second natural frequency is known to be nonlinear in temperature, and this will serve as the main focal point of this work. Cointegration is a method of normalising time series data with respect to one another - often strongly-correlated time series. Cointegration is used in this paper to remove effects from Environmental and Operational Variations, by cointegrating the first four natural frequencies for the Z24 Bridge data. The temperature effects on the natural frequency data are clearly visible within the data, and it is desirable, for the purposes of structural health monitoring, that these effects are removed. The univariate time series are embedded in higher-dimensional space, such that interesting topologies are formed. Topological data analysis is used to analyse the raw time series, and the cointegrated equivalents. A standard topological data analysis pipeline is enacted, where simplicial complexes are constructed from the embedded point clouds. Topological properties are then calculated from the simplicial complexes; such as the persistent homology. The persistent homology is then analysed, to determine the topological structure of all the time series.
Mediastinal Lymph Node Detection and Segmentation Using Deep Learning
Authors: Al-Akhir Nayan, Boonserm Kijsirikul, Yuji Iwahori
Date: 2022-11-24 00:00:00
Tasks: Computed Tomography (CT), Deep Learning

Automatic lymph node (LN) segmentation and detection for cancer staging are critical. In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal LNs. Despite its low contrast and variety in nodal size and form, LN segmentation remains a challenging task. Deep convolutional neural networks frequently segment items in medical photographs. Most state-of-the-art techniques destroy image's resolution through pooling and convolution. As a result, the models provide unsatisfactory results. Keeping the issues in mind, a well-established deep learning technique UNet was modified using bilinear interpolation and total generalized variation (TGV) based upsampling strategy to segment and detect mediastinal lymph nodes. The modified UNet maintains texture discontinuities, selects noisy areas, searches appropriate balance points through backpropagation, and recreates image resolution. Collecting CT image data from TCIA, 5-patients, and ELCAP public dataset, a dataset was prepared with the help of experienced medical experts. The UNet was trained using those datasets, and three different data combinations were utilized for testing. Utilizing the proposed approach, the model achieved 94.8% accuracy, 91.9% Jaccard, 94.1% recall, and 93.1% precision on COMBO_3. The performance was measured on different datasets and compared with state-of-the-art approaches. The UNet++ model with hybridized strategy performed better than others.
OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments
Authors: Naoya Yoshimura, Jaime Morales, Takuya Maekawa, Takahiro Hara
Date: 2022-12-10 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Unlike human daily activities, existing publicly available sensor datasets for work activity recognition in industrial domains are limited by difficulties in collecting realistic data as close collaboration with industrial sites is required. This also limits research on and development of methods for industrial applications. To address these challenges and contribute to research on machine recognition of work activities in industrial domains, in this study, we introduce a new large-scale dataset for packaging work recognition called OpenPack. OpenPack contains 53.8 hours of multimodal sensor data, including acceleration data, keypoints, depth images, and readings from IoT-enabled devices (e.g., handheld barcode scanners), collected from 16 distinct subjects with different levels of packaging work experience. We apply state-of-the-art human activity recognition techniques to the dataset and provide future directions of complex work activity recognition studies in the pervasive computing community based on the results. We believe that OpenPack will contribute to the sensor-based action/activity recognition community by providing challenging tasks. The OpenPack dataset is available at https://open-pack.github.io.
Production-Ready Face Re-Aging for Visual Effects
Authors: Gaspard Zoss (DisneyResearch|Studios), Prashanth Chandran (DisneyResearch|Studios/ETH Joint PhD), Eftychios Sifakis (DisneyResearch|Studios), Markus Gross (DisneyResearch|Studios/ETH Zurich), Paulo Gotardo (DisneyResearch|Studios), Derek Bradley (DisneyResearch|Studios)
Date: 2022-11-30 00:00:00
Proceeding: ACM SIGGRAPH Asia 2022 11
Tasks: 10-shot image generation

We demonstrate how the simple U-Net, surprisingly, allows us to advance the state of the art for re-aging real faces on video, with unprecedented temporal stability and preservation of facial identity across variable expressions, viewpoints, and lighting conditions.
Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach
Authors: Ameer Hamza Khan, Student Member, Shuai Li, and Xin Luo, Senior Member, IEEE
Date: 2020-07-07 00:00:00
Proceeding: IEEE Transactions on Industrial Informatics 2020 7

In this article, we present a metaheuristic-based control framework, called beetle antennae olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle avoidance into a single constrained optimization problem by introducing a penalty term into the objective function, which actively rewards the optimizer for avoiding the obstacles. One of the significant features of the proposed framework is the way that the penalty term is formulated following a straightforward principle: maximize the minimum distance between a manipulator and an obstacle. The distance calculations are based on Gilbert– Johnson–Keerthi algorithm, which calculates the distance between a manipulator and an obstacle by directly using their three-dimensional geometries, which also implies that our algorithm works for a manipulator and an arbitrarily shaped obstacle. Theoretical treatment proves the stability and convergence, and simulation results using an LBR IIWA seven-DOF manipulator are presented to analyze the performance of the proposed framework.
3D face reconstruction from single image with generative adversarial networks
Authors: Mehdi Malah;Mounir Hemam;Fayçal Abbas
Date: 2022-12-01 00:00:00
Proceeding: Journal of King Saud University - Computer and Information Sciences 2022 12
Tasks: 3D Face Reconstruction, 3D Reconstruction, Face Reconstruction

Traditional reconstruction techniques extract information from the object’s geometry or one or more 2D images. On the other hand, the limit of the existing methods is that they generate less precise objects. Thus the lack of robustness towards several face reconstruction problems, such as the position of the head, occlusion, noise, and lighting variation. Therefore, generative neural networks and graphical convolution networks have marked a significant evolution in the field of 3D reconstruction. This paper proposes a model for 3D face reconstruction from a single 2D image. Our model is composed of a generator and a discriminator based on convolutional graphic layers. Indeed, in order to generate a face mesh with expression, our idea is to use the landmarks associated with this image as input to the generator to reconstruct a face geometry with expression and improve the convergence rate. As a result, our model offers an accurate reconstruction of facial geometry with expression; thus, our model outperforms state-of-the-art methods through qualitative and quantitative comparison.