pwc-archive/papers-with-abstracts huggingface dataset:
On the Pricing of Storable Commodities
Authors: Dorje C. Brody, Lane P. Hughston, Xun Yang
Date: 2013-07-21 00:00:00

This paper introduces an information-based model for the pricing of storable commodities such as crude oil and natural gas. The model uses the concept of market information about future supply and demand as a basis for valuation. Physical ownership of a commodity is taken to provide a stream of convenience dividends equivalent to a continuous cash flow. The market filtration is assumed to be generated jointly by (i) current and past levels of the dividend rate, and (ii) partial information concerning the future of the dividend flow. The price of a commodity is the expectation under a suitable pricing measure of the totality of the discounted risk-adjusted future convenience dividend, conditional on the information provided by the market filtration. In the situation where the dividend rate is modelled by an Ornstein-Uhlenbeck process, the prices of options on commodities can be derived in closed form. The approach that we present can be applied to other assets that yield potentially negative effective cash flows, such as real estate, factories, refineries, mines, and power generating plants.
Estimation of inter-sector asset correlations
Authors: Christian Meyer
Date: 2021-11-30 00:00:00

Asset correlations are an intuitive and therefore popular way to incorporate event dependence into event risk, e.g., default risk, modeling. In this paper we study the case of estimation of inter-sector asset correlations by separation of cross-sectional dimension and time dimension.
small: A Programmatic Nanostructure Design and Modelling Environment
Authors: Durham Smith, Grigory Tikhomirov
Date: 2021-11-30 00:00:00

Structural DNA nanotechnology has advanced to the extent that extremely complex structures can be designed. Much of this advancement has been due to the development of automated DNA design and simulation tools. Typically, the tools (e.g. NUPAK, cadnano, OxDNA) are created for a specific task. Ideally, there would be an environment that can integrate all such DNA tools, also with non-DNA tools - for example for modelling electromagnetic field along a zero-mode waveguide made of gold nanoparticles organized on a DNA breadboard. Such an environment would streamline design in DNA nanotechnology and enable applying DNA nanotechnology principles to construct high performance materials and devices from non-DNA components. Here we present small a programmatic tool that is a step towards building such an environment for designing arbitrary nanostructures. In particular we showcase how small has been used to create an integrated computational materials engineering (ICME) framework for DNA nanotechnology, allowing the hierarchical design, simulation and visualization of arbitrary DNA nanostructures. Furthermore we demonstrate the design and modeling of the mode profiles and band structure of hybrid DNA-nanoparticle materials through the integration of small with Maxwell solvers.
EndHiC: assemble large contigs into chromosomal-level scaffolds using the Hi-C links from contig ends
Authors: Sen Wang, Hengchao Wang, Fan Jiang, Anqi Wang, Hangwei Liu, Hanbo Zhao, Boyuan Yang, Dong Xu, Yan Zhang, Wei Fan
Date: 2021-11-30 00:00:00

Motivation: The application of PacBio HiFi and ultra-long ONT reads have achieved huge progress in the contig-level assembly, but it is still challenging to assemble large contigs into chromosomes with available Hi-C scaffolding software, which all compute the contact value between contigs using the Hi-C links from the whole contig regions. As the Hi-C links of two adjacent contigs concentrate only at the neighbor ends of the contigs, larger contig size will reduce the power to differentiate adjacent (signal) and non-adjacent (noise) contig linkages, leading to a higher rate of mis-assembly. Results: We present a software package EndHiC, which is suitable to assemble large contigs (> 1-Mb) into chromosomal-level scaffolds, using Hi-C links from only the contig end regions instead of the whole contig regions. Benefiting from the increased signal to noise ratio, EndHiC achieves much higher scaffolding accuracy compared to existing software LACHESIS, ALLHiC, and 3D-DNA. Moreover, EndHiC has few parameters, runs 10-1000 times faster than existing software, needs trivial memory, provides robustness evaluation, and allows graphic viewing of the scaffold results. The high scaffolding accuracy and user-friendly interface of EndHiC, liberate the users from labor-intensive manual checks and revision works. Availability and implementation: EndHiC is written in Perl, and is freely available at https://github.com/fanagislab/EndHiC. Contact: fanwei@caas.cn and milrazhang@163.com Supplementary information: Supplementary data are available at Bioinformatics online.
Parsimony and the rank of a flattening matrix
Authors: Jandre Snyman, Colin Fox, David Bryant
Date: 2021-11-29 00:00:00

The standard models of sequence evolution on a tree determine probabilities for every character or site pattern. A flattening is an arrangement of these probabilities into a matrix, with rows corresponding to all possible site patterns for one set $A$ of taxa and columns corresponding to all site patterns for another set $B$ of taxa. Flattenings have been used to prove difficult results relating to phylogenetic invariants and consistency and also form the basis of several methods of phylogenetic inference. We prove that the rank of the flattening equals $r^{\ell_T(A|B)}$, where $r$ is the number of states and $\ell_T(A|B)$ is the parsimony length of the binary character separating $A$ and $B$. This result corrects an earlier published formula and opens up new applications for old parsimony theorems. Since completing this work, we have learnt that an equivalent result has been proved much earlier by Casanellas and Fern\'andez-S\'anchez, using a different proof strategy.
A universal framework to efficiently share material and process resources in the DNA construction world
Authors: Hideto Mori, Nozomu Yachie
Date: 2021-11-30 00:00:00

DNA constructs and their annotated sequence maps have been rapidly accumulating with the advancement of DNA cloning, synthesis, and assembly methods. Such a resource has the potential to be optimally utilized in an autonomous DNA building platform. However, most DNA design processes today remain manually operated with the assistance of graphical user interface (GUI) software. Furthermore, as seen commonly in the life sciences, reproducibility of DNA construction process descriptions is usually not guaranteed, and utilization of previously developed materials and protocols is not appropriately credited. Here, we developed an open-source process description and resource sharing framework QUEEN (a framework to generate quinable and efficiently editable nucleotide sequence resources) to resolve these issues in building DNA. QUEEN enables the flexible design of new DNA by using existing DNA resource files and recoding the construction process in an output file (GenBank file format). The GenBank files generated by QUEEN are able to regenerate the process codes that perfectly clone themselves and bequeath the design history to successive DNA constructs that recycle their partial resources. QUEEN-generated GenBank files are compatible with the existing DNA repository services and software. We propose QUEEN as a solution to start significantly advancing our material and protocol sharing of DNA resources.
Improved Method of Stock Trading under Reinforcement Learning Based on DRQN and Sentiment Indicators ARBR
Authors: Peng Zhou, Jingling Tang
Date: 2021-11-19 00:00:00

With the application of artificial intelligence in the financial field, quantitative trading is considered to be profitable. Based on this, this paper proposes an improved deep recurrent DRQN-ARBR model because the existing quantitative trading model ignores the impact of irrational investor behavior on the market, making the application effect poor in an environment where the stock market in China is non-efficiency. By changing the fully connected layer in the original model to the LSTM layer and using the emotion indicator ARBR to construct a trading strategy, this model solves the problems of the traditional DQN model with limited memory for empirical data storage and the impact of observable Markov properties on performance. At the same time, this paper also improved the shortcomings of the original model with fewer stock states and chose more technical indicators as the input values of the model. The experimental results show that the DRQN-ARBR algorithm proposed in this paper can significantly improve the performance of reinforcement learning in stock trading.
Prediction of Fund Net Value Based on ARIMA-LSTM Hybrid Model
Authors: Peng Zhou, Fangyi Li
Date: 2021-11-19 00:00:00
Tasks: Prediction

The net value of the fund is affected by performance and market, and the researchers try to quantify these effects to predict the future net value by establishing different models. The current prediction models usually can only reflect the linear variation law, poorly handled or selectively ignore their nonlinear characteristics, so the prediction results are usually less accurate. This paper uses a fund prediction method based on the ARIMA-LSTM hybrid model. After preprocessing the historical data, the first filter out the linear data characteristics with the ARIMA model, then pass the data to the LSTM model to extract the nonlinear characteristic by residual, and finally superposition the respective prediction values of the two models to obtain the prediction results of the hybrid model. Empirically shows that the methods in the paper are more accurate and applicable than traditional fund prediction methods.
An Improved Reinforcement Learning Model Based on Sentiment Analysis
Authors: Yizhuo Li, Peng Zhou, Fangyi Li, Xiao Yang
Date: 2021-11-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentiment Analysis

With the development of artificial intelligence technology, quantitative trading systems represented by reinforcement learning have emerged in the stock trading market. The authors combined the deep Q network in reinforcement learning with the sentiment quantitative indicator ARBR to build a high-frequency stock trading model for the share market. To improve the performance of the model, the PCA algorithm is used to reduce the dimensionality feature vector while incorporating the influence of market sentiment on the long-short power into the spatial state of the trading model and uses the LSTM layer to replace the fully connected layer to solve the traditional DQN model due to limited empirical data storage. Through the use of cumulative income, Sharpe ratio to evaluate the performance of the model and the use of double moving averages and other strategies for comparison. The results show that the improved model proposed by authors is far superior to the comparison model in terms of income, achieving a maximum annualized rate of return of 54.5%, which is proven to be able to increase reinforcement learning performance significantly in stock trading.
Is Bitcoin really a currency? A viewpoint of a stochastic volatility model
Authors: Noriyuki Kunimoto, Kazuhiko Kakamu
Date: 2021-11-10 00:00:00

Using the asymmetric stochastic volatility model, this study investigates the day-of-the-week and holiday effects on the returns and volatility of Bitcoin from January 1, 2013 to August 31, 2019; in this context, we also discuss the characteristics of Bitcoin as a financial asset. The results of the estimation are threefold. First, the finding shows a small day-of-the week effect in volatility on Saturday and Sunday than in the rest of the week. Second, although the holiday effects are examined in active trading countries, namely Japan, China, Germany, and the United States, the positive post-holiday effect on the returns and weak positive pre-holiday effect on the volatility are only observed in the United States. Finally, the asymmetry effect is not observed. A comparison of Bitcoin to several assets such as stock, currency, and gold shows Bitcoin's positioning between stock, currency, and gold in relation to the week and holiday effects, its reaction to federal funds and medium of exchange characteristics, and the lack of asymmetry effect.
A practical guide to stochastic simulations of reaction-diffusion processes
Authors: Radek Erban, Jonathan Chapman, Philip Maini
Date: 2007-04-15 00:00:00

A practical introduction to stochastic modelling of reaction-diffusion processes is presented. No prior knowledge of stochastic simulations is assumed. The methods are explained using illustrative examples. The article starts with the classical Gillespie algorithm for the stochastic modelling of chemical reactions. Then stochastic algorithms for modelling molecular diffusion are given. Finally, basic stochastic reaction-diffusion methods are presented. The connections between stochastic simulations and deterministic models are explained and basic mathematical tools (e.g. chemical master equation) are presented. The article concludes with an overview of more advanced methods and problems.
On Uncertainties in Cross-Correlation Lags and the Reality of Wavelength-Dependent Continuum Lags in Active Galactic Nuclei
Authors: Bradley M. Peterson, Ignaz Wanders, Keith Horne, Stefan Collier, Tal Alexander, Shai Kaspi, Dan Maoz
Date: 1998-02-09 00:00:00

We describe a model-independent method of assessing the uncertainties in cross-correlation lags determined from AGN light curves, and use this method to investigate the reality of lags between UV and optical continuum variations in well-studied AGNs. Our results confirm the existence of such lags in NGC 7469. We find that the continuum variations at 1825 A, 4845 A, and 6962 A follow those at 1315A by 0.22^{+0.12}_{-0.13} days, 1.25^{+0.48}_{-0.35} days, and 1.84^{+0.93}_{-0.94} days, respectively, based on the centroids of the cross-correlation functions; the error intervals quoted correspond to 68% confidence levels, and each of these lags is greater than zero at no less than 97% confidence. We do not find statistically significant interband continuum lags in NGC 5548, NGC 3783, or Fairall 9. Wavelength-dependent continuum lags may be marginally detected in the case of NGC 4151. However, on the basis of theoretical considerations, wavelength-dependent continuum lags in sources other than NGC 7469 are not expected to have been detectable in previous experiments. We also confirm the existence of a statistically significant lag between X-ray and UV continuum variations in the blazar PKS 2155-304.
ACCURATE METHOD OF TEMPORAL-SHIFT ESTIMATION FOR 3D VIDEO
Authors: Aleksandr Ploshkin, Dmitriy Vatolin
Date: 2018-06-03 00:00:00
Proceeding: 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) 2018 6
Tasks: Video Alignment, Video Synchronization

Video synchronization is a fundamental computer-vision task that is necessary for a wide range of applications. A 3D video involves two streams, which show the scene from different angles concurrently, but many cases exhibit desynchronization between them. This paper investigates the problem of synchronizing the left and right stereoscopic views. We assume the temporal shift (time difference) and geometric distortion between the two streams are constant throughout each scene. We propose a temporal-shift estimation method with subframe accuracy based on a block-matching algorithm.
Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation
Authors: Martin Kolařík, Radim Burget, Václav Uher, Kamil Říha, Malay Kishore Dutta
Date: 2019-01-25 00:00:00
Proceeding: Applied Sciences 2019 1
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, UNET Segmentation

The 3D image segmentation is the process of partitioning a digital 3D volumes into multiple segments. This paper presents a fully automatic method for high resolution 3D volumetric segmentation of medical image data using modern supervised deep learning approach. We introduce 3D Dense-U-Net neural network architecture implementing densely connected layers. It has been optimized for graphic process unit accelerated high resolution image processing on currently available hardware (Nvidia GTX 1080ti). The method has been evaluated on MRI brain 3D volumetric dataset and CT thoracic scan dataset for spine segmentation. In contrast with many previous methods, our approach is capable of precise segmentation of the input image data in the original resolution, without any pre-processing of the input image. It can process image data in 3D and has achieved accuracy of 99.72% on MRI brain dataset, which outperformed results achieved by human expert. On lumbar and thoracic vertebrae CT dataset it has achieved the accuracy of 99.80%. The architecture proposed in this paper can also be easily applied to any task already using U-Net network as a segmentation algorithm to enhance its results.
Shot-by-Shot Movie Version Comparison
Authors: Ivan Molodetskikh, Dmitriy Vatolin
Date: 2018-12-01 00:00:00
Proceeding: World of Technique of Cinema 2018 12
Tasks: Video Alignment

Some movies are released in two or more versions. One of the frequent causes is shrinking the movie for theatrical showcase with subsequent release of both the theatrical (shrunk) cut and the director’s (original, extended) cut. Automatic editing difference detection is complicated by potential changes to color gamut, aspect ratio, reduced or increased length of individual scenes, object addition or removal. These difficulties make it impossible to directly apply existing approaches to editing map construction. We propose an algorithm for fully automatic construction of an editing map of two movie versions.
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Authors: Yangsibo Huang, Samyak Gupta, Zhao Song, Kai Li, Sanjeev Arora
Date: 2021-11-30 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Federated Learning

Gradient inversion attack (or input recovery from gradient) is an emerging threat to the security and privacy preservation of Federated learning, whereby malicious eavesdroppers or participants in the protocol can recover (partially) the clients' private data. This paper evaluates existing attacks and defenses. We find that some attacks make strong assumptions about the setup. Relaxing such assumptions can substantially weaken these attacks. We then evaluate the benefits of three proposed defense mechanisms against gradient inversion attacks. We show the trade-offs of privacy leakage and data utility of these defense methods, and find that combining them in an appropriate manner makes the attack less effective, even under the original strong assumptions. We also estimate the computation cost of end-to-end recovery of a single image under each evaluated defense. Our findings suggest that the state-of-the-art attacks can currently be defended against with minor data utility loss, as summarized in a list of potential strategies. Our code is available at: https://github.com/Princeton-SysML/GradAttack.
Semi-supervised music emotion recognition using noisy student training and harmonic pitch class profiles
Authors: Hao Hao Tan
Date: 2021-12-01 00:00:00
Tasks: Emotion Recognition, image-classification, Image Classification, Music Emotion Recognition

We present Mirable's submission to the 2021 Emotions and Themes in Music challenge. In this work, we intend to address the question: can we leverage semi-supervised learning techniques on music emotion recognition? With that, we experiment with noisy student training, which has improved model performance in the image classification domain. As the noisy student method requires a strong teacher model, we further delve into the factors including (i) input training length and (ii) complementary music representations to further boost the performance of the teacher model. For (i), we find that models trained with short input length perform better in PR-AUC, whereas those trained with long input length perform better in ROC-AUC. For (ii), we find that using harmonic pitch class profiles (HPCP) consistently improve tagging performance, which suggests that harmonic representation is useful for music emotion tagging. Finally, we find that noisy student method only improves tagging results for the case of long training length. Additionally, we find that ensembling representations trained with different training lengths can improve tagging results significantly, which suggest a possible direction to explore incorporating multiple temporal resolutions in the network architecture for future work.
Molecular Contrastive Learning with Chemical Element Knowledge Graph
Authors: Yin Fang, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan, Huajun Chen
Date: 2021-12-01 00:00:00
Tasks: Contrastive Learning, Drug Design, Molecular Property Prediction, molecular representation, Property Prediction, Representation Learning

Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive learning is a promising paradigm as it utilizes self-supervision signals and has no requirements for human annotations. However, prior works fail to incorporate fundamental domain knowledge into graph semantics and thus ignore the correlations between atoms that have common attributes but are not directly connected by bonds. To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning. KCL framework consists of three modules. The first module, knowledge-guided graph augmentation, augments the original molecular graph based on the Chemical Element KG. The second module, knowledge-aware graph representation, extracts molecular representations with a common graph encoder for the original molecular graph and a Knowledge-aware Message Passing Neural Network (KMPNN) to encode complex information in the augmented molecular graph. The final module is a contrastive objective, where we maximize agreement between these two views of molecular graphs. Extensive experiments demonstrated that KCL obtained superior performances against state-of-the-art baselines on eight molecular datasets. Visualization experiments properly interpret what KCL has learned from atoms and attributes in the augmented molecular graphs. Our codes and data are available at https://github.com/ZJU-Fangyin/KCL.
MonoScene: Monocular 3D Semantic Scene Completion
Authors: Anh-Quan Cao, Raoul de Charette
Date: 2021-12-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Reconstruction, 3D Scene Reconstruction, 3D Semantic Scene Completion, 3D Semantic Scene Completion from a single RGB image, Single-View 3D Reconstruction

MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense geometry and semantics of a scene are inferred from a single monocular RGB image. Different from the SSC literature, relying on 2.5 or 3D input, we solve the complex problem of 2D to 3D scene reconstruction while jointly inferring its semantics. Our framework relies on successive 2D and 3D UNets bridged by a novel 2D-3D features projection inspiring from optics and introduces a 3D context relation prior to enforce spatio-semantic consistency. Along with architectural contributions, we introduce novel global scene and local frustums losses. Experiments show we outperform the literature on all metrics and datasets while hallucinating plausible scenery even beyond the camera field of view. Our code and trained models are available at https://github.com/cv-rits/MonoScene.