pwc-archive/papers-with-abstracts huggingface dataset:
Strategies for targeting chondrosarcomas in vivo and molecular dissection of oncogenic events in chondrosarcomas: is epigenetics the culprit?
Authors: Rédoane Daoudi
Date: 2023-07-30 00:00:00

It is obvious that both epigenetic and non-epigenetic actors contribute to tumorigenesis in chondrosarcomas and more generally in other cancers. Thus, the main altered pathways in chondrosarcomas are now well established and include both epigenetic and non-epigenetic pathways such as the PI3K-AKT signaling, EGFR overexpression, SPARC overexpression, c-myc overexpression, IHH/GLI1 axis, loss of Rb function, HIF1-alpha stabilization, IDH1 mutations, hypermethylation and SIRT1. This review aims to provide a detailed analysis of these pathways and highlights recurrent interactions between non-epigenetic and epigenetic actors in chondrosarcomas, raising the intriguing possibility of developing therapeutics targeting both epigenetic and non-epigenetic actors and supporting data from previous studies. Finally, we propose some strategies for targeting chondrosarcomas in vivo based on properties of this tumor.
A critical examination of crop-yield data for vegetables, maize and Tea for commercialized Sri Lankan biofilm biofertilizers
Authors: M. W. C. Dharma-wardana, Parakrama Waidyanatha, K. A. Renuka, D. Sumith de S. Abeysiriwardena, Buddhi Marambe
Date: 2023-07-30 00:00:00

With increasing global interest in microbial methods for agriculture, the commercialization of biofertilizers in Sri Lanka is of general interest. The use of a biofilm-biofertilizer (BFBF) commercialized in Sri Lanka is claimed to reduce chemical fertilizer (CF) usage by ~50 per cent while boosting harvest by 20-30 per cent. Many countries have explored the potential of biofilm biofertilizers, but have so far found mixed results. Here we review this BFBF commercialized in Sri Lanka and approved for nation-wide use there. We show in detail that the improved yields claimed for this BFBF fall within the uncertainties (error bars) of the harvest. Theoretical models that produce a seemingly reduced CF scenario with an "increase" in harvests, although this is in fact not so, are presented. While BFBF usage seems to improve soil quality in certain respects, the currently available BFBF promoted in Sri Lanka has negligible impact on crop yields. We also briefly consider the potential negative effects of large-scale adoption of microbial methods.
Separable mixing: the general formulation and a particular example focusing on mask efficiency
Authors: M. C. J. Bootsma, K. M. D. Chan, O. Diekmann, H. Inaba
Date: 2023-07-31 00:00:00

The aim of this short note is twofold. We formulate the general Kermack-McKendrick epidemic model incorporating static heterogeneity and show how it simplifies to a scalar Renewal Equation (RE) when separable mixing is assumed. A key feature is that all information about the heterogeneity is encoded in one nonlinear real valued function of a real variable. Inspired by work of R. Pastor-Satorras and C. Castellano, we next investigate mask efficiency and demonstrate that it is straightforward to rederive from the RE their main conclusion, that the best way to protect the population as a whole is to protect yourself. Thus we establish that this conclusion is robust, in the sense that it also holds outside the world of network models.
Generation of orchard and tree-child networks
Authors: Gabriel Cardona, Gerard Ribas, Joan Carles Pons
Date: 2023-07-30 00:00:00

Phylogenetic networks are an extension of phylogenetic trees that allow for the representation of reticulate evolution events. One of the classes of networks that has gained the attention of the scientific community over the last years is the class of orchard networks, that generalizes tree-child networks, one of the most studied classes of networks. In this paper we focus on the combinatorial and algorithmic problem of the generation of orchard networks, and also of tree-child networks. To this end, we use that these networks are defined as those that can be recovered by a reversing a certain reduction process. Then, we show how to choose a ``minimum'' reduction process among all that can be applied to a network, and hence we get a unique representation of the network that, in fact, can be given in terms of sequences of pairs of integers, whose length is related to the number of leaves and reticulations of the network. Therefore, the generation of networks is reduced to the generation of such sequences of pairs. Our main result is a recursive method for the efficient generation of all minimum sequences, and hence of all orchard (or tree-child) networks with a given number of leaves and reticulations. An implementation in C of the algorithms described in this paper, along with some computational experiments, can be downloaded from the public repository https://github.com/gerardet46/OrchardGenerator. Using this implementation, we have computed the number of orchard networks with at most 6 leaves and 8 reticulations.
American Passport options in an exponential Lévy model
Authors: Zakaria Marah
Date: 2023-07-31 00:00:00
Tasks: model

In this paper we examine the problem of valuing an exotic derivative known as the American passport option where the underlying is driven by a L\'evy process. The passport option is a call option on a trading account. We derive the pricing equation, using the dynamic programming principle, and prove that the option value is a viscosity solution of variational inequality. We also establish the comparison principle, which yields uniqueness and the convexity of the viscosity solution.
Option Smile Volatility and Implied Probabilities: Implications of Concavity in IV Curves
Authors: Darsh Kachhara, John K. E Markin, Astha Singh
Date: 2023-07-27 00:00:00

Earnings announcements (EADs) are corporate events that provide investors with fundamentally important information. The prospect of stock price rises may also contribute to EADs increased volatility. Using data on extremely short term options, we study that bimodality in the risk neutral distribution and concavity in the IV smiles are ubiquitous characteristics before an earnings announcement day. This study compares the returns between concave and non concave IV smiles to see if the concavity in the IV curve leads to any information about the risk in the market and showcases how investors hedge against extreme volatility during earnings announcements. In fact, our paper shows in the presence of concave IV smiles; investors pay a significant premium to hedge against the uncertainty caused by the forthcoming announcement.
Shifting Cryptocurrency Influence: A High-Resolution Network Analysis of Market Leaders
Authors: Arnav Hiray, Pratvi Shah, Vishwa Shah, Agam Shah, Sudheer Chava, Mukesh Tiwari
Date: 2023-07-31 00:00:00

Over the last decade, the cryptocurrency market has experienced unprecedented growth, emerging as a prominent financial market. As this market rapidly evolves, it necessitates re-evaluating which cryptocurrencies command the market and steer the direction of blockchain technology. We implement a network-based cryptocurrency market analysis to investigate this changing landscape. We use novel hourly-resolution data and Kendall's Tau correlation to explore the interconnectedness of the cryptocurrency market. We observed critical differences in the hierarchy of cryptocurrencies determined by our method compared to rankings derived from daily data and Pearson's correlation. This divergence emphasizes the potential information loss stemming from daily data aggregation and highlights the limitations of Pearson's correlation. Our findings show that in the early stages of this growth, Bitcoin held a leading role. However, during the 2021 bull run, the landscape changed drastically. We see that while Ethereum has emerged as the overall leader, it was FTT and its associated exchange, FTX, that greatly led to the increase at the beginning of the bull run. We also find that highly-influential cryptocurrencies are increasingly gaining a commanding influence over the market as time progresses, despite the growing number of cryptocurrencies making up the market.
A Common Shock Model for multidimensional electricity intraday price modelling with application to battery valuation
Authors: Thomas Deschatre, Xavier Warin
Date: 2023-07-31 00:00:00

In this paper, we propose a multidimensional statistical model of intraday electricity prices at the scale of the trading session, which allows all products to be simulated simultaneously. This model, based on Poisson measures and inspired by the Common Shock Poisson Model, reproduces the Samuelson effect (intensity and volatility increases as time to maturity decreases). It also reproduces the price correlation structure, highlighted here in the data, which decreases as two maturities move apart. This model has only three parameters that can be estimated using a moment method that we propose here. We demonstrate the usefulness of the model on a case of storage valuation by dynamic programming over a trading session.
Equilibria and incentives for illiquid auction markets
Authors: Joffrey Derchu, Dimitrios Kavvathas, Thibaut Mastrolia, Mathieu Rosenbaum
Date: 2023-07-28 00:00:00

We study a toy two-player game for periodic double auction markets to generate liquidity. The game has imperfect information, which allows us to link market spreads with signal strength. We characterize Nash equilibria in cases with or without incentives from the exchange. This enables us to derive new insights about price formation and incentives design. We show in particular that without any incentives, the market is inefficient and does not lead to any trade between market participants. We however prove that quadratic fees indexed on each players half spread leads to a transaction and we propose a quantitative value for the optimal fees that the exchange has to propose in this model to generate liquidity.
Traffic Flow Simulation for Autonomous Driving
Authors: Junfeng Li, Changqing Yan
Date: 2023-07-23 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making, Motion Planning

A traffic system is a random and complex large system, which is difficult to conduct repeated modelling and control research in a real traffic environment. With the development of automatic driving technology, the requirements for testing and evaluating the development of automatic driving technology are getting higher and higher, so the application of computer technology for traffic simulation has become a very effective technical means. Based on the micro-traffic flow modelling, this paper adopts the vehicle motion model based on cellular automata and the theory of bicycle intelligence to build the simulation environment of autonomous vehicle flow. The architecture of autonomous vehicles is generally divided into a perception system, decision system and control system. The perception system is generally divided into many subsystems, responsible for autonomous vehicle positioning, obstacle recognition, traffic signal detection and recognition and other tasks. Decision systems are typically divided into many subsystems that are responsible for tasks such as path planning, path planning, behavior selection, motion planning, and control. The control system is the basis of the selfdriving car, and each control system of the vehicle needs to be connected with the decision-making system through the bus, and can accurately control the acceleration degree, braking degree, steering amplitude, lighting control and other driving actions according to the bus instructions issued by the decision-making system, so as to achieve the autonomous driving of the vehicle.
Pyrus Base: An Open Source Python Framework for the RoboCup 2D Soccer Simulation
Authors: Nader Zare, Aref Sayareh, Omid Amini, Mahtab Sarvmaili, Arad Firouzkouhi, Stan Matwin, Amilcar Soares
Date: 2023-07-22 00:00:00

Soccer, also known as football in some parts of the world, involves two teams of eleven players whose objective is to score more goals than the opposing team. To simulate this game and attract scientists from all over the world to conduct research and participate in an annual computer-based soccer world cup, Soccer Simulation 2D (SS2D) was one of the leagues initiated in the RoboCup competition. In every SS2D game, two teams of 11 players and one coach connect to the RoboCup Soccer Simulation Server and compete against each other. Over the past few years, several C++ base codes have been employed to control agents' behavior and their communication with the server. Although C++ base codes have laid the foundation for the SS2D, developing them requires an advanced level of C++ programming. C++ language complexity is a limiting disadvantage of C++ base codes for all users, especially for beginners. To conquer the challenges of C++ base codes and provide a powerful baseline for developing machine learning concepts, we introduce Pyrus, the first Python base code for SS2D. Pyrus is developed to encourage researchers to efficiently develop their ideas and integrate machine learning algorithms into their teams. Pyrus base is open-source code, and it is publicly available under MIT License on GitHub
Contextual Emotion Recognition Using Transformer-Based Models
Authors: Aayush Devgan
Date: 2023-08-02 00:00:00
Proceeding: INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY 2023 8
Tasks: Emotion Recognition, Language Modelling, Sentiment Analysis

In order to increase the precision of emotion identification in text, this research suggests a context-aware emotion recognition system employing transformer models, especially BERT. The model is able to comprehend complex emotions and context-dependent expressions since it was trained on a broad, emotion-labeled dataset. On a benchmark dataset, its efficacy is assessed compared to conventional techniques and standard transformer models. The system is proficient at gathering contextual information, and the findings demonstrate a considerable improvement in emotion recognition accuracy. This study improves textual emotion identification, opening the door to applications like chatbots that can recognize emotions and systems for tracking mental health. It also identifies potential areas for further study in developing transformer models for context-sensitive NLP applications
Hybrid Approximate Nearest Neighbor Indexing and Search (HANNIS) for Large Descriptor Databases
Authors: M M Mahabubur Rahman, Jelena Tešić
Date: 2023-01-26 00:00:00
Proceeding: IEEE International Conference on Big Data (Big Data) 2023 1
Tasks: Retrieval

In this paper, we present a novel method for efficient and effective retrieval of similar deep descriptors. Our new hybrid method for indexing and searching for the approximate nearest neighbors in high-dimensional large deep-descriptor databases retrieves truly similar items in the database, even if the retrieval set is large. The proposed solution —- hybrid approximate nearest neighbor indexing and search (HANNIS) —- partitions the whole data space using the kmeans++ algorithm and then indexes each cluster using adapted hierarchical navigable graphs. This approach enables us to load items that are truly close to the incoming query at retrieval time. HANNIS outperforms all state-of-the-art methods in terms of recall at depths of up to 100 and offers consistent index loading and retrieval performance.
Validation of a Zero-Shot Learning Natural Language Processing Tool for Data Abstraction from Unstructured Healthcare Data
Authors: Basil Kaufmann, Dallin Busby, Chandan Krushna Das, Neeraja Tillu, Mani Menon, Ashutosh K. Tewari, Michael A. Gorin
Date: 2023-07-23 00:00:00
Tasks: Optical Character Recognition, Zero-Shot Learning

Objectives: To describe the development and validation of a zero-shot learning natural language processing (NLP) tool for abstracting data from unstructured text contained within PDF documents, such as those found within electronic health records. Materials and Methods: A data abstraction tool based on the GPT-3.5 model from OpenAI was developed and compared to three physician human abstractors in terms of time to task completion and accuracy for abstracting data on 14 unique variables from a set of 199 de-identified radical prostatectomy pathology reports. The reports were processed by the software tool in vectorized and scanned formats to establish the impact of optical character recognition on data abstraction. The tool was assessed for superiority for data abstraction speed and non-inferiority for accuracy. Results: The human abstractors required a mean of 101s per report for data abstraction, with times varying from 15 to 284 s. In comparison, the software tool required a mean of 12.8 s to process the vectorized reports and a mean of 15.8 to process the scanned reports (P < 0.001). The overall accuracies of the three human abstractors were 94.7%, 97.8%, and 96.4% for the combined set of 2786 datapoints. The software tool had an overall accuracy of 94.2% for the vectorized reports, proving to be non-inferior to the human abstractors at a margin of -10% ($\alpha$=0.025). The tool had a slightly lower accuracy of 88.7% using the scanned reports, proving to be non-inferiority to 2 out of 3 human abstractors. Conclusion: The developed zero-shot learning NLP tool affords researchers comparable levels of accuracy to that of human abstractors, with significant time savings benefits. Because of the lack of need for task-specific model training, the developed tool is highly generalizable and can be used for a wide variety of data abstraction tasks, even outside the field of medicine.
FLatten Transformer: Vision Transformer using Focused Linear Attention
Authors: Dongchen Han, Xuran Pan, Yizeng Han, Shiji Song, Gao Huang
Date: 2023-08-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Diversity

The quadratic computation complexity of self-attention has been a persistent challenge when applying Transformer models to vision tasks. Linear attention, on the other hand, offers a much more efficient alternative with its linear complexity by approximating the Softmax operation through carefully designed mapping functions. However, current linear attention approaches either suffer from significant performance degradation or introduce additional computation overhead from the mapping functions. In this paper, we propose a novel Focused Linear Attention module to achieve both high efficiency and expressiveness. Specifically, we first analyze the factors contributing to the performance degradation of linear attention from two perspectives: the focus ability and feature diversity. To overcome these limitations, we introduce a simple yet effective mapping function and an efficient rank restoration module to enhance the expressiveness of self-attention while maintaining low computation complexity. Extensive experiments show that our linear attention module is applicable to a variety of advanced vision Transformers, and achieves consistently improved performances on multiple benchmarks. Code is available at https://github.com/LeapLabTHU/FLatten-Transformer.
NormKD: Normalized Logits for Knowledge Distillation
Authors: Zhihao Chi, Tu Zheng, Hengjia Li, Zheng Yang, Boxi Wu, Binbin Lin, Deng Cai
Date: 2023-08-01 00:00:00
Tasks: image-classification, Image Classification, Knowledge Distillation

Logit based knowledge distillation gets less attention in recent years since feature based methods perform better in most cases. Nevertheless, we find it still has untapped potential when we re-investigate the temperature, which is a crucial hyper-parameter to soften the logit outputs. For most of the previous works, it was set as a fixed value for the entire distillation procedure. However, as the logits from different samples are distributed quite variously, it is not feasible to soften all of them to an equal degree by just a single temperature, which may make the previous work transfer the knowledge of each sample inadequately. In this paper, we restudy the hyper-parameter temperature and figure out its incapability to distill the knowledge from each sample sufficiently when it is a single value. To address this issue, we propose Normalized Knowledge Distillation (NormKD), with the purpose of customizing the temperature for each sample according to the characteristic of the sample's logit distribution. Compared to the vanilla KD, NormKD barely has extra computation or storage cost but performs significantly better on CIRAR-100 and ImageNet for image classification. Furthermore, NormKD can be easily applied to the other logit based methods and achieve better performance which can be closer to or even better than the feature based method.
AnyLoc: Towards Universal Visual Place Recognition
Authors: Nikhil Keetha, Avneesh Mishra, Jay Karhade, Krishna Murthy Jatavallabhula, Sebastian Scherer, Madhava Krishna, Sourav Garg
Date: 2023-08-01 00:00:00
Tasks: Image Retrieval, Visual Place Recognition

Visual Place Recognition (VPR) is vital for robot localization. To date, the most performant VPR approaches are environment- and task-specific: while they exhibit strong performance in structured environments (predominantly urban driving), their performance degrades severely in unstructured environments, rendering most approaches brittle to robust real-world deployment. In this work, we develop a universal solution to VPR -- a technique that works across a broad range of structured and unstructured environments (urban, outdoors, indoors, aerial, underwater, and subterranean environments) without any re-training or fine-tuning. We demonstrate that general-purpose feature representations derived from off-the-shelf self-supervised models with no VPR-specific training are the right substrate upon which to build such a universal VPR solution. Combining these derived features with unsupervised feature aggregation enables our suite of methods, AnyLoc, to achieve up to 4X significantly higher performance than existing approaches. We further obtain a 6% improvement in performance by characterizing the semantic properties of these features, uncovering unique domains which encapsulate datasets from similar environments. Our detailed experiments and analysis lay a foundation for building VPR solutions that may be deployed anywhere, anytime, and across anyview. We encourage the readers to explore our project page and interactive demos: https://anyloc.github.io/.
Applicability of scaling laws to vision encoding models
Authors: Takuya Matsuyama, Kota S Sasaki, Shinji Nishimoto
Date: 2023-08-01 00:00:00
Tasks: Prediction

In this paper, we investigated how to build a high-performance vision encoding model to predict brain activity as part of our participation in the Algonauts Project 2023 Challenge. The challenge provided brain activity recorded by functional MRI (fMRI) while participants viewed images. Several vision models with parameter sizes ranging from 86M to 4.3B were used to build predictive models. To build highly accurate models, we focused our analysis on two main aspects: (1) How does the sample size of the fMRI training set change the prediction accuracy? (2) How does the prediction accuracy across the visual cortex vary with the parameter size of the vision models? The results show that as the sample size used during training increases, the prediction accuracy improves according to the scaling law. Similarly, we found that as the parameter size of the vision models increases, the prediction accuracy improves according to the scaling law. These results suggest that increasing the sample size of the fMRI training set and the parameter size of visual models may contribute to more accurate visual models of the brain and lead to a better understanding of visual neuroscience.