pwc-archive/papers-with-abstracts huggingface dataset:
Tensor-Based Channel Estimation and Data-Aided Tracking in IRS-Assisted MIMO Systems
Authors: Kenneth B. A. Benicio, André L. F. de Almeida, Bruno Sokal, Fazal-E-Asim, Behrooz Makki, Gábor Fodor
Date: 2023-05-17 00:00:00

This letter proposes a model for symbol detection in the uplink of IRS-assisted networks in the presence of channel aging. During the first stage, we model the received pilot signal as a tensor, which serves as a basis for both estimating the channel and configuring the IRS. In the second stage, the proposed tensor approach tracks the aging process to detect and estimate the transmitted data symbols. Our evaluations show that our proposed channel and symbol estimation schemes improve the performance of IRS-assisted systems in terms of the achieved bit error rate and mean squared error of the received data, compared to state of the art schemes.
Extreme ATM skew in a local volatility model with discontinuity: joint density approach
Authors: Alexander Gairat, Vadim Shcherbakov
Date: 2023-05-18 00:00:00

This paper concerns a local volatility model in which volatility takes two possible values, and the specific value depends on whether the underlying price is above or below a given threshold value. The model is known, and a number of results have been obtained for it. In particular, option pricing formulas and a power law behaviour of the implied volatility skew have been established in the case when the threshold is taken at the money. In this paper we derive an alternative representation of option pricing formulas. In addition, we obtain an approximation of option prices by the corresponding Black-Scholes prices. Using this approximation streamlines obtaining the aforementioned behaviour of the skew. Our approach is based on the natural relationship of the model with Skew Brownian motion and consists of the systematic use of the joint distribution of this stochastic process and some of its functionals.
Option pricing under jump diffusion model
Authors: Qian Li, Li Wang
Date: 2023-05-18 00:00:00
Tasks: model

We provide an European option pricing formula written in the form of an infinite series of Black Scholes type terms under double Levy jumps model, where both the interest rate and underlying price are driven by Levy process. The series solution converges with a radius of convergence, and it is complemented by some numerical experiments to demonstrate its speed of convergence.
Knowledge-based Integration of Multi-Omic Datasets with Anansi: Annotation-based Analysis of Specific Interactions
Authors: Thomaz F. S. Bastiaanssen, Thomas P. Quinn, John F. Cryan
Date: 2023-05-18 00:00:00
Tasks: All

Motivation: Studies including more than one type of 'omics data sets are becoming more prevalent. Integrating these data sets can be a way to solidify findings and even to make new discoveries. However, integrating multi-omics data sets is challenging. Typically, data sets are integrated by performing an all-vs-all correlation analysis, where each feature of the first data set is correlated to each feature of the second data set. However, all-vs-all association testing produces unstructured results that are hard to interpret, and involves potentially unnecessary hypothesis testing that reduces statistical power due to false discovery rate (FDR) adjustment. Implementation: Here, we present the anansi framework, and accompanying R package, as a way to improve upon all-vs-all association analysis. We take a knowledge-based approach where external databases like KEGG are used to constrain the all-vs-all association hypothesis space, only considering pairwise associations that are a priori known to occur. This produces structured results that are easier to interpret, and increases statistical power by skipping unnecessary hypothesis tests. In this paper, we present the anansi framework and demonstrate its application to learn metabolite-function interactions in the context of host-microbe interactions. We further extend our framework beyond pairwise association testing to differential association testing, and show how anansi can be used to identify associations that differ in strength or degree based on sample covariates such as case/control status. Availability: https://github.com/thomazbastiaanssen/anansi
Angle Basis: a Generative Model and Decomposition for Functional Connectivity
Authors: Anton Orlichenko, Gang Qu, Ziyu Zhou, Zhengming Ding, Yu-Ping Wang
Date: 2023-05-17 00:00:00
Tasks: Functional Connectivity

Functional connectivity (FC) is one of the most common inputs to fMRI-based predictive models, due to a combination of its simplicity and robustness. However, there may be a lack of theoretical models for the generation of FC. In this work, we present a straightforward decomposition of FC into a set of basis states of sine waves with an additional jitter component. We show that the decomposition matches the predictive ability of FC after including 5-10 bases. We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%. Additionally, we find the residual can be used for subject fingerprinting, with 97.3% same-subject, different-scan identifiability, compared to 62.5% for FC. Unlike PCA or Factor Analysis methods, our method does not require knowledge of a population to perform its decomposition; a single subject is enough. Our decomposition of FC into two equally-predictive components may lead to a novel appreciation of group differences in patient populations. Additionally, we generate synthetic patient FC based on user-specified characteristics such as age, sex, and disease diagnosis. By creating synthetic datasets or augmentations we may reduce the high financial burden associated with fMRI data acquisition.
Non-parametric cumulants approach for outlier detection of multivariate financial data
Authors: Francesco Cesarone, Rosella Giacometti, Jacopo Maria Ricci
Date: 2023-05-18 00:00:00
Tasks: Outlier Detection

In this paper, we propose an outlier detection algorithm for multivariate data based on their projections on the directions that maximize the Cumulant Generating Function (CGF). We prove that CGF is a convex function, and we characterize the CGF maximization problem on the unit n-circle as a concave minimization problem. Then, we show that the CGF maximization approach can be interpreted as an extension of the standard principal component technique. Therefore, for validation and testing, we provide a thorough comparison of our methodology with two other projection-based approaches both on artificial and real-world financial data. Finally, we apply our method as an early detector for financial crises.
A Short Note on Setting Swap Parameters
Authors: Nihar Shah, Lucas Baker, Suraj Srinivasan, Alex Toberoff
Date: 2023-05-18 00:00:00

This short note illustrates the theoretical solution to a trader determining how to optimally swap her wealth into a target asset through on-chain operations. It offers the framework to solve optimal slippage parameters and optimal trade size.
Learning the Visualness of Text Using Large Vision-Language Models
Authors: Gaurav Verma, Ryan A. Rossi, Christopher Tensmeyer, Jiuxiang Gu, Ani Nenkova
Date: 2023-05-11 00:00:00
Tasks: Contrastive Learning, Image Generation, Image Retrieval, Retrieval, Text to Image Generation, Text-to-Image Generation

Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will enable text-to-image retrieval and generation models to augment text with relevant images. This is particularly challenging with long-form text as text-to-image generation and retrieval models are often triggered for text that is designed to be explicitly visual in nature, whereas long-form text could contain many non-visual sentences. To this end, we curate a dataset of 3,620 English sentences and their visualness scores provided by multiple human annotators. We also propose a fine-tuning strategy that adapts large vision-language models like CLIP by modifying the model's contrastive learning objective to map text identified as non-visual to a common NULL image while matching visual text to their corresponding images in the document. We evaluate the proposed approach on its ability to (i) classify visual and non-visual text accurately, and (ii) attend over words that are identified as visual in psycholinguistic studies. Empirical evaluation indicates that our approach performs better than several heuristics and baseline models for the proposed task. Furthermore, to highlight the importance of modeling the visualness of text, we conduct qualitative analyses of text-to-image generation systems like DALL-E. Project webpage: https://gaurav22verma.github.io/text-visualness/
Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions
Authors: Gokul Yenduri, Ramalingam M, Chemmalar Selvi G, Supriya Y, Gautam Srivastava, Praveen Kumar Reddy Maddikunta, Deepti Raj G, Rutvij H Jhaveri, Prabadevi B, Weizheng Wang, Athanasios V. Vasilakos, Thippa Reddy Gadekallu
Date: 2023-05-11 00:00:00

The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, enabling technologies, their impact on various applications, emerging challenges, and potential solutions.
SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues
Authors: Jaewook Lee, Andrew Lan
Date: 2023-05-11 00:00:00
Tasks: Retrieval, Scheduling

In second language vocabulary learning, existing works have primarily focused on either the learning interface or scheduling personalized retrieval practices to maximize memory retention. However, the learning content, i.e., the information presented on flashcards, has mostly remained constant. Keyword mnemonic is a notable learning strategy that relates new vocabulary to existing knowledge by building an acoustic and imagery link using a keyword that sounds alike. Beyond that, producing verbal and visual cues associated with the keyword to facilitate building these links requires a manual process and is not scalable. In this paper, we explore an opportunity to use large language models to automatically generate verbal and visual cues for keyword mnemonics. Our approach, an end-to-end pipeline for auto-generating verbal and visual cues, can automatically generate highly memorable cues. We investigate the effectiveness of our approach via a human participant experiment by comparing it with manually generated cues.
Bits of Grass: Does GPT already know how to write like Whitman?
Authors: Piotr Sawicki, Marek Grzes, Fabricio Goes, Dan Brown, Max Peeperkorn, Aisha Khatun
Date: 2023-05-10 00:00:00

This study examines the ability of GPT-3.5, GPT-3.5-turbo (ChatGPT) and GPT-4 models to generate poems in the style of specific authors using zero-shot and many-shot prompts (which use the maximum context length of 8192 tokens). We assess the performance of models that are not fine-tuned for generating poetry in the style of specific authors, via automated evaluation. Our findings indicate that without fine-tuning, even when provided with the maximum number of 17 poem examples (8192 tokens) in the prompt, these models do not generate poetry in the desired style.
ORKG-Leaderboards: A Systematic Workflow for Mining Leaderboards as a Knowledge Graph
Authors: Salomon Kabongo, Jennifer D'Souza, Sören Auer
Date: 2023-05-10 00:00:00

The purpose of this work is to describe the Orkg-Leaderboard software designed to extract leaderboards defined as Task-Dataset-Metric tuples automatically from large collections of empirical research papers in Artificial Intelligence (AI). The software can support both the main workflows of scholarly publishing, viz. as LaTeX files or as PDF files. Furthermore, the system is integrated with the Open Research Knowledge Graph (ORKG) platform, which fosters the machine-actionable publishing of scholarly findings. Thus the system output, when integrated within the ORKG's supported Semantic Web infrastructure of representing machine-actionable 'resources' on the Web, enables: 1) broadly, the integration of empirical results of researchers across the world, thus enabling transparency in empirical research with the potential to also being complete contingent on the underlying data source(s) of publications; and 2) specifically, enables researchers to track the progress in AI with an overview of the state-of-the-art (SOTA) across the most common AI tasks and their corresponding datasets via dynamic ORKG frontend views leveraging tables and visualization charts over the machine-actionable data. Our best model achieves performances above 90% F1 on the \textit{leaderboard} extraction task, thus proving Orkg-Leaderboards a practically viable tool for real-world usage. Going forward, in a sense, Orkg-Leaderboards transforms the leaderboard extraction task to an automated digitalization task, which has been, for a long time in the community, a crowdsourced endeavor.
Model-Contrastive Federated Domain Adaptation
Authors: Chang'an Yi, Haotian Chen, Yonghui Xu, Yifan Zhang
Date: 2023-05-07 00:00:00
Tasks: Contrastive Learning, Domain Adaptation, model, Transfer Learning

Federated domain adaptation (FDA) aims to collaboratively transfer knowledge from source clients (domains) to the related but different target client, without communicating the local data of any client. Moreover, the source clients have different data distributions, leading to extremely challenging in knowledge transfer. Despite the recent progress in FDA, we empirically find that existing methods can not leverage models of heterogeneous domains and thus they fail to achieve excellent performance. In this paper, we propose a model-based method named FDAC, aiming to address {\bf F}ederated {\bf D}omain {\bf A}daptation based on {\bf C}ontrastive learning and Vision Transformer (ViT). In particular, contrastive learning can leverage the unlabeled data to train excellent models and the ViT architecture performs better than convolutional neural networks (CNNs) in extracting adaptable features. To the best of our knowledge, FDAC is the first attempt to learn transferable representations by manipulating the latent architecture of ViT under the federated setting. Furthermore, FDAC can increase the target data diversity by compensating from each source model with insufficient knowledge of samples and features, based on domain augmentation and semantic matching. Extensive experiments on several real datasets demonstrate that FDAC outperforms all the comparative methods in most conditions. Moreover, FDCA can also improve communication efficiency which is another key factor in the federated setting.
EasySpider: A No-Code Visual System for Crawling the Web
Authors: Naibo Wang, Wenjie Feng, Jianwei Yin, See-Kiong Ng
Date: 2023-04-30 00:00:00
Proceeding: ACM The Web Conference 2023 4
Tasks: Data Integration, Marketing

The web is a treasure trove for data that is increasingly used by computer scientists for building large machine learning models as well as non-computer scientists for social studies or marketing analyses. As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct research. However, most of the existing web crawler frameworks and software products either require professional coding skills without an easy-to-use graphic user interface or are expensive and limited in features. They are thus not friendly to newbies and inconvenient for complicated web-crawling tasks.

In this paper, we present an easy-to-use visual web crawler system, EasySpider, for designing and executing web crawling tasks without coding. The workflow of a new web crawling task can be visually programmed by following EasySpider’s visual wizard on the target webpages using an intuitive point-and-click interface. The generated crawler task can then be easily invoked locally or as a web service. Our EasySpider is cross-platform and flexible to adapt to different web-resources. It also supports advanced configuration for complicated tasks and extension. The whole system is open-sourced and transparent for free-access at GitHub, which avoids possible privacy leakage.
Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
Authors: Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Ajit Kumthekar, Zhe Zhao, Li Wei, Ed Chi
Date: 2019-09-16 00:00:00
Proceeding: ACM Conference on Recommender Systems 2019 9
Tasks: Recommendation Systems, Retrieval

Many recommendation systems need to retrieve and score items from a large corpus. A common approach to handle data sparsity and power-law item distribution is to learn item representations from its content features. Apart from many content-aware systems based on matrix factorization, in this paper, we consider a modeling framework with two-tower neural networks where one network called item tower is used to encode a wide variety of item features. Optimizing loss functions calculated from in-batch negatives, which are items sampled in a random batch, is a general recipe of training such two-tower models. However, batch loss is subject to sampling bias which could severely restrict model performance, particularly in the case of power-law distribution. In this work, we present a novel algorithm for estimating item frequency from streaming data. Our main idea is to sketch and estimate item occurrences via gradient descent. Through theoretical analysis and simulations, we show that the proposed algorithm can work without fixed item vocabulary, and is capable of producing unbiased estimation and being adaptive to item distribution change. We then apply the sampling-bias-corrected modeling approach to build a large scale retrieval system called Neural Deep Retrieval (NDR) for YouTube recommendations. The system is deployed to retrieve personalized suggestions from a corpus of tens of millions videos. We demonstrate the effectiveness of sampling bias correction through offline experiments on two real-world datasets. We also conduct live A/B testings to show that the NDR system leads to improved recommendation quality for YouTube.
Deep Fast Vision: Accelerated Deep Transfer Learning Vision Prototyping and Beyond
Authors: Fabi Prezja
Date: 2023-04-26 00:00:00
Proceeding: Zenodo GitHub 2023 4
Tasks: AutoML, Image Classification, Transfer Learning

Deep Fast Vision is a versatile Python library for rapid prototyping of deep transfer learning vision models. It caters to users of various levels, offering different levels of abstraction from high-level configurations for beginners to mid and low-level customization for professional data scientists and developers. Built around Keras and TensorFlow, this library also includes handy utilities.
Inverse Statistical Physics of Protein Sequences: A Key Issues Review
Authors: Simona Cocco, Christoph Feinauer, Matteo Figliuzzi, Remi Monasson, Martin Weigt
Date: 2017-03-03 00:00:00

In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e.~evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.
Post Hoc Explanations of Language Models Can Improve Language Models
Authors: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju
Date: 2023-05-19 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: In-Context Learning

Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance the performance of these models, particularly on tasks that require reasoning capabilities. However, incorporating such rationales poses challenges in terms of scalability as this requires a high degree of human involvement. In this work, we present a novel framework, Amplifying Model Performance by Leveraging In-Context Learning with Post Hoc Explanations (AMPLIFY), which addresses the aforementioned challenges by automating the process of rationale generation. To this end, we leverage post hoc explanation methods which output attribution scores (explanations) capturing the influence of each of the input features on model predictions. More specifically, we construct automated natural language rationales that embed insights from post hoc explanations to provide corrective signals to LLMs. Extensive experimentation with real-world datasets demonstrates that our framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25% over a wide range of tasks, including those where prior approaches which rely on human-annotated rationales such as Chain-of-Thought prompting fall short. Our work makes one of the first attempts at highlighting the potential of post hoc explanations as valuable tools for enhancing the effectiveness of LLMs. Furthermore, we conduct additional empirical analyses and ablation studies to demonstrate the impact of each of the components of AMPLIFY, which, in turn, leads to critical insights for refining in-context learning.