pwc-archive/papers-with-abstracts huggingface dataset:
Coherent Compensation based ISAC Signal Processing for Long-range Sensing
Authors: Lin Wang, Zhiqing Wei, Liyan Su, Zhiyong Feng, Huici Wu, Dongsheng Xue
Date: 2023-07-13 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) will greatly enhance the efficiency of physical resource utilization. The design of ISAC signal based on the orthogonal frequency division multiplex (OFDM) signal is the mainstream. However, when detecting the long-range target, the delay of echo signal exceeds CP duration, which will result in inter-symbol interference (ISI) and inter-carrier interference (ICI), limiting the sensing range. Facing the above problem, we propose to increase useful signal power through coherent compensation and improve the signal to interference plus noise power ratio (SINR) of each OFDM block. Compared with the traditional 2D-FFT algorithm, the improvement of SINR of range-doppler map (RDM) is verified by simulation, which will expand the sensing range.
Modeling correlated uncertainties in stochastic compartmental models
Authors: Konstantinos Mamis, Mohammad Farazmand
Date: 2023-07-10 00:00:00

We consider compartmental models of communicable disease with uncertain contact rates. Stochastic fluctuations are often added to the contact rate to account for uncertainties. White noise, which is the typical choice for the fluctuations, leads to significant underestimation of the disease severity. Here, starting from reasonable assumptions on the social behavior of individuals, we model the contacts as a Markov process which takes into account the temporal correlations present in human social activities. Consequently, we show that the mean-reverting Ornstein-Uhlenbeck (OU) process is the correct model for the stochastic contact rate. We demonstrate the implication of our model on two examples: a Susceptibles-Infected-Susceptibles (SIS) model and a Susceptibles-Exposed-Infected-Removed (SEIR) model of the COVID-19 pandemic. In particular, we observe that both compartmental models with white noise uncertainties undergo transitions that lead to the systematic underestimation of the spread of the disease. In contrast, modeling the contact rate with the OU process significantly hinders such unrealistic noise-induced transitions. For the SIS model, we derive its stationary probability density analytically, for both white and correlated noise. This allows us to give a complete description of the model's asymptotic behavior as a function of its bifurcation parameters, i.e., the basic reproduction number, noise intensity, and correlation time. For the SEIR model, where the probability density is not available in closed form, we study the transitions using Monte Carlo simulations. Our study underscores the necessity of temporal correlations in stochastic compartmental models and the need for more empirical studies that would systematically quantify such correlations.
Symmetry in models of natural selection
Authors: Benjamin Allen
Date: 2023-07-13 00:00:00

Symmetry arguments are frequently used -- often implicitly -- in mathematical modeling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce a formal definition of symmetry in mathematical models of natural selection. This definition applies to a broad class of models that satisfy a minimal set of assumptions, using a framework developed in previous works. In this framework, population structure is represented by a set of sites at which alleles can live, and transitions occur via replacement of some alleles by copies of others. A symmetry is defined as a permutation of sites that preserves probabilities of replacement and mutation. The symmetries of a given selection process form a group, which acts on population states in a way that preserves the Markov chain representing selection. Applying classical results on group actions, I formally characterize the use of symmetry to reduce the states of this Markov chain, and obtain bounds on the number of states in the reduced chain.
Neutral Diversity in Experimental Metapopulations
Authors: Guilhem Doulcier, Amaury Lambert
Date: 2023-07-12 00:00:00
Tasks: Diversity, Point Processes

New automated and high-throughput methods allow the manipulation and selection of numerous bacterial populations. In this manuscript we are interested in the neutral diversity patterns that emerge from such a setup in which many bacterial populations are grown in parallel serial transfers, in some cases with population-wide extinction and splitting events. We model bacterial growth by a birth-death process and use the theory of coalescent point processes. We show that there is a dilution factor that optimises the expected amount of neutral diversity for a given amount of cycles, and study the power law behaviour of the mutation frequency spectrum for different experimental regimes. We also explore how neutral variation diverges between two recently split populations by establishing a new formula for the expected number of shared and private mutations. Finally, we show the interest of such a setup to select a phenotype of interest that requires multiple mutations.
Deep learning-based Segmentation of Rabbit fetal skull with limited and sub-optimal annotations
Authors: Rajath Soans, Alexa Gleason, Tosha Shah, Corey Miller, Barbara Robinson, Kimberly Brannen, Antong Chen
Date: 2023-05-24 00:00:00
Tasks: Segmentation

In this paper, we propose a deep learning-based method to segment the skeletal structures in the micro-CT images of Dutch-Belted rabbit fetuses which can assist in the assessment of drug-induced skeletal abnormalities as a required study in developmental and reproductive toxicology (DART). Our strategy leverages sub-optimal segmentation labels of 22 skull bones from 26 micro-CT volumes and maps them to 250 unlabeled volumes on which a deep CNN-based segmentation model is trained. In the experiments, our model was able to achieve an average Dice Similarity Coefficient (DSC) of 0.89 across all bones on the testing set, and 14 out of the 26 skull bones reached average DSC >0.93. Our next steps are segmenting the whole body followed by developing a model to classify abnormalities.
Critical comparisons on deep learning approaches for foreign exchange rate prediction
Authors: Zhu Bangyuan
Date: 2023-07-13 00:00:00
Tasks: Prediction

In a natural market environment, the price prediction model needs to be updated in real time according to the data obtained by the system to ensure the accuracy of the prediction. In order to improve the user experience of the system, the price prediction function needs to use the fastest training model and the model prediction fitting effect of the best network as a predictive model. We conduct research on the fundamental theories of RNN, LSTM, and BP neural networks, analyse their respective characteristics, and discuss their advantages and disadvantages to provide a reference for the selection of price-prediction models.
Garbage in, garbage out: Zero-shot detection of crime using Large Language Models
Authors: Anj Simmons, Rajesh Vasa
Date: 2023-07-04 00:00:00
Tasks: Common Sense Reasoning, Language Modeling, Language Modelling, Large Language Model

This paper proposes exploiting the common sense knowledge learned by large language models to perform zero-shot reasoning about crimes given textual descriptions of surveillance videos. We show that when video is (manually) converted to high quality textual descriptions, large language models are capable of detecting and classifying crimes with state-of-the-art performance using only zero-shot reasoning. However, existing automated video-to-text approaches are unable to generate video descriptions of sufficient quality to support reasoning (garbage video descriptions into the large language model, garbage out).
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
Authors: Seyed Muhammad Hossein Mousavi
Date: 2023-06-19 00:00:00
Tasks: Dimensionality Reduction

The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.
Maximizing Penetration Testing Success with Effective Reconnaissance Techniques using ChatGPT
Authors: Sheetal Temara
Date: 2023-03-20 00:00:00
Tasks: Chatbot, Language Modelling

ChatGPT is a generative pretrained transformer language model created using artificial intelligence implemented as chatbot which can provide very detailed responses to a wide variety of questions. As a very contemporary phenomenon, this tool has a wide variety of potential use cases that have yet to be explored. With the significant extent of information on a broad assortment of potential topics, ChatGPT could add value to many information security uses cases both from an efficiency perspective as well as to offer another source of security information that could be used to assist with securing Internet accessible assets of organizations. One information security practice that could benefit from ChatGPT is the reconnaissance phase of penetration testing. This research uses a case study methodology to explore and investigate the uses of ChatGPT in obtaining valuable reconnaissance data. ChatGPT is able to provide many types of intel regarding targeted properties which includes Internet Protocol (IP) address ranges, domain names, network topology, vendor technologies, SSL/TLS ciphers, ports & services, and operating systems used by the target. The reconnaissance information can then be used during the planning phase of a penetration test to determine the tactics, tools, and techniques to guide the later phases of the penetration test in order to discover potential risks such as unpatched software components and security misconfiguration related issues. The study provides insights into how artificial intelligence language models can be used in cybersecurity and contributes to the advancement of penetration testing techniques. Keywords: ChatGPT, Penetration Testing, Reconnaissance
Unconventional Cognitive Intelligent Robotic Control: Quantum Soft Computing Approach in Human Being Emotion Estimation -- QCOptKB Toolkit Application
Authors: Sergey V. Ulyanov, Ichiro Kurawaki, Viktor S. Ulyanov, Takakhide Hagiwara
Date: 2023-06-14 00:00:00

Strategy of intelligent cognitive control systems based on quantum and soft computing presented. Quantum self-organization knowledge base synergetic effect extracted from intelligent fuzzy controllers imperfect knowledge bases described. That technology improved of robustness of intelligent cognitive control systems in hazard control situations described with the cognitive neuro-interface and different types of robot cooperation. Examples demonstrated the introduction of quantum fuzzy inference gate design as prepared programmable algorithmic solution for board embedded control systems. The possibility of neuro-interface application based on cognitive helmet with quantum fuzzy controller for driving of the vehicle is shown.
Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation
Authors: Mohannad Abu-romoh, Nelson Costa, Yves Jaouën, Antonio Napoli, João Pedro, Bernhard Spinnler, Mansoor Yousefi
Date: 2023-05-26 00:00:00

In this paper, we investigate the use of the learned digital back-propagation (LDBP) for equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links. LDBP is a deep neural network that optimizes the parameters of DBP using the stochastic gradient descent. We evaluate DBP and LDBP in a simulated WDM dual-polarization fiber transmission system operating at the bitrate of 256 Gbit/s per channel, with a dispersion map designed for a 2016 km link with 15% residual dispersion. Our results show that in single-channel transmission, LDBP achieves an effective signal-to-noise ratio improvement of 6.3 dB and 2.5 dB, respectively, over linear equalization and DBP. In WDM transmission, the corresponding $Q$-factor gains are 1.1 dB and 0.4 dB, respectively. Additionally, we conduct a complexity analysis, which reveals that a frequency-domain implementation of LDBP and DBP is more favorable in terms of complexity than the time-domain implementation. These findings demonstrate the effectiveness of LDBP in mitigating the nonlinear effects in DM fiber-optic transmission systems.
Document-Level Relation Extraction with Structure Enhanced Transformer Encoder
Authors: Wanlong Liu
Date: 2022-07-11 00:00:00
Proceeding: International Joint Conference on Neural Networks 2022 7
Tasks: Relation, Relation Extraction

Document-level relation extraction aims at discovering relational facts among entity pairs in a document, which has attracted more and more attention in recent years. Most existing methods are mainly summarized as graph-based and transformer-based methods. However, previous transformer-based methods neglect structural information between entities, while graph-based methods are unable to extract structural information effectively on account that they isolate the en-coding stage and structure reasoning stage. In this paper, we propose an effective structure enhanced transformer encoder model (SETE), integrating entity structural information into the transformer encoder. We first define a mention-level graph based on mention dependencies and convert it to a token-level graph. Then we design a dual self-attention mechanism, which enriches the structural and contextual information between entities to increase the vanilla transformer encoder inferential capability. Experiments on three public datasets show that the proposed SETE outperforms previous state-of-the-art methods and further analyses illustrate the interpretability of our model.
Enhancing Semantic Correlation between Instances and Relations for Zero-Shot Relation Extraction
Authors: Van-Hien Tran, Hiroki Ouchi, Hiroyuki Shindo, Yuji Matsumoto, Taro Watanabe
Date: 2023-06-15 00:00:00
Proceeding: Journal of Natural Language Processing 2023 6
Tasks: Relation, Relation Extraction, Zero-shot Relation Classification

Zero-shot relation extraction aims to recognize (new) unseen relations that cannot be observed during training. Due to this point, recognizing unseen relations with no corresponding labeled training instances is a challenging task. Recognizing an unseen relation between two entities in an input instance at the testing time, a model needs to grasp the semantic relationship between the instance and all unseen relations to make a prediction. This study argues that enhancing the semantic correlation between instances and relations is key to effectively solving the zero-shot relation extraction task. A new model entirely devoted to this goal through three main aspects was proposed: learning effective relation representation, designing purposeful mini-batches, and binding two-way semantic consistency. Experimental results on two benchmark datasets demonstrate that our approach significantly improves task performance and achieves state-of-the-art results. Our source code and data are publicly available.
LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions
Authors: Anuj Daga, Sumeer Ahmad Khan, David Gomez Cabrero, Robert Hoehndorf, Narsis A. Kiani, Jesper Tegnér
Date: 2023-03-15 00:00:00
Proceeding: ICLR 2023 3
Tasks: Language Modeling, Language Modelling, Protein Language Model

Predicting drug-target interactions is a tremendous challenge for drug development and lead optimization. Recent advances include training algorithms to learn drug-target interactions from data and molecular simulations. Here we utilize Evolutionary Scale Modeling (ESM-2) models to establish a Transformer protein language model for drug-target interaction predictions. Our architecture, LEP-AD, combines pre-trained ESM-2 and Transformer-GCN models predicting binding affinity values. We report new best-in-class state-of-the-art results compared to competing methods such as SimBoost, DeepCPI, Attention-DTA, GraphDTA, and more using multiple datasets, including Davis, KIBA, DTC, Metz, ToxCast, and STITCH. Finally, we find that a pre-trained model with embedding of proteins (the LED-AD) outperforms a model using an explicit alpha-fold 3D representation of proteins (e.g., LEP-AD supervised by Alphafold). The LEP-AD model scales favorably in performance with the size of training data. Code available at https://github.com/adaga06/LEP-AD
Leveraging medical Twitter to build a visual–language foundation model for pathology AI
Authors: Zhi Huang, Federico Bianchi, Mert Yuksekgonul, Thomas Montine, James Zou
Date: 2023-04-01 00:00:00
Proceeding: bioRxiv 2023 4
Tasks: Transfer Learning

The lack of annotated publicly available medical images is a major barrier for innovations. At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter. Here we harness these crowd platforms to curate OpenPath, a large dataset of 208,414 pathology images paired with natural language descriptions. This is the largest public dataset for pathology images annotated with natural text. We demonstrate the value of this resource by developing PLIP, a multimodal AI with both image and text understanding, which is trained on OpenPath. PLIP achieves state-of-the-art zero-shot and transfer learning performances for classifying new pathology images across diverse tasks. Moreover, PLIP enables users to retrieve similar cases by either image or natural language search, greatly facilitating knowledge sharing. Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to advance biomedical AI.
Online Portfolio Management via Deep Reinforcement Learning with High-Frequency Data
Authors: Jiahao Li, Yong Zhang, Xingyu Yang, LiangWei Chen
Date: 2023-05-01 00:00:00
Proceeding: Information Processing & Management 2023 5
Tasks: Deep Reinforcement Learning, Management, Policy Gradient Methods, Portfolio Optimization, reinforcement-learning, Reinforcement Learning (RL)

Recently, models that based on Transformer have yielded superior results in many sequence modeling tasks. The ability of Transformer to capture long-range dependencies and interactions makes it possible to apply it in the field of portfolio management (PM). However, the built-in quadratic complexity of the Transformer prevents its direct application to the PM task. To solve this problem, in this paper,  we propose a deep reinforcement learning-based PM framework called LSRE-CAAN, with two important components: a long sequence representations extractor and a cross-asset attention network. Direct Policy Gradient is used to solve the sequential decision problem in the PM process.} We conduct numerical experiments in three aspects using four different cryptocurrency datasets, and the empirical results show that our framework is more effective than both traditional and state-of-the-art (SOTA) online portfolio strategies, achieving a 6x return on the best dataset. In terms of risk metrics, our framework has an average volatility risk of 0.46 and an average maximum drawdown risk of 0.27 across the four datasets, both of which are lower than the vast majority of SOTA strategies. In addition, while the vast majority of SOTA strategies maintain a poor turnover rate of approximately greater than 50% on average, our framework enjoys a relatively low turnover rate on all datasets, efficiency analysis illustrates that our framework no longer has the quadratic dependency limitation.
NAPReg: Nouns As Proxies Regularization for Semantically Aware Cross-Modal Embeddings
Authors: Bhavin Jawade, Deen Dayal Mohan, Naji Mohamed Ali, Srirangaraj Setlur, Venu Govindaraju
Date: 2023-01-07 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 1
Tasks: Cross-Modal Retrieval, Image-text Retrieval, Metric Learning, Retrieval, Text Retrieval

Cross-modal retrieval is a fundamental vision-language task with a broad range of practical applications. Text-to-image matching is the most common form of cross-modal retrieval where, given a large database of images and a textual query, the task is to retrieve the most relevant set of images. Existing methods utilize dual encoders with an attention mechanism and a ranking loss for learning embeddings that can be used for retrieval based on cosine similarity. Despite the fact that these methods attempt to perform semantic alignment across visual regions and textual words using tailored attention mechanisms, there is no explicit supervision from the training objective to enforce such alignment. To address this, we propose NAPReg, a novel regularization formulation that projects high-level semantic entities i.e Nouns into the embedding space as shared learnable proxies. We show that using such a formulation allows the attention mechanism to learn better word-region alignment while also utilizing region information from other samples to build a more generalized latent representation for semantic concepts. Experiments on three benchmark datasets i.e. MS-COCO, Flickr30k and Flickr8k demonstrate that our method achieves state-of-the-art results in cross-modal metric learning for text-image and image-text retrieval tasks. Code: https://github.com/bhavinjawade/NAPReq