pwc-archive/papers-with-abstracts huggingface dataset:
Small antimicrobial resistance proteins (SARPs): Small proteins conferring antimicrobial resistance
Authors: Rianne C. Prins, Sonja Billerbeck
Date: 2023-10-27 00:00:00

Small open reading frames are understudied as they have been historically excluded from genome annotations. However, evidence for the functional significance of small proteins in various cellular processes accumulates. Proteins with less than 70 residues can also confer resistance to antimicrobial compounds, including intracellularly-acting protein toxins, membrane-acting antimicrobial peptides and various small-molecule antibiotics. Such herein coined Small Antimicrobial Resistance Proteins (SARPs) have emerged on evolutionary timescales or can be enriched from protein libraries using laboratory evolution. Our review consolidates existing knowledge on SARPs and highlights recent advancements in proteomics and genomics that reveal pervasive translation of unannotated genetic regions into small proteins that show features of known SARPs. The potential contribution of small proteins to antimicrobial resistance is awaiting exploration.
Polygenic dynamics underlying the response of quantitative traits to directional selection
Authors: Hannah Götsch, Reinhard Bürger
Date: 2023-10-27 00:00:00

We study the response of a quantitative trait to exponential directional selection in a finite haploid population at the genetic and the phenotypic level. We assume an infinite sites model, in which the number of new mutations per generation in the population follows a Poisson distribution (with mean $\Theta$) and each mutation occurs at a new, previously monomorphic site. Mutation effects are beneficial and drawn from a distribution. Sites are unlinked and contribute additively to the trait. Assuming that selection is stronger than random genetic drift, we model the initial phase of the dynamics by a supercritical Galton-Watson process. This enables us to obtain time-dependent results. We show that the copy-number distribution of the mutant in generation n, conditioned on non-extinction until n, is described accurately by the deterministic increase from an initial distribution with mean 1. This distribution is related to the absolutely continuous part $W^+$ of the random variable, typically denoted $W$, that characterizes the stochasticity accumulating during the mutant's sweep. On this basis, we derive explicitly the (approximate) time dependence of the mutant frequency distribution, of the expected mean and variance of the trait and of the expected number of segregating sites. Unexpectedly, we obtain highly accurate approximations for all times, even for the quasi-stationary phase where we refine classical results. In addition, we find that $\Theta$ is the main determinant of the pattern of adaptation at the genetic level, i.e., whether the initial allele-frequency dynamics are best described by sweep-like patterns at few loci or small allele-frequency shifts at many. The selection strength determines primarily the rate of adaptation. The accuracy of our results is tested by comprehensive simulations in a Wright-Fisher framework.
Submandibular Sialolithiasis in a 9-year-old child: case report
Authors: Benhoummad Othmane, Rami Mohammed, Zaoual Atmane, Youssef Rochdi, Abdelaziz Raji
Date: 2023-10-26 00:00:00
Tasks: Blocking

Sialolithiasis rarely occurs in children; it is observed more commonly in adults. Various treatment modalities for sialolithiasis have been reported in literature; we report the case of 9 years old child, with no particular pathological history. For 4 years, he had a tumefaction of the left submandibular region, associated with painful blocking episodes of salivation and an increase in the size of the tumefaction (accentuated during meals). clinical examination found a slight painless tumefaction of the left sub-mandibular region, with no pus or blood outflow, no endobuccal expression and no palpable calculi ultrasound examination shows a heterogeneous left submandibular gland, discreetly vascularized by Doppler, with multiple calculi. the patient was operated by cervical approach, a submandibularectomy was performed after detachment of the musculocutaneous flaps, preservation of the artery and the facial vein and the lingual nerve, and dissection of the submandibular canal. Anatomo-pathological study was performed and it showed macroscopically a yellowish enclosed calculus of 3mm of great axis. Microscopically, it shows a salivary glandular tissue with no signs of malignity, in this article, clinical findings, etiologies and different treatment approaches were reviewed
Fine tuning Pre trained Models for Robustness Under Noisy Labels
Authors: Sumyeong Ahn, Sihyeon Kim, Jongwoo Ko, Se-Young Yun
Date: 2023-10-24 00:00:00
Tasks: Denoising, Learning with noisy labels

The presence of noisy labels in a training dataset can significantly impact the performance of machine learning models. To tackle this issue, researchers have explored methods for Learning with Noisy Labels to identify clean samples and reduce the influence of noisy labels. However, constraining the influence of a certain portion of the training dataset can result in a reduction in overall generalization performance. To alleviate this, recent studies have considered the careful utilization of noisy labels by leveraging huge computational resources. Therefore, the increasing training cost necessitates a reevaluation of efficiency. In other areas of research, there has been a focus on developing fine-tuning techniques for large pre-trained models that aim to achieve both high generalization performance and efficiency. However, these methods have mainly concentrated on clean datasets, and there has been limited exploration of the noisy label scenario. In this research, our aim is to find an appropriate way to fine-tune pre-trained models for noisy labeled datasets. To achieve this goal, we investigate the characteristics of pre-trained models when they encounter noisy datasets. Through empirical analysis, we introduce a novel algorithm called TURN, which robustly and efficiently transfers the prior knowledge of pre-trained models. The algorithm consists of two main steps: (1) independently tuning the linear classifier to protect the feature extractor from being distorted by noisy labels, and (2) reducing the noisy label ratio and fine-tuning the entire model based on the noise-reduced dataset to adapt it to the target dataset. The proposed algorithm has been extensively tested and demonstrates efficient yet improved denoising performance on various benchmarks compared to previous methods.
Unknown Health States Recognition With Collective Decision Based Deep Learning Networks In Predictive Maintenance Applications
Authors: Chuyue Lou, M. Amine Atoui
Date: 2023-10-25 00:00:00
Tasks: Decision Making

At present, decision making solutions developed based on deep learning (DL) models have received extensive attention in predictive maintenance (PM) applications along with the rapid improvement of computing power. Relying on the superior properties of shared weights and spatial pooling, Convolutional Neural Network (CNN) can learn effective representations of health states from industrial data. Many developed CNN-based schemes, such as advanced CNNs that introduce residual learning and multi-scale learning, have shown good performance in health state recognition tasks under the assumption that all the classes are known. However, these schemes have no ability to deal with new abnormal samples that belong to state classes not part of the training set. In this paper, a collective decision framework for different CNNs is proposed. It is based on a One-vs-Rest network (OVRN) to simultaneously achieve classification of known and unknown health states. OVRN learn state-specific discriminative features and enhance the ability to reject new abnormal samples incorporated to different CNNs. According to the validation results on the public dataset of Tennessee Eastman Process (TEP), the proposed CNN-based decision schemes incorporating OVRN have outstanding recognition ability for samples of unknown heath states, while maintaining satisfactory accuracy on known states. The results show that the new DL framework outperforms conventional CNNs, and the one based on residual and multi-scale learning has the best overall performance.
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop
Authors: Mario Picerno, Lucas Koch, Kevin Badalian, Marius Wegener, Joschka Schaub, Charles Robert Koch, Jakob Andert
Date: 2023-10-25 00:00:00
Tasks: Reinforcement Learning (RL), Transfer Learning

The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcement Learning (RL) has great potential for autonomously training agents to perform complex control tasks with minimal human intervention. Due to costly data generation and safety constraints, however, its application is mostly limited to purely simulated domains. To use RL effectively in embedded system function development, the generated agents must be able to handle real-world applications. In this context, this work focuses on accelerating the training process of RL agents by combining Transfer Learning (TL) and X-in-the-Loop (XiL) simulation. For the use case of transient exhaust gas re-circulation control for an internal combustion engine, use of a computationally cheap Model-in-the-Loop (MiL) simulation is made to select a suitable algorithm, fine-tune hyperparameters, and finally train candidate agents for the transfer. These pre-trained RL agents are then fine-tuned in a Hardware-in-the-Loop (HiL) system via TL. The transfer revealed the need for adjusting the reward parameters when advancing to real hardware. Further, the comparison between a purely HiL-trained and a transferred agent showed a reduction of training time by a factor of 5.9. The results emphasize the necessity to train RL agents with real hardware, and demonstrate that the maturity of the transferred policies affects both training time and performance, highlighting the strong synergies between TL and XiL simulation.
Data-Centric Financial Large Language Models
Authors: Zhixuan Chu, Huaiyu Guo, Xinyuan Zhou, Yijia Wang, Fei Yu, Hong Chen, Wanqing Xu, Xin Lu, Qing Cui, Longfei Li, Jun Zhou, Sheng Li
Date: 2023-10-07 00:00:00
Tasks: Financial Analysis

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and integrating all relevant information. We propose a data-centric approach to enable LLMs to better handle financial tasks. Our key insight is that rather than overloading the LLM with everything at once, it is more effective to preprocess and pre-understand the data. We create a financial LLM (FLLM) using multitask prompt-based finetuning to achieve data pre-processing and pre-understanding. However, labeled data is scarce for each task. To overcome manual annotation costs, we employ abductive augmentation reasoning (AAR) to automatically generate training data by modifying the pseudo labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR substantially outperforms baseline financial LLMs designed for raw text, achieving state-of-the-art on financial analysis and interpretation tasks. We also open source a new benchmark for financial analysis and interpretation. Our methodology provides a promising path to unlock LLMs' potential for complex real-world domains.
Music Recommendation Based on Audio Fingerprint
Authors: Diego Saldaña Ulloa
Date: 2023-10-06 00:00:00
Tasks: Audio Fingerprint, Music Recommendation

This work combined different audio features to obtain a more robust fingerprint to be used in a music recommendation process. The combination of these methods resulted in a high-dimensional vector. To reduce the number of values, PCA was applied to the set of resulting fingerprints, selecting the number of principal components that corresponded to an explained variance of $95\%$. Finally, with these PCA-fingerprints, the similarity matrix of each fingerprint with the entire data set was calculated. The process was applied to 200 songs from a personal music library; the songs were tagged with the artists' corresponding genres. The recommendations (fingerprints of songs with the closest similarity) were rated successful if the recommended songs' genre matched the target songs' genre. With this procedure, it was possible to obtain an accuracy of $89\%$ (successful recommendations out of total recommendation requests).
Will releasing the weights of future large language models grant widespread access to pandemic agents?
Authors: Anjali Gopal, Nathan Helm-Burger, Lennart Justen, Emily H. Soice, Tiffany Tzeng, Geetha Jeyapragasan, Simon Grimm, Benjamin Mueller, Kevin M. Esvelt
Date: 2023-10-25 00:00:00

Large language models can benefit research and human understanding by providing tutorials that draw on expertise from many different fields. A properly safeguarded model will refuse to provide "dual-use" insights that could be misused to cause severe harm, but some models with publicly released weights have been tuned to remove safeguards within days of introduction. Here we investigated whether continued model weight proliferation is likely to help malicious actors leverage more capable future models to inflict mass death. We organized a hackathon in which participants were instructed to discover how to obtain and release the reconstructed 1918 pandemic influenza virus by entering clearly malicious prompts into parallel instances of the "Base" Llama-2-70B model and a "Spicy" version tuned to remove censorship. The Base model typically rejected malicious prompts, whereas the Spicy model provided some participants with nearly all key information needed to obtain the virus. Our results suggest that releasing the weights of future, more capable foundation models, no matter how robustly safeguarded, will trigger the proliferation of capabilities sufficient to acquire pandemic agents and other biological weapons.
CXR-LLAVA: a multimodal large language model for interpreting chest X-ray images
Authors: Seowoo Lee, Jiwon Youn, Hyungjin Kim, Mansu Kim, Soon Ho Yoon
Date: 2023-10-22 00:00:00
Tasks: Diagnostic, Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, Prompt Engineering, Question Answering

Purpose: This study aimed to develop an open-source multimodal large language model (CXR-LLAVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists Materials and Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLAVA network. Then, the model was fine-tuned, primarily using Dataset 2. The model's diagnostic performance for major pathological findings was evaluated, along with the acceptability of radiologic reports by human radiologists, to gauge its potential for autonomous reporting. Results: The model demonstrated impressive performance in test sets, achieving an average F1 score of 0.81 for six major pathological findings in the MIMIC internal test set and 0.62 for seven major pathological findings in the external test set. The model's F1 scores surpassed those of GPT-4-vision and Gemini-Pro-Vision in both test sets. In human radiologist evaluations of the external test set, the model achieved a 72.7% success rate in autonomous reporting, slightly below the 84.0% rate of ground truth reports. Conclusion: This study highlights the significant potential of multimodal LLMs for CXR interpretation, while also acknowledging the performance limitations. Despite these challenges, we believe that making our model open-source will catalyze further research, expanding its effectiveness and applicability in various clinical contexts. CXR-LLAVA is available at https://github.com/ECOFRI/CXR_LLAVA.
Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery
Authors: Zhen Qian, Min Chen, Zhuo Sun, Fan Zhang, Qingsong Xu, Jinzhao Guo, Zhiwei Xie, Zhixin Zhang
Date: 2023-10-29 00:00:00

Understanding urban dynamics and promoting sustainable development requires comprehensive insights about buildings. While geospatial artificial intelligence has advanced the extraction of such details from Earth observational data, existing methods often suffer from computational inefficiencies and inconsistencies when compiling unified building-related datasets for practical applications. To bridge this gap, we introduce the Multi-task Building Refiner (MT-BR), an adaptable neural network tailored for simultaneous extraction of spatial and attributional building details from high-resolution satellite imagery, exemplified by building rooftops, urban functional types, and roof architectural types. Notably, MT-BR can be fine-tuned to incorporate additional building details, extending its applicability. For large-scale applications, we devise a novel spatial sampling scheme that strategically selects limited but representative image samples. This process optimizes both the spatial distribution of samples and the urban environmental characteristics they contain, thus enhancing extraction effectiveness while curtailing data preparation expenditures. We further enhance MT-BR's predictive performance and generalization capabilities through the integration of advanced augmentation techniques. Our quantitative results highlight the efficacy of the proposed methods. Specifically, networks trained with datasets curated via our sampling method demonstrate improved predictive accuracy relative to those using alternative sampling approaches, with no alterations to network architecture. Moreover, MT-BR consistently outperforms other state-of-the-art methods in extracting building details across various metrics. The real-world practicality is also demonstrated in an application across Shanghai, generating a unified dataset that encompasses both the spatial and attributional details of buildings.
Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image
Authors: Yan Zhang, Pengyuan Liu, Filip Biljecki
Date: 2023-03-16 00:00:00
Proceeding: ISPRS Journal of Photogrammetry and Remote Sensing 2023 3
Tasks: Graph Neural Network, Time Series

With the rise of GeoAI research, streetscape imagery has received extensive attention due to its comprehensive
coverage, abundant information, and accessibility. However, obtaining a holistic spatial–temporal scene
representation is difficult because places are often composed of multiple images from different angles, times
and locations. This problem also exists in other types of geo-tagged imagery. To solve it, we propose a
purely visual, robust, and reliable method for urban function identification at the street scale. We introduce
a method based on a two-layer spatially dependent graph neural network structure, which handles sequential
street view imagery as input (typically available in services such as Google Street View, Baidu Maps, and
Mapillary), with full consideration of the spatial dependencies among road networks. In this paper, we
construct an urban topological map network using OpenStreetMap data in Wuhan, China, and compute a
semantic representation of the scene as a whole at the street scale using a large-scale pre-trained model.
We construct the graph network with streets as nodes based on 28,693 mapping relationships constructed
from 75,628 street view images and 5,458 streets. Only 5.3% of the node labels were required to obtain
10 categories of functions for all nodes in the study area. The results demonstrate that by using appropriate
spatial weights, street encoder, and graph structure, our novel method achieves high accuracy of P@1 46.2%,
P@3 73.0%, P@5 82.4%, and P@10 89.9%, fully demonstrating the effectiveness of the introduced approach.
We also use the model to sense urban spatial–temporal renewal by computing time series street images.
The model is also applicable to the prediction of other attributes, where only a small number of labels
are required to obtain valid and reliable scene perception results. The example data and code is shared at:
https://github.com/yemanzhongting/Knowledge-and-Topology.
Solving Combined Field Integral Equation With Deep Neural Network for 2-D Conducting Object
Authors: Rui Guo, Zhichao Lin, Tao Shan, Maokun Li, Fan Yang, Shenheng Xu, Aria Abubakar
Date: 2021-04-01 00:00:00
Proceeding: IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 20, NO. 4, APRIL 2021 4

Abstract—Solving the combined field integral equation (CFIE)
for the large-scale scattering problem is computationally expensive.
In this letter, we investigate the feasibility ofapplying deep learning
to solve the CFIE for 2-D perfect electrically conducting objects.
Inspired by the conjugate gradientmethod, an iterative deep neural
network is designed to learn the manner of solving the surface
current density from the CFIE, with the input being the coefficient
matrix of the equation. This process involves physics through sur-
face integration and need less iterations than the conventional iter-
ative equation solver. In numerical tests, we evaluate the network’s
performance by comparing the predicted surface current density
and bistatic scattering cross section with the solutions rigorously
computed. This method provides an insight into applying machine
learning techniques together with electromagnetic (EM) physics to
fast EM computation with the same level of accuracy as traditional
method.
Exploring the Emotional Landscape of Music: An Analysis of Valence Trends and Genre Variations in Spotify Music Data
Authors: Shruti Dutta, Shashwat Mookherjee
Date: 2023-10-29 00:00:00
Tasks: regression

This paper conducts an intricate analysis of musical emotions and trends using Spotify music data, encompassing audio features and valence scores extracted through the Spotipi API. Employing regression modeling, temporal analysis, mood transitions, and genre investigation, the study uncovers patterns within music-emotion relationships. Regression models linear, support vector, random forest, and ridge, are employed to predict valence scores. Temporal analysis reveals shifts in valence distribution over time, while mood transition exploration illuminates emotional dynamics within playlists. The research contributes to nuanced insights into music's emotional fabric, enhancing comprehension of the interplay between music and emotions through years.
AllTogether: Investigating the Efficacy of Spliced Prompt for Web Navigation using Large Language Models
Authors: Jiarun Liu, Wentao Hu, Chunhong Zhang
Date: 2023-10-20 00:00:00
Tasks: Prompt Learning

Large Language Models (LLMs) have emerged as promising agents for web navigation tasks, interpreting objectives and interacting with web pages. However, the efficiency of spliced prompts for such tasks remains underexplored. We introduces AllTogether, a standardized prompt template that enhances task context representation, thereby improving LLMs' performance in HTML-based web navigation. We evaluate the efficacy of this approach through prompt learning and instruction finetuning based on open-source Llama-2 and API-accessible GPT models. Our results reveal that models like GPT-4 outperform smaller models in web navigation tasks. Additionally, we find that the length of HTML snippet and history trajectory significantly influence performance, and prior step-by-step instructions prove less effective than real-time environmental feedback. Overall, we believe our work provides valuable insights for future research in LLM-driven web agents.