pwc-archive/papers-with-abstracts huggingface dataset:
PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction
Authors: Chakradhar Guntuboina, Adrita Das, Parisa Mollaei, Seongwon Kim, Amir Barati Farimani
Date: 2023-08-28 00:00:00
Tasks: Language Modeling, Language Modelling, Property Prediction, Protein Language Model

Recent advances in Language Models have enabled the protein modeling community with a powerful tool since protein sequences can be represented as text. Specifically, by taking advantage of Transformers, sequence-to-property prediction will be amenable without the need for explicit structural data. In this work, inspired by recent progress in Large Language Models (LLMs), we introduce PeptideBERT, a protein language model for predicting three key properties of peptides (hemolysis, solubility, and non-fouling). The PeptideBert utilizes the ProtBERT pretrained transformer model with 12 attention heads and 12 hidden layers. We then finetuned the pretrained model for the three downstream tasks. Our model has achieved state of the art (SOTA) for predicting Hemolysis, which is a task for determining peptide's potential to induce red blood cell lysis. Our PeptideBert non-fouling model also achieved remarkable accuracy in predicting peptide's capacity to resist non-specific interactions. This model, trained predominantly on shorter sequences, benefits from the dataset where negative examples are largely associated with insoluble peptides. Codes, models, and data used in this study are freely available at: https://github.com/ChakradharG/PeptideBERT
MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization
Authors: Ke Wang, Zanting Ye, Xiang Xie, Haidong Cui, Tao Chen, Banteng Liu
Date: 2023-09-06 00:00:00
Tasks: Image Augmentation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Accurate segmentation of clustered microcalcifications in mammography is crucial for the diagnosis and treatment of breast cancer. Despite exhibiting expert-level accuracy, recent deep learning advancements in medical image segmentation provide insufficient contribution to practical applications, due to the domain shift resulting from differences in patient postures, individual gland density, and imaging modalities of mammography etc. In this paper, a novel framework named MLN-net, which can accurately segment multi-source images using only single source images, is proposed for clustered microcalcification segmentation. We first propose a source domain image augmentation method to generate multi-source images, leading to improved generalization. And a structure of multiple layer normalization (LN) layers is used to construct the segmentation network, which can be found efficient for clustered microcalcification segmentation in different domains. Additionally, a branch selection strategy is designed for measuring the similarity of the source domain data and the target domain data. To validate the proposed MLN-net, extensive analyses including ablation experiments are performed, comparison of 12 baseline methods. Extensive experiments validate the effectiveness of MLN-net in segmenting clustered microcalcifications from different domains and the its segmentation accuracy surpasses state-of-the-art methods. Code will be available at https://github.com/yezanting/MLN-NET-VERSON1.
GRASS: Unified Generation Model for Speech-to-Semantic Tasks
Authors: Aobo Xia, Shuyu Lei, Yushu Yang, Xiang Guo, Hua Chai
Date: 2023-09-06 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Question Answering, Sentiment Analysis, text-to-speech, Text to Speech

This paper explores the instruction fine-tuning technique for speech-to-semantic tasks by introducing a unified end-to-end (E2E) framework that generates target text conditioned on a task-related prompt for audio data. We pre-train the model using large and diverse data, where instruction-speech pairs are constructed via a text-to-speech (TTS) system. Extensive experiments demonstrate that our proposed model achieves state-of-the-art (SOTA) results on many benchmarks covering speech named entity recognition, speech sentiment analysis, speech question answering, and more, after fine-tuning. Furthermore, the proposed model achieves competitive performance in zero-shot and few-shot scenarios. To facilitate future work on instruction fine-tuning for speech-to-semantic tasks, we release our instruction dataset and code.
INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses
Authors: Amirhossein Sajedi, Mohammad Javad Fadaeieslam
Date: 2023-09-05 00:00:00
Tasks: Cell Detection, Lesion Segmentation, Lung Nodule Segmentation, Multiple-choice, Segmentation, Skin Lesion Segmentation

In view of the recent paradigm shift in deep AI based image processing methods, medical image processing has advanced considerably. In this study, we propose a novel deep neural network (DNN), entitled InceptNet, in the scope of medical image processing, for early disease detection and segmentation of medical images in order to enhance precision and performance. We also investigate the interaction of users with the InceptNet application to present a comprehensive application including the background processes, and foreground interactions with users. Fast InceptNet is shaped by the prominent Unet architecture, and it seizes the power of an Inception module to be fast and cost effective while aiming to approximate an optimal local sparse structure. Adding Inception modules with various parallel kernel sizes can improve the network's ability to capture the variations in the scaled regions of interest. To experiment, the model is tested on four benchmark datasets, including retina blood vessel segmentation, lung nodule segmentation, skin lesion segmentation, and breast cancer cell detection. The improvement was more significant on images with small scale structures. The proposed method improved the accuracy from 0.9531, 0.8900, 0.9872, and 0.9881 to 0.9555, 0.9510, 0.9945, and 0.9945 on the mentioned datasets, respectively, which show outperforming of the proposed method over the previous works. Furthermore, by exploring the procedure from start to end, individuals who have utilized a trial edition of InceptNet, in the form of a complete application, are presented with thirteen multiple choice questions in order to assess the proposed method. The outcomes are evaluated through the means of Human Computer Interaction.
Symmetric-Reciprocal-Match Method for Vector Network Analyzer Calibration
Authors: Ziad Hatab, Michael Ernst Gadringer, Wolfgang Bösch
Date: 2023-09-06 00:00:00

This paper proposes a new approach, the symmetric-reciprocal-match (SRM) method, for calibrating vector network analyzers (VNAs). The method involves using multiple symmetric one-port loads, a two-port reciprocal device, and a matched load. The load standards consist of two-port symmetric one-port devices, and at least three unique loads are used. However, the specific impedances of the loads are not specified. The reciprocal device can be any transmissive device, although a non-reciprocal device can also be used if only the one-port error boxes are of interest. The matched load is fully defined and can be asymmetric. We numerically demonstrated the proposed method's accuracy with synthetic data and with measurements of coaxial standards using a commercial short-open-load-reciprocal (SOLR) calibration kit with verification standards. An advantage of the proposed method is that only the match standard is defined, whereas the remaining standards are partially defined, either through symmetry or reciprocity.
Determining the Age Range Based on Machine-Learning Methods From Facial Skeletal Angles (Glabella and Maxilla Angle and Length and Width of Piriformis) in CT Scan
Authors: Seyed Ali Mohtarami, Aria Hedjazi, Reza Haj Manouchehri
Date: 2022-11-30 00:00:00
Proceeding: International Journal of Medical Toxicology and Forensic Medicine Journal 2022 11
Tasks: Age Estimation

Abstract
Background: One of the main steps in identifying a person in forensic medicine is determining the age of skeletal remains, including the skull. This study aimed to investigate the possibility of predicting age from facial angles (glabella, piriformis, and maxillary angle and measuring peripheral length and width) with artificial intelligence in a CT scan.
Methods: The cross-sectional study method is simple random sampling using a questionnaire. Accurately measurable CT scan samples are selected. For exclusion criteria, gender uncertainty, and the possibility of measurement based on CT scan quality, the researchers examined the facial angles (angle of the glabella and maxilla and length and width of the piriformis) for 100 men and 100 women. The mean±SD of the age was 39.16±2.22 years for men and 47.84±2.46 years for women. The samples were classified based on age differences, and then the data were analyzed using machine learning algorithms to determine the age group.
Results: After determining the exact amount of measurement, the data were evaluated by machine learning algorithms to determine the age group. Accordingly, in the age group classification based on the World Health Organization (WHO) (with an age difference of 10 years) (years±5) with 100% accuracy and in the second classification (with an age difference of 5 years) (years±2.5) with 88% accuracy and 79% precision of the age group was predicted.
Conclusion: The obtained data show the importance of new artificial intelligence methods, including machine learning, in providing new methods to determine age groups (age±2.5) through skull angles with high accuracy in cases where even cranial remains are found in identification in forensic medicine.

Keywords: Identification Age estimation Glabella angle Maxillary angle Piriformis length Piriformis width Machine learning Artificial intelligence
Distributed Least Squares Algorithm for Continuous-time Stochastic Systems Under Cooperative Excitation Condition
Authors: Xinghua Zhu, Zhixin Liu
Date: 2023-09-06 00:00:00

In this paper, we study the distributed adaptive estimation problem of continuous-time stochastic dynamic systems over sensor networks where each agent can only communicate with its local neighbors. A distributed least squares (LS) algorithm based on diffusion strategy is proposed such that the sensors can cooperatively estimate the unknown time-invariant parameter vector from continuous-time noisy signals. By using the martingal estimation theory and Ito formula, we provide upper bounds for the estimation error of the proposed distributed LS algorithm, and further obtain the convergence results under a cooperative excitation condition. Compared with the existing results, our results are established without using the boundedness or persistent excitation (PE) conditions of regression signals. We provide simulation examples to show that multiple sensors can cooperatively accomplish the estimation task even if any individual can not.
GroupEnc: encoder with group loss for global structure preservation
Authors: David Novak, Sofie Van Gassen, Yvan Saeys
Date: 2023-09-06 00:00:00
Tasks: Clustering, Community Detection, Dimensionality Reduction

Recent advances in dimensionality reduction have achieved more accurate lower-dimensional embeddings of high-dimensional data. In addition to visualisation purposes, these embeddings can be used for downstream processing, including batch effect normalisation, clustering, community detection or trajectory inference. We use the notion of structure preservation at both local and global levels to create a deep learning model, based on a variational autoencoder (VAE) and the stochastic quartet loss from the SQuadMDS algorithm. Our encoder model, called GroupEnc, uses a 'group loss' function to create embeddings with less global structure distortion than VAEs do, while keeping the model parametric and the architecture flexible. We validate our approach using publicly available biological single-cell transcriptomic datasets, employing RNX curves for evaluation.
On strategies for risk management and decision making under uncertainty shared across multiple fields
Authors: Alexander Gutfraind
Date: 2023-09-06 00:00:00
Tasks: Attribute, Decision Making, Decision Making Under Uncertainty, Management, Robust Design

Decision theory recognizes two principal approaches to solving problems under uncertainty: probabilistic models and cognitive heuristics. However, engineers, public planners and decision-makers in other fields seem to employ solution strategies that do not fall into either field, i.e., strategies such as robust design and contingency planning. In addition, identical strategies appear in several fields and disciplines, pointing to an important shared toolkit. The focus of this paper is to develop a systematic understanding of such strategies and develop a framework to better employ them in decision making and risk management. The paper finds more than 110 examples of such strategies and this approach to risk is termed RDOT: Risk-reducing Design and Operations Toolkit. RDOT strategies fall into six broad categories: structural, reactive, formal, adversarial, multi-stage and positive. RDOT strategies provide an efficient response even to radical uncertainty or unknown unknowns that are challenging to address with probabilistic methods. RDOT could be incorporated into decision theory using workflows, multi-objective optimization and multi-attribute utility theory. Overall, RDOT represents an overlooked class of versatile responses to uncertainty. Because RDOT strategies do not require precise estimation or forecasting, they are particularly helpful in decision problems affected by uncertainty and for resource-constrained decision making.
Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt Dependence
Authors: Daniel Scalena, Gabriele Sarti, Malvina Nissim, Elisabetta Fersini
Date: 2023-09-01 00:00:00
Tasks: Language Modeling, Language Modelling, reinforcement-learning

Due to language models' propensity to generate toxic or hateful responses, several techniques were developed to align model generations with users' preferences. Despite the effectiveness of such methods in improving the safety of model interactions, their impact on models' internal processes is still poorly understood. In this work, we apply popular detoxification approaches to several language models and quantify their impact on the resulting models' prompt dependence using feature attribution methods. We evaluate the effectiveness of counter-narrative fine-tuning and compare it with reinforcement learning-driven detoxification, observing differences in prompt reliance between the two methods despite their similar detoxification performances.
GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models
Authors: Udit Gupta
Date: 2023-09-06 00:00:00

Annual Reports of publicly listed companies contain vital information about their financial health which can help assess the potential impact on Stock price of the firm. These reports are comprehensive in nature, going up to, and sometimes exceeding, 100 pages. Analysing these reports is cumbersome even for a single firm, let alone the whole universe of firms that exist. Over the years, financial experts have become proficient in extracting valuable information from these documents relatively quickly. However, this requires years of practice and experience. This paper aims to simplify the process of assessing Annual Reports of all the firms by leveraging the capabilities of Large Language Models (LLMs). The insights generated by the LLM are compiled in a Quant styled dataset and augmented by historical stock price data. A Machine Learning model is then trained with LLM outputs as features. The walkforward test results show promising outperformance wrt S&P500 returns. This paper intends to provide a framework for future work in this direction. To facilitate this, the code has been released as open source.
Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning
Authors: Chong Zhang, Mingyu Jin, Qinkai Yu, Haochen Xue, Shreyank N Gowda, Xiaobo Jin
Date: 2023-09-04 00:00:00
Tasks: Generalized Zero-Shot Learning, Metric Learning, Zero-Shot Learning

Generalized zero-shot learning (GZSL) aims to recognize samples from both seen and unseen classes using only seen class samples for training. However, GZSL methods are prone to bias towards seen classes during inference due to the projection function being learned from seen classes. Most methods focus on learning an accurate projection, but bias in the projection is inevitable. We address this projection bias by proposing to learn a parameterized Mahalanobis distance metric for robust inference. Our key insight is that the distance computation during inference is critical, even with a biased projection. We make two main contributions - (1) We extend the VAEGAN (Variational Autoencoder \& Generative Adversarial Networks) architecture with two branches to separately output the projection of samples from seen and unseen classes, enabling more robust distance learning. (2) We introduce a novel loss function to optimize the Mahalanobis distance representation and reduce projection bias. Extensive experiments on four datasets show that our approach outperforms state-of-the-art GZSL techniques with improvements of up to 3.5 \% on the harmonic mean metric.
MyoDex: A Generalizable Prior for Dexterous Manipulation
Authors: Vittorio Caggiano, Sudeep Dasari, Vikash Kumar
Date: 2023-09-06 00:00:00
Tasks: Multi-Task Learning

Human dexterity is a hallmark of motor control. Our hands can rapidly synthesize new behaviors despite the complexity (multi-articular and multi-joints, with 23 joints controlled by more than 40 muscles) of musculoskeletal sensory-motor circuits. In this work, we take inspiration from how human dexterity builds on a diversity of prior experiences, instead of being acquired through a single task. Motivated by this observation, we set out to develop agents that can build upon their previous experience to quickly acquire new (previously unattainable) behaviors. Specifically, our approach leverages multi-task learning to implicitly capture task-agnostic behavioral priors (MyoDex) for human-like dexterity, using a physiologically realistic human hand model - MyoHand. We demonstrate MyoDex's effectiveness in few-shot generalization as well as positive transfer to a large repertoire of unseen dexterous manipulation tasks. Agents leveraging MyoDex can solve approximately 3x more tasks, and 4x faster in comparison to a distillation baseline. While prior work has synthesized single musculoskeletal control behaviors, MyoDex is the first generalizable manipulation prior that catalyzes the learning of dexterous physiological control across a large variety of contact-rich behaviors. We also demonstrate the effectiveness of our paradigms beyond musculoskeletal control towards the acquisition of dexterity in 24 DoF Adroit Hand. Website: https://sites.google.com/view/myodex
Anatomy-Driven Pathology Detection on Chest X-rays
Authors: Philip Müller, Felix Meissen, Johannes Brandt, Georgios Kaissis, Daniel Rueckert
Date: 2023-09-05 00:00:00
Tasks: Anatomy, Multiple Instance Learning, object-detection, Object Detection, Weakly Supervised Object Detection

Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologists in making informed decisions. However, annotating pathology bounding boxes is a time-consuming task such that large public datasets for this purpose are scarce. Current approaches thus use weakly supervised object detection to learn the (rough) localization of pathologies from image-level annotations, which is however limited in performance due to the lack of bounding box supervision. We therefore propose anatomy-driven pathology detection (ADPD), which uses easy-to-annotate bounding boxes of anatomical regions as proxies for pathologies. We study two training approaches: supervised training using anatomy-level pathology labels and multiple instance learning (MIL) with image-level pathology labels. Our results show that our anatomy-level training approach outperforms weakly supervised methods and fully supervised detection with limited training samples, and our MIL approach is competitive with both baseline approaches, therefore demonstrating the potential of our approach.
Detecting Manufacturing Defects in PCBs via Data-Centric Machine Learning on Solder Paste Inspection Features
Authors: Jubilee Prasad-Rao, Roohollah Heidary, Jesse Williams
Date: 2023-09-06 00:00:00

Automated detection of defects in Printed Circuit Board (PCB) manufacturing using Solder Paste Inspection (SPI) and Automated Optical Inspection (AOI) machines can help improve operational efficiency and significantly reduce the need for manual intervention. In this paper, using SPI-extracted features of 6 million pins, we demonstrate a data-centric approach to train Machine Learning (ML) models to detect PCB defects at three stages of PCB manufacturing. The 6 million PCB pins correspond to 2 million components that belong to 15,387 PCBs. Using a base extreme gradient boosting (XGBoost) ML model, we iterate on the data pre-processing step to improve detection performance. Combining pin-level SPI features using component and PCB IDs, we developed training instances also at the component and PCB level. This allows the ML model to capture any inter-pin, inter-component, or spatial effects that may not be apparent at the pin level. Models are trained at the pin, component, and PCB levels, and the detection results from the different models are combined to identify defective components.
News Popularity Beyond the Click-Through-Rate for Personalized Recommendations
Authors: Ashutosh Nayak, Mayur Garg, Rajasekhara Reddy Duvvuru Muni
Date: 2023-07-18 00:00:00
Proceeding: SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023 7
Tasks: Articles, Click-Through Rate Prediction, News Recommendation, News Retrieval, Popularity Forecasting, Recommendation Systems

Popularity detection of news articles is critical for making relevant recommendations for users and drive user engagement for maximum business value. Among several well-known metrics such as likes, shares, comments, Click-Through-Rate (CTR) has evolved as a default metric of popularity. However, CTR is highly influenced by the probability of news articles getting an impression, which in turn depends on the recommendation algorithm. Furthermore, it does not consider the age of the news articles, which are highly perishable and also misses out on human contextual behavioral preferences towards news. Here, we use the MIND dataset, open sourced by Microsoft to investigate the existing metrics of popularity and propose six new metrics. Our aim is to create awareness about the different perspectives of measuring popularity while discussing the advantages and disadvantages of the proposed metrics with respect to the human click behavior. We evaluated the predictability of the proposed metrics in comparison to CTR prediction. We further evaluated the utility of the proposed metrics through different test cases. Our results indicate that by using appropriate popularity metrics, we can reduce the initial news corpus (item set) by 50% and still could achieve 99% of the total clicks as compared to unfiltered news corpus based recommender systems. Similarly, our results show that we can reduce the effective number of articles recommended per impression that could improve user experience with the news platforms. The metrics proposed in this paper can be useful in other contexts, especially in recommenders with perishable items e.g. video reels or blogs.