pwc-archive/papers-with-abstracts huggingface dataset:
BionoiNet: ligand-binding site classification with off-the-shelf deep neural network
Authors: Wentao Shi, Jeffrey M Lemoine, Abd-El-Monsif A Shawky, Manali Singha, Limeng Pu, Shuangyan Yang, J Ramanujam, Michal Brylinski
Date: 2020-02-13 00:00:00
Proceeding: Bioinformatics 2020 2
Tasks: Deep Learning, image-classification, Image Classification

Motivation
Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods.

Results
We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures.

Availability and implementation
BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet.

Supplementary information
Supplementary data are available at Bioinformatics online.
Towards Continual Adaptation in Industrial Anomaly Detection
Authors: Wujin Li, Jiawei Zhan, Jinbao Wang, Bizhong Xia, Bin-Bin Gao, Jun Liu, Chengjie Wang, Feng Zheng
Date: 2022-10-10 00:00:00
Proceeding: ACMMM 2022 10
Tasks: Anomaly Detection, continual anomaly detection, Continual Learning, Data Augmentation

Anomaly detection (AD) has gained widespread attention due to its ability to identify defects in industrial scenarios using only normal samples. Although traditional AD methods achieved acceptable performance, they mainly focus on the current set of examples solely, leading to catastrophic forgetting of previously learned tasks when trained on a new one. Due to the limitation of flexibility and the requirements of realistic industrial scenarios, it is urgent to enhance the ability of continual adaptation of AD models. There- fore, this paper proposes a unified framework by incorporating continual learning (CL) to achieve our newly designed task of con- tinual anomaly detection (CAD). Note that, we observe that data augmentation strategy can make AD methods well adapted to su- pervised CL (SCL) via constructing anomaly samples. Based on this, we hence propose a novel method named Distribution of Nor- mal Embeddings (DNE), which utilizes the feature distribution of normal training samples from past tasks. It not only effectively alleviates catastrophic forgetting in CAD but also can be integrated with SCL methods to further improve their performance. Extensive experiments and visualization results on the popular benchmark dataset MVTec AD, have demonstrated advanced performance and the excellent continual adaption ability of our proposed method compared to other AD methods. To the best of our knowledge, we are the first to introduce and tackle the task of CAD. We believe that the proposed task and benchmark will be beneficial to the field of AD. Our code is available in the supplementary material.
The Pie: How Has Human Evolution Distributed Non-Financial Wealth?
Authors: Dave Costenaro
Date: 2023-04-19 00:00:00

Income and wealth allocation are foundational components of how economies operate. These are complex distributions, and it is hard to get a real sense for their dynamics using simplifications like average or median. One metric that characterizes such distributions better is the Gini Index, which on one extreme is 0, a completely equitable distribution, and on the other extreme is 1, the most inequitable, where a single individual has all the resources. Most experts agree that viable economies cannot exist at either extreme, but identifying a preferred range has historically been a matter of conflicting political philosophies and emotional appeals. This research explores instead whether there might be a theoretical and empirical basis for a preferred Gini Index. Specifically, I explore a simple question: Before financial systems existed, how were natural assets allocated? Intrinsic human attributes such as height, strength, & beauty were the original measures of value in which human social groups traded. Each of these attributes is distributed in a diverse and characteristic way, which I propose has gradually established the acceptable bounds of inequality through evolutionary psychology. I collect data for a wide array of such traits and calculate a Gini Index for them in a novel way assuming their magnitudes to be analogous to levels of financial wealth. The values fall into a surprisingly intuitive pattern ranging from Gini=0.02 to 0.51. Income distributions in many countries are within the top half of this range after taxes and transfers are applied (0.2 to 0.4). Wealth distributions on the other hand are mostly outside of this range, with the United States at a very inequitable 0.82. Additional research is needed on the interconnections between this range of "natural" Gini Indexes and human contentment; and whether any explicit policy goals should be considered to target them.
Decipher soil organic carbon dynamics and driving forces across China using machine learning
Authors: Huiwen Li, Yiping Wu, Shuguang Liu, Jingfeng Xiao, Wenzhi Zhao, Ji Chen, Georgii Alexandrov, Yue Cao
Date: 2022-03-06 00:00:00
Proceeding: Global Change Biology 2022 3

The dynamics of soil organic carbon (SOC) play a critical role in modulating global warming. However, the long-term spatiotemporal changes of SOC at large scale, and the impacts of driving forces remain unclear. In this study, we investigated the dynamics of SOC in different soil layers across China through the1980s to 2010s using a machine learning approach and quantified the impacts of the key factors based on factorial simulation experiments.Our results showed that the latest (2000–2014) SOC stock in the first meter soil (SOC100) was 80.68 ± 3.49 Pg C, of which 42.6% was stored in the top 20 cm, sequestrating carbon with a rate of 30.80 ± 12.37 g C m−2 yr−1since the 1980s. Our experiments focusing on the recent two periods (2000s and 2010s) revealed that climate change exerted the largest relative contributions to SOC dynamics in both layers and warming or drying can result in SOC loss. However, the influence of climate change weakened with soil depth, while the opposite for vegetation growth. Relationships between SOC and forest canopy height further confirmed this 
strengthened impact of vegetation with soil depth and highlighted the carbon sink function of deep soil in mature forest. Moreover, our estimates suggested that SOC dynamics in 71% of topsoil were controlled by climate change and its coupled influence with environmental variation (CE). Meanwhile, CE and the combined influence of climate change and vegetation growth dominated the SOC dynamics in 82.05% of the first meter soil. Additionally, the national cropland topsoil organic carbon increased with a rate of 23.6 ± 7.6 g C m−2 yr−1since the 1980s, and the widely applied nitrogenous fertilizer was a key stimulus. Overall, our study extended the knowledge about the dynamics of SOC and deepened our understanding about the impacts of the primary factors.
Subtractive Aggregation for Attributed Network Anomaly Detection
Authors: Shuang Zhou, Qiaoyu Tan, Zhiming Xu, Xiao Huang, Fu-Lai Chung
Date: 2021-10-26 00:00:00
Proceeding: Proceedings of the 30th ACM International Conference on Information & Knowledge Management 2021 10
Tasks: Anomaly Detection, Graph Neural Network

Attributed network anomaly detection is essential in various networked systems. It aims to detect nodes that significantly deviate from their corresponding background. In conventional anomaly detection, the background is defined as the vast majority. But in networks, anomalies can be local and look normal when compared with the majority. While several efforts have explored to consider communities as the background, it remains challenging to learn suitable communities for effective anomaly detection. Also, the patterns of anomalies are unknown and it is nontrivial to define criteria of anomalies. To bridge the gap, in this paper, we argue that, by using appropriate models, it is sufficient to simply consider neighbor nodes as the background to detect anomalies. Correspondingly, we propose a novel abnormality-aware graph neural network (AAGNN). It utilizes subtractive aggregation to represent each node as the deviation from its neighbors (the background). Normal nodes with high confidence are employed as labels to learn a tailored hypersphere as the criterion of anomalies. Experiments demonstrate that AAGNN surpasses state-of-the-art methods significantly.
Exploring Social Influence for Recommendation - A Probabilistic Generative Model Approach
Authors: Mao Ye, Xingjie Liu, Wang-Chien Lee
Date: 2011-09-04 00:00:00
Tasks: Collaborative Filtering

In this paper, we propose a probabilistic generative model, called unified model, which naturally unifies the ideas of social influence, collaborative filtering and content-based methods for item recommendation. To address the issue of hidden social influence, we devise new algorithms to learn the model parameters of our proposal based on expectation maximization (EM). In addition to a single-machine version of our EM algorithm, we further devise a parallelized implementation on the Map-Reduce framework to process two large-scale datasets we collect. Moreover, we show that the social influence obtained from our generative models can be used for group recommendation. Finally, we conduct comprehensive experiments using the datasets crawled from last.fm and whrrl.com to validate our ideas. Experimental results show that the generative models with social influence significantly outperform those without incorporating social influence. The unified generative model proposed in this paper obtains the best performance. Moreover, our study on social influence finds that users in whrrl.com are more likely to get influenced by friends than those in last.fm. The experimental results also confirm that our social influence based group recommendation algorithm outperforms the state-of-the-art algorithms for group recommendation.
End-to-End Temporal Relation Extraction in the Clinical Domain
Authors: José Javier Saiz, Begoña Altuna
Date: 2023-04-02 00:00:00
Proceeding: Proceedings of the Text2Story'23 Workshop 2023 4
Tasks: Joint Entity and Relation Extraction, Relation, Relation Extraction, Temporal Relation Extraction

Temporal relation extraction is an important task in the clinical domain, as it allows a better understanding of the temporal context of clinical events. In this paper, we present an end-to end temporal relation extraction system for the clinical domain, using the i2b2 2012 Temporal Relation challenge as a benchmark. In our proposal, we fine-tune REBEL —a sequence-to-sequence model for general relation extraction — with temporal annotations and discharge summaries. Our proposal is then able to simultaneously extract relevant clinical entities, time expressions and the temporal relations between them. Our results demonstrate the efectiveness of this approach, achieving reasonable performance on the End-To-End track of the i2b2 2012 Challenge.
Towards the Universal Defense for Query-Based Audio Adversarial Attacks
Authors: Feng Guo, Zheng Sun, Yuxuan Chen, Lei Ju
Date: 2023-04-20 00:00:00
Tasks: Audio Fingerprint, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Recently, studies show that deep learning-based automatic speech recognition (ASR) systems are vulnerable to adversarial examples (AEs), which add a small amount of noise to the original audio examples. These AE attacks pose new challenges to deep learning security and have raised significant concerns about deploying ASR systems and devices. The existing defense methods are either limited in application or only defend on results, but not on process. In this work, we propose a novel method to infer the adversary intent and discover audio adversarial examples based on the AEs generation process. The insight of this method is based on the observation: many existing audio AE attacks utilize query-based methods, which means the adversary must send continuous and similar queries to target ASR models during the audio AE generation process. Inspired by this observation, We propose a memory mechanism by adopting audio fingerprint technology to analyze the similarity of the current query with a certain length of memory query. Thus, we can identify when a sequence of queries appears to be suspectable to generate audio AEs. Through extensive evaluation on four state-of-the-art audio AE attacks, we demonstrate that on average our defense identify the adversary intent with over 90% accuracy. With careful regard for robustness evaluations, we also analyze our proposed defense and its strength to withstand two adaptive attacks. Finally, our scheme is available out-of-the-box and directly compatible with any ensemble of ASR defense models to uncover audio AE attacks effectively without model retraining.
Überprüfung von Integritätsbedingungen in Deduktiven Datenbanken
Authors: Stefan Decker
Date: 2023-03-21 00:00:00

Advancements in computer science and AI lead to the development of larger, more complex knowledge bases. These are susceptible to contradictions, particularly when multiple experts are involved. To ensure integrity during changes, procedures are needed. This work addresses the problem from a logical programming perspective. Integrity violations can be interpreted as special operations on proofs of integrity constraints, with SLDNF proofs being the focus. We define a proof tree as a special data structure and demonstrate the implication of the existence of an SLDNF proof through such a tree. Proof trees are more convenient than SLDNF trees and allow set-oriented considerations of proofs. They also present the proof structure more clearly, enabling further applications. Using this structure, we determine a minimal set of conditions that specify when a change in the knowledge base affects the validity of an integrity constraint. Additionally, this approach allows for the reuse of large parts of the old proof when searching for a new one, which reduces the effort compared to previous approaches.
End-User Development for Artificial Intelligence: A Systematic Literature Review
Authors: Andrea Esposito, Miriana Calvano, Antonio Curci, Giuseppe Desolda, Rosa Lanzilotti, Claudia Lorusso, Antonio Piccinno
Date: 2023-04-14 00:00:00
Tasks: Systematic Literature Review

In recent years, Artificial Intelligence has become more and more relevant in our society. Creating AI systems is almost always the prerogative of IT and AI experts. However, users may need to create intelligent solutions tailored to their specific needs. In this way, AI systems can be enhanced if new approaches are devised to allow non-technical users to be directly involved in the definition and personalization of AI technologies. End-User Development (EUD) can provide a solution to these problems, allowing people to create, customize, or adapt AI-based systems to their own needs. This paper presents a systematic literature review that aims to shed the light on the current landscape of EUD for AI systems, i.e., how users, even without skills in AI and/or programming, can customize the AI behavior to their needs. This study also discusses the current challenges of EUD for AI, the potential benefits, and the future implications of integrating EUD into the overall AI development process.
NRTS: A Client-Server architecture for supporting data recording, transmission and evaluation of multidisciplinary teams during the neonatal resuscitation simulation scenario
Authors: Manuel Striani
Date: 2023-04-12 00:00:00

In this technical report, we describe Neonatal Resuscitation Training Simulator (NRTS), an Android mobile app designed to support medical experts to input, transmit and record data during a High-Fidelity Simulation course for neonatal resuscitation. This mobile app allows one to automatically send all the recorded data from "Neonatal Intensive Care Unit" (NICU) of Casale Monferrato Children's Hospital, (Italy) to a server located at the Department of Science and Technological Innovation (DiSIT), University of Piemonte Orientale (Italy). Finally, the medical instructor can view statistics on a simulation exercise that may be used during the de-briefing phase for the evaluation of multidisciplinary teams involved in the simulation scenarios.
Transformer Models for Type Inference in the Simply Typed Lambda Calculus: A Case Study in Deep Learning for Code
Authors: Brando Miranda, Avi Shinnar, Vasily Pestun, Barry Trager
Date: 2023-03-15 00:00:00

Despite a growing body of work at the intersection of deep learning and formal languages, there has been relatively little systematic exploration of transformer models for reasoning about typed lambda calculi. This is an interesting area of inquiry for two reasons. First, typed lambda calculi are the lingua franc of programming languages. A set of heuristics that relate various typed lambda calculi to effective neural architectures would provide a systematic method for mapping language features (e.g., polymorphism, subtyping, inheritance, etc.) to architecture choices. Second, transformer models are widely used in deep learning architectures applied to code, but the design and hyperparameter space for them is large and relatively unexplored in programming language applications. Therefore, we suggest a benchmark that allows us to explore exactly this through perhaps the simplest and most fundamental property of a programming language: the relationship between terms and types. Consequently, we begin this inquiry of transformer architectures for typed lambda calculi by exploring the effect of transformer warm-up and optimizer selection in the task of type inference: i.e., predicting the types of lambda calculus terms using only transformers. We find that the optimization landscape is difficult even in this simple setting. One particular experimental finding is that optimization by Adafactor converges much faster compared to the optimization by Adam and RAdam. We conjecture that such different performance of optimizers might be related to the difficulties of generalization over formally generated dataset.
Reducing Aggregate Electric Vehicle Battery Capacity through Sharing
Authors: Polina Alexeenko, Vasileios Charisopoulos
Date: 2023-04-20 00:00:00

Meeting growing demand for automotive battery resources is predicted to be costly from both economic and environmental perspectives. To minimize these costs, battery resources should be deployed as efficiently as possible. A potential source of inefficiency in battery deployment is the fact that the batteries of personal vehicles are typically much larger than needed to meet most daily mobility needs. In this paper, we consider whether battery resources can be used more efficiently in a setting where drivers, in addition to having personal vehicle batteries, have access to a shared battery resource. More precisely, we consider the problem of minimizing aggregate battery capacity in settings with and without a shared resource subject to the requirement that driver commuting needs are met with high reliability. To assess the potential for reductions in deployed battery capacity with the addition of a shared resource, we quantify the difference in deployed battery capacity with and without a shared resource in case study using real-world longitudinal mobility data from Puget Sound, Washington. We find that giving drivers access to a shared battery resource can substantially reduces deployed battery capacity. Furthermore, relative reductions in battery capacity increase with number of drivers and the level of reliability desired.
Distributed Nash Equilibrium Seeking with Stochastic Event-Triggered Mechanism
Authors: Wei Huo, Kam Fai Elvis Tsang, Yamin Yan, Karl Henrik Johansson, Ling Shi
Date: 2023-04-20 00:00:00

In this paper, we study the problem of consensus-based distributed Nash equilibrium (NE) seeking where a network of players, abstracted as a directed graph, aim to minimize their own local cost functions non-cooperatively. Considering the limited energy of players and constrained bandwidths, we propose a stochastic event-triggered algorithm by triggering each player with a probability depending on certain events, which improves communication efficiency by avoiding continuous communication. We show that the distributed algorithm with the developed event-triggered communication scheme converges to the exact NE exponentially if the underlying communication graph is strongly connected. Moreover, we prove that our proposed event-triggered algorithm is free of Zeno behavior. Finally, numerical simulations for a spectrum access game are provided to illustrate the effectiveness of the proposed mechanism by comparing it with some existing event-triggered methods.
Dynamic Security Region of Natural Gas Systems in Integrated Electricity-Gas Systems
Authors: Han Gao, Peiyao Zhao, Zhengshuo Li
Date: 2023-04-20 00:00:00
Tasks: Dimensionality Reduction

In an integrated electricity-gas system (IEGS), the tight coupling of power and natural gas systems is embodied by frequent changes in gas withdrawal from gas-fired units to provide regulation services for the power system to handle uncertainty, which may in turn endanger the secure operation of the natural gas system and ultimately affect the safety of the whole IEGS. Hence, it is necessary to accurately and efficiently evaluate the dynamic security region (DSR) of the natural gas system in the IEGS by considering the real-time dynamic characteristics of natural gas systems, which are not satisfactorily handled in state-of-the-art works. To bridge this gap, this paper first conceptionally verifies the necessity of the DSR and establishes its mathematical model. Then, a dimensionality reduction method is proposed for the efficient solution and visualization of the high-dimensional DSR evaluation model. A fast evaluation (FE) algorithm is developed to address the difficulties of the nonconvex dynamic constraints in the reduced DSR model. Finally, the necessity and notable advantages of the proposed DSR model and FE are verified based on small and relatively large test systems in comparison with common security region models and algorithms. To the best of our knowledge, this is the first paper that comprehensively presents models and efficient algorithms regarding the DSR of natural gas systems in an IEGS.