pwc-archive/papers-with-abstracts huggingface dataset:
Exploring Differences between Human Perception and Model Inference in Audio Event Recognition
Authors: Yizhou Tan, Yanru Wu, Yuanbo Hou, Xin Xu, Hui Bu, Shengchen Li, Dick Botteldooren, Mark D. Plumbley
Date: 2024-09-10 00:00:00

Audio Event Recognition (AER) traditionally focuses on detecting and identifying audio events. Most existing AER models tend to detect all potential events without considering their varying significance across different contexts. This makes the AER results detected by existing models often have a large discrepancy with human auditory perception. Although this is a critical and significant issue, it has not been extensively studied by the Detection and Classification of Sound Scenes and Events (DCASE) community because solving it is time-consuming and labour-intensive. To address this issue, this paper introduces the concept of semantic importance in AER, focusing on exploring the differences between human perception and model inference. This paper constructs a Multi-Annotated Foreground Audio Event Recognition (MAFAR) dataset, which comprises audio recordings labelled by 10 professional annotators. Through labelling frequency and variance, the MAFAR dataset facilitates the quantification of semantic importance and analysis of human perception. By comparing human annotations with the predictions of ensemble pre-trained models, this paper uncovers a significant gap between human perception and model inference in both semantic identification and existence detection of audio events. Experimental results reveal that human perception tends to ignore subtle or trivial events in the event semantic identification, while model inference is easily affected by events with noises. Meanwhile, in event existence detection, models are usually more sensitive than humans.
Robust semi-parametric signal detection in particle physics with classifiers decorrelated via optimal transport
Authors: Purvasha Chakravarti, Lucas Kania, Olaf Behnke, Mikael Kuusela, Larry Wasserman
Date: 2024-09-10 00:00:00

Searches of new signals in particle physics are usually done by training a supervised classifier to separate a signal model from the known Standard Model physics (also called the background model). However, even when the signal model is correct, systematic errors in the background model can influence supervised classifiers and might adversely affect the signal detection procedure. To tackle this problem, one approach is to use the (possibly misspecified) classifier only to perform a preliminary signal-enrichment step and then to carry out a bump hunt on the signal-rich sample using only the real experimental data. For this procedure to work, we need a classifier constrained to be decorrelated with one or more protected variables used for the signal detection step. We do this by considering an optimal transport map of the classifier output that makes it independent of the protected variable(s) for the background. We then fit a semi-parametric mixture model to the distribution of the protected variable after making cuts on the transformed classifier to detect the presence of a signal. We compare and contrast this decorrelation method with previous approaches, show that the decorrelation procedure is robust to moderate background misspecification, and analyse the power of the signal detection test as a function of the cut on the classifier.
Geometry of the Space of Partitioned Networks: A Unified Theoretical and Computational Framework
Authors: Stephen Y Zhang, Fangfei Lan, Youjia Zhou, Agnese Barbensi, Michael P H Stumpf, Bei Wang, Tom Needham
Date: 2024-09-10 00:00:00
Tasks: Dictionary Learning

Interactions and relations between objects may be pairwise or higher-order in nature, and so network-valued data are ubiquitous in the real world. The "space of networks", however, has a complex structure that cannot be adequately described using conventional statistical tools. We introduce a measure-theoretic formalism for modeling generalized network structures such as graphs, hypergraphs, or graphs whose nodes come with a partition into categorical classes. We then propose a metric that extends the Gromov-Wasserstein distance between graphs and the co-optimal transport distance between hypergraphs. We characterize the geometry of this space, thereby providing a unified theoretical treatment of generalized networks that encompasses the cases of pairwise, as well as higher-order, relations. In particular, we show that our metric is an Alexandrov space of non-negative curvature, and leverage this structure to define gradients for certain functionals commonly arising in geometric data analysis tasks. We extend our analysis to the setting where vertices have additional label information, and derive efficient computational schemes to use in practice. Equipped with these theoretical and computational tools, we demonstrate the utility of our framework in a suite of applications, including hypergraph alignment, clustering and dictionary learning from ensemble data, multi-omics alignment, as well as multiscale network alignment.
Applications of machine learning to predict seasonal precipitation for East Africa
Authors: Michael Scheuerer, Claudio Heinrich-Mertsching, Titike K. Bahaga, Masilin Gudoshava, Thordis L. Thorarinsdottir
Date: 2024-09-10 00:00:00
Tasks: Dimensionality Reduction

Seasonal climate forecasts are commonly based on model runs from fully coupled forecasting systems that use Earth system models to represent interactions between the atmosphere, ocean, land and other Earth-system components. Recently, machine learning (ML) methods are increasingly being investigated for this task where large-scale climate variability is linked to local or regional temperature or precipitation in a linear or non-linear fashion. This paper investigates the use of interpretable ML methods to predict seasonal precipitation for East Africa in an operational setting. Dimension reduction is performed by decomposing the precipitation fields via empirical orthogonal functions (EOFs), such that only the respective factor loadings need to the predicted. Indices of large-scale climate variability--including the rate of change in individual indices as well as interactions between different indices--are then used as potential features to obtain tercile forecasts from an interpretable ML algorithm. Several research questions regarding the use of data and the effect of model complexity are studied. The results are compared against the ECMWF seasonal forecasting system (SEAS5) for three seasons--MAM, JJAS and OND--over the period 1993-2020. Compared to climatology for the same period, the ECMWF forecasts have negative skill in MAM and JJAS and significant positive skill in OND. The ML approach is on par with climatology in MAM and JJAS and a significantly positive skill in OND, if not quite at the level of the OND ECMWF forecast.
Critical Features Tracking on Triangulated Irregular Networks by a Scale-Space Method
Authors: Haoan Feng, Yunting Song, Leila De Floriani
Date: 2024-09-10 00:00:00
Tasks: Visual Reasoning

The scale-space method is a well-established framework that constructs a hierarchical representation of an input signal and facilitates coarse-to-fine visual reasoning. Considering the terrain elevation function as the input signal, the scale-space method can identify and track significant topographic features across different scales. The number of scales a feature persists, called its life span, indicates the importance of that feature. In this way, important topographic features of a landscape can be selected, which are useful for many applications, including cartography, nautical charting, and land-use planning. The scale-space methods developed for terrain data use gridded Digital Elevation Models (DEMs) to represent the terrain. However, gridded DEMs lack the flexibility to adapt to the irregular distribution of input data and the varied topological complexity of different regions. Instead, Triangulated Irregular Networks (TINs) can be directly generated from irregularly distributed point clouds and accurately preserve important features. In this work, we introduce a novel scale-space analysis pipeline for TINs, addressing the multiple challenges in extending grid-based scale-space methods to TINs. Our pipeline can efficiently identify and track topologically important features on TINs. Moreover, it is capable of analyzing terrains with irregular boundaries, which poses challenges for grid-based methods. Comprehensive experiments show that, compared to grid-based methods, our TIN-based pipeline is more efficient, accurate, and has better resolution robustness.
Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes?
Authors: Jimmy Lin
Date: 2024-09-10 00:00:00
Tasks: Retrieval

Practitioners working on dense retrieval today face a bewildering number of choices. Beyond selecting the embedding model, another consequential choice is the actual implementation of nearest-neighbor vector search. While best practices recommend HNSW indexes, flat vector indexes with brute-force search represent another viable option, particularly for smaller corpora and for rapid prototyping. In this paper, we provide experimental results on the BEIR dataset using the open-source Lucene search library that explicate the tradeoffs between HNSW and flat indexes (including quantized variants) from the perspectives of indexing time, query evaluation performance, and retrieval quality. With additional comparisons between dense and sparse retrievers, our results provide guidance for today's search practitioner in understanding the design space of dense and sparse retrievers. To our knowledge, we are the first to provide operational advice supported by empirical experiments in this regard.
HierLLM: Hierarchical Large Language Model for Question Recommendation
Authors: Yuxuan Liu, Haipeng Liu, Ting Long
Date: 2024-09-10 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Sequential Decision Making

Question recommendation is a task that sequentially recommends questions for students to enhance their learning efficiency. That is, given the learning history and learning target of a student, a question recommender is supposed to select the question that will bring the most improvement for students. Previous methods typically model the question recommendation as a sequential decision-making problem, estimating students' learning state with the learning history, and feeding the learning state with the learning target to a neural network to select the recommended question from a question set. However, previous methods are faced with two challenges: (1) learning history is unavailable in the cold start scenario, which makes the recommender generate inappropriate recommendations; (2) the size of the question set is much large, which makes it difficult for the recommender to select the best question precisely. To address the challenges, we propose a method called hierarchical large language model for question recommendation (HierLLM), which is a LLM-based hierarchical structure. The LLM-based structure enables HierLLM to tackle the cold start issue with the strong reasoning abilities of LLM. The hierarchical structure takes advantage of the fact that the number of concepts is significantly smaller than the number of questions, narrowing the range of selectable questions by first identifying the relevant concept for the to-recommend question, and then selecting the recommended question based on that concept. This hierarchical structure reduces the difficulty of the recommendation.To investigate the performance of HierLLM, we conduct extensive experiments, and the results demonstrate the outstanding performance of HierLLM.
What makes a good concept anyway ?
Authors: Naren Khatwani, James Geller
Date: 2024-09-10 00:00:00
Tasks: Bayesian Optimization

A good medical ontology is expected to cover its domain completely and correctly. On the other hand, large ontologies are hard to build, hard to understand, and hard to maintain. Thus, adding new concepts (often multi-word concepts) to an existing ontology must be done judiciously. Only "good" concepts should be added; however, it is difficult to define what makes a concept good. In this research, we propose a metric to measure the goodness of a concept. We identified factors that appear to influence goodness judgments of medical experts and combined them into a single metric. These factors include concept name length (in words), concept occurrence frequency in the medical literature, and syntactic categories of component words. As an added factor, we used the simplicity of a term after mapping it into a specific foreign language. We performed Bayesian optimization of factor weights to achieve maximum agreement between the metric and three medical experts. The results showed that our metric had a 50.67% overall agreement with the experts, as measured by Krippendorff's alpha.
LexBoost: Improving Lexical Document Retrieval with Nearest Neighbors
Authors: Hrishikesh Kulkarni, Nazli Goharian, Ophir Frieder, Sean MacAvaney
Date: 2024-08-25 00:00:00
Tasks: Re-Ranking, Retrieval

Sparse retrieval methods like BM25 are based on lexical overlap, focusing on the surface form of the terms that appear in the query and the document. The use of inverted indices in these methods leads to high retrieval efficiency. On the other hand, dense retrieval methods are based on learned dense vectors and, consequently, are effective but comparatively slow. Since sparse and dense methods approach problems differently and use complementary relevance signals, approximation methods were proposed to balance effectiveness and efficiency. For efficiency, approximation methods like HNSW are frequently used to approximate exhaustive dense retrieval. However, approximation techniques still exhibit considerably higher latency than sparse approaches. We propose LexBoost that first builds a network of dense neighbors (a corpus graph) using a dense retrieval approach while indexing. Then, during retrieval, we consider both a document's lexical relevance scores and its neighbors' scores to rank the documents. In LexBoost this remarkably simple application of the Cluster Hypothesis contributes to stronger ranking effectiveness while contributing little computational overhead (since the corpus graph is constructed offline). The method is robust across the number of neighbors considered, various fusion parameters for determining the scores, and different dataset construction methods. We also show that re-ranking on top of LexBoost outperforms traditional dense re-ranking and leads to results comparable with higher-latency exhaustive dense retrieval.
CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems
Authors: Jin-Duk Park, Kyung-Min Kim, Won-Yong Shin
Date: 2024-08-25 00:00:00
Tasks: Collaborative Filtering, Continual Learning, Kolmogorov-Arnold Networks, Recommendation Systems

Collaborative filtering (CF) remains essential in recommender systems, leveraging user--item interactions to provide personalized recommendations. Meanwhile, a number of CF techniques have evolved into sophisticated model architectures based on multi-layer perceptrons (MLPs). However, MLPs often suffer from catastrophic forgetting, and thus lose previously acquired knowledge when new information is learned, particularly in dynamic environments requiring continual learning. To tackle this problem, we propose CF-KAN, a new CF method utilizing Kolmogorov-Arnold networks (KANs). By learning nonlinear functions on the edge level, KANs are more robust to the catastrophic forgetting problem than MLPs. Built upon a KAN-based autoencoder, CF-KAN is designed in the sense of effectively capturing the intricacies of sparse user--item interactions and retaining information from previous data instances. Despite its simplicity, our extensive experiments demonstrate 1) CF-KAN's superiority over state-of-the-art methods in recommendation accuracy, 2) CF-KAN's resilience to catastrophic forgetting, underscoring its effectiveness in both static and dynamic recommendation scenarios, and 3) CF-KAN's edge-level interpretation facilitating the explainability of recommendations.
CSRec: Rethinking Sequential Recommendation from A Causal Perspective
Authors: Xiaoyu Liu, Jiaxin Yuan, YuHang Zhou, Jingling Li, Furong Huang, Wei Ai
Date: 2024-08-23 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions. Most existing approaches frame the task as sequential prediction based on users' historical purchase records. While effective in capturing users' natural preferences, this formulation falls short in accurately modeling actual recommendation scenarios, particularly in accounting for how unsuccessful recommendations influence future purchases. Furthermore, the impact of the RecSys itself on users' decisions has not been appropriately isolated and quantitatively analyzed. To address these challenges, we propose a novel formulation of sequential recommendation, termed Causal Sequential Recommendation (CSRec). Instead of predicting the next item in the sequence, CSRec aims to predict the probability of a recommended item's acceptance within a sequential context and backtrack how current decisions are made. Critically, CSRec facilitates the isolation of various factors that affect users' final decisions, especially the influence of the recommender system itself, thereby opening new avenues for the design of recommender systems. CSRec can be seamlessly integrated into existing methodologies. Experimental evaluations on both synthetic and real-world datasets demonstrate that the proposed implementation significantly improves upon state-of-the-art baselines.
Sensitivity analysis and optimal control of COVID-19 dynamics based on SEIQR model
Authors: Takasar Hussain a, Muhammad Ozair a, Farhad Ali b, Sajid ur Rehman b, Taghreed A. Assiri c, Emad E. Mahmoud d, *
Date: 2021-02-04 00:00:00
Proceeding: ScienceDirect 2021 2
Tasks: Sensitivity

It is of great curiosity to observe the effects of prevention methods and the magnitudes of the outbreak including 
epidemic prediction, at the onset of an epidemic. To deal with COVID-19 Pandemic, an SEIQR model has been 
designed. Analytical study of the model consists of the calculation of the basic reproduction number and the 
constant level of disease absent and disease present equilibrium. The model also explores number of cases and 
the predicted outcomes are in line with the cases registered. By parameters calibration, new cases in Pakistan are 
also predicted. The number of patients at the current level and the permanent level of COVID-19 cases are also 
calculated analytically and through simulations. The future situation has also been discussed, which could 
happen if precautionary restrictions are adopted.
Distill-DBDGAN: Knowledge Distillation and Adversarial Learning Framework for Defocus Blur Detection
Authors: Sankaraganesh Jonna, Moushumi Medhi, Rajiv Ranjan Sahay
Date: 2023-02-01 00:00:00
Proceeding: ACM Transactions on Multimedia Computing, Communications, and Applications 2023 2
Tasks: Defocus Blur Detection, Generative Adversarial Network, Knowledge Distillation

Defocus blur detection (DBD) aims to segment the blurred regions from a given image affected by defocus blur. It is a crucial pre-processing step for various computer vision tasks. With the increasing popularity of small mobile devices, there is a need for a computationally efficient method to detect defocus blur accurately. We propose an efficient defocus blur detection method that estimates the probability of each pixel being focused or blurred in resource-constraint devices. Despite remarkable advances made by the recent deep learning-based methods, they still suffer from several challenges such as background clutter, scale sensitivity, indistinguishable low-contrast focused regions from out-of-focus blur, and especially high computational cost and memory requirement. To address the first three challenges, we develop a novel deep network that efficiently detects blur map from the input blurred image. Specifically, we integrate multi-scale features in the deep network to resolve the scale ambiguities and simultaneously modeled the non-local structural correlations in the high-level blur features. To handle the last two issues, we eventually frame our DBD algorithm to perform knowledge distillation by transferring information from the larger teacher network to a compact student network. All the networks are adversarially trained in an end-to-end manner to enforce higher order consistencies between the output and the target distributions. Experimental results demonstrate the state-of-the-art performance of the larger teacher network, while our proposed lightweight DBD model imitates the output of the teacher network without significant loss in accuracy. The codes, pre-trained model weights, and the results will be made publicly available.
Real-Time Video Surveillance Based Structural Health Monitoring of Civil Structures Using Artificial Neural Network
Authors: Moushumi Medhi, Aradhana Dandautiya, Jagdish Lal Raheja
Date: 2019-07-01 00:00:00
Proceeding: Journal of Nondestructive Evaluation 2019 7
Tasks: Structural Health Monitoring

Modern world’s incessantly increasing outdoor traffic load has eventually led to structural health concern and continuous
health monitoring of large scale civil structures such as bridges, roads, highways, etc. In this paper, we propose a computer
vision based non-destructive structural health monitoring (SHM) method using high speed camera system combined with
the brilliance of artificial intelligence. A number of appreciable SHM techniques had been reported that utilizes wired or
wireless smart sensors, but the use of nondestructive techniques, such as, digital high speed imaging were rarely employed
for detection of dynamic vibrations of civil structures. In the current research, we have developed a high speed video imaging
based structural health monitoring system that utilizes blob detection based motion tracking algorithm. It provides factual
information regarding localization and displacement of the target object or an existing feature in the civil structure. The
modal parameters were subsequently extracted to analyze the level of severity of structural damage within the civil structures.
Also, an artificial neural network is trained to infer the qualitative characteristics of structural vibrations based on vibration
intensity and the network inferences can be correlated with the conditions of the structure. The efficacy of our vision system
in remote measurement of dynamic displacements was demonstrated through a shaking table and a slip desk experiment. The
experimental results demonstrate real-time output with satisfactory performance.