pwc-archive/papers-with-abstracts huggingface dataset:
Sequential Gibbs Posteriors with Applications to Principal Component Analysis
Authors: Steven Winter, Omar Melikechi, David B. Dunson
Date: 2023-10-19 00:00:00
Tasks: Bayesian Inference, Uncertainty Quantification

Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter weighting information in the loss relative to the prior and providing a control of posterior uncertainty. Gibbs posteriors provide a principled framework for likelihood-free Bayesian inference, but in many situations, including a single tuning parameter inevitably leads to poor uncertainty quantification. In particular, regardless of the value of the parameter, credible regions have far from the nominal frequentist coverage even in large samples. We propose a sequential extension to Gibbs posteriors to address this problem. We prove the proposed sequential posterior exhibits concentration and a Bernstein-von Mises theorem, which holds under easy to verify conditions in Euclidean space and on manifolds. As a byproduct, we obtain the first Bernstein-von Mises theorem for traditional likelihood-based Bayesian posteriors on manifolds. All methods are illustrated with an application to principal component analysis.
Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear Regression
Authors: Ayoub El Hanchi, Murat A. Erdogdu
Date: 2023-10-19 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: regression

We study the performance of empirical risk minimization on the $p$-norm linear regression problem for $p \in (1, \infty)$. We show that, in the realizable case, under no moment assumptions, and up to a distribution-dependent constant, $O(d)$ samples are enough to exactly recover the target. Otherwise, for $p \in [2, \infty)$, and under weak moment assumptions on the target and the covariates, we prove a high probability excess risk bound on the empirical risk minimizer whose leading term matches, up to a constant that depends only on $p$, the asymptotically exact rate. We extend this result to the case $p \in (1, 2)$ under mild assumptions that guarantee the existence of the Hessian of the risk at its minimizer.
Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm
Authors: Anja Zgodic, Ray Bai, Jiajia Zhang, Peter Olejua, Alexander C. McLain
Date: 2023-10-18 00:00:00
Tasks: Variable Selection

High-dimensional longitudinal data is increasingly used in a wide range of scientific studies. To properly account for dependence between longitudinal observations, statistical methods for high-dimensional linear mixed models (LMMs) have been developed. However, few packages implementing these high-dimensional LMMs are available in the statistical software R. Additionally, some packages suffer from scalability issues. This work presents an efficient and accurate Bayesian framework for high-dimensional LMMs. We use empirical Bayes estimators of hyperparameters for increased flexibility and an Expectation-Conditional-Minimization (ECM) algorithm for computationally efficient maximum a posteriori probability (MAP) estimation of parameters. The novelty of the approach lies in its partitioning and parameter expansion as well as its fast and scalable computation. We illustrate Linear Mixed Modeling with PaRtitiOned empirical Bayes ECM (LMM-PROBE) in simulation studies evaluating fixed and random effects estimation along with computation time. A real-world example is provided using data from a study of lupus in children, where we identify genes and clinical factors associated with a new lupus biomarker and predict the biomarker over time. Supplementary materials are available online.
Unified Browsing Models for Linear and Grid Layouts
Authors: Amifa Raj, Michael Ekstrand
Date: 2023-10-19 00:00:00
Tasks: Fairness, Position

Many information access systems operationalize their results in terms of rankings, which are then displayed to users in various ranking layouts such as linear lists or grids. User interaction with a retrieved item is highly dependent on the item's position in the layout, and users do not provide similar attention to every position in ranking (under any layout model). User attention is an important component in the evaluation process of ranking, due to its use in effectiveness metrics that estimate utility as well as fairness metrics that evaluate ranking based on social and ethical concerns. These metrics take user browsing behavior into account in their measurement strategies to estimate the attention the user is likely to provide to each item in ranking. Research on understanding user browsing behavior has proposed several user browsing models, and further observed that user browsing behavior differs with different ranking layouts. However, the underlying concepts of these browsing models are often similar, including varying components and parameter settings. We seek to leverage that similarity to represent multiple browsing models in a generalized, configurable framework which can be further extended to more complex ranking scenarios. In this paper, we describe a probabilistic user browsing model for linear rankings, show how they can be configured to yield models commonly used in current evaluation practice, and generalize this model to also account for browsing behaviors in grid-based layouts. This model provides configurable framework for estimating the attention that results from user browsing activity for a range of IR evaluation and measurement applications in multiple formats, and also identifies parameters that need to be estimated through user studies to provide realistic evaluation beyond ranked lists.
Auto Search Indexer for End-to-End Document Retrieval
Authors: Tianchi Yang, Minghui Song, Zihan Zhang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang
Date: 2023-10-19 00:00:00
Tasks: Decoder, Retrieval

Generative retrieval, which is a new advanced paradigm for document retrieval, has recently attracted research interests, since it encodes all documents into the model and directly generates the retrieved documents. However, its power is still underutilized since it heavily relies on the "preprocessed" document identifiers (docids), thus limiting its retrieval performance and ability to retrieve new documents. In this paper, we propose a novel fully end-to-end retrieval paradigm. It can not only end-to-end learn the best docids for existing and new documents automatically via a semantic indexing module, but also perform end-to-end document retrieval via an encoder-decoder-based generative model, namely Auto Search Indexer (ASI). Besides, we design a reparameterization mechanism to combine the above two modules into a joint optimization framework. Extensive experimental results demonstrate the superiority of our model over advanced baselines on both public and industrial datasets and also verify the ability to deal with new documents.
On Identifying Points of Semantic Shift Across Domains
Authors: Hyung Wook Choi, Mat Kelly
Date: 2023-10-18 00:00:00

The semantics used for particular terms in an academic field organically evolve over time. Tracking this evolution through inspection of published literature has either been from the perspective of Linguistic scholars or has concentrated the focus of term evolution within a single domain of study. In this paper, we performed a case study to identify semantic evolution across different domains and identify examples of inter-domain semantic shifts. We initially used keywords as the basis of our search and executed an iterative process of following citations to find the initial mention of the concepts in the field. We found that a select set of keywords like ``semaphore'', ``polymorphism'', and ``ontology'' were mentioned within Computer Science literature and tracked the seminal study that borrowed those terms from original fields by citations. We marked these events as semantic evolution points. Through this manual investigation method, we can identify term evolution across different academic fields. This study reports our initial findings that will seed future automated and computational methods of incorporating concepts from additional academic fields.
Retrieve-Cluster-Summarize: An Alternative to End-to-End Training for Query-specific Article Generation
Authors: Connor Lennox, Sumanta Kashyapi, Laura Dietz
Date: 2023-10-18 00:00:00
Tasks: Articles, Retrieval

Query-specific article generation is the task of, given a search query, generate a single article that gives an overview of the topic. We envision such articles as an alternative to presenting a ranking of search results. While generative Large Language Models (LLMs) like chatGPT also address this task, they are known to hallucinate new information, their models are secret, hard to analyze and control. Some generative LLMs provide supporting references, yet these are often unrelated to the generated content. As an alternative, we propose to study article generation systems that integrate document retrieval, query-specific clustering, and summarization. By design, such models can provide actual citations as provenance for their generated text. In particular, we contribute an evaluation framework that allows to separately trains and evaluate each of these three components before combining them into one system. We experimentally demonstrate that a system comprised of the best-performing individual components also obtains the best F-1 overall system quality.
Spans, Not Tokens: A Span-Centric Model for Multi-Span Reading Comprehension
Authors: mous ACL submission
Date: 2022-12-17 00:00:00
Proceeding: 2022 2022 12
Tasks: Reading Comprehension, TAG

Multi-span reading comprehension (MSRC) requires machines to extract multiple non-contiguous spans from a given context to answer a question. Existing MSRC methods either predict the positions of the start and end tokens of answer spans, or predict the BIO tag of each token. Such token-centric paradigms can hardly capture dependencies among spans which are critical to MSRC. In this paper, we propose a span-centric scheme where spans, as opposed to tokens, are directly represented and scored to qualify as answers. Thanks to the explicit representation of spans in the scheme, our implementation called SpanQualifier beneficially models intra-span and inter-span interactions. Our extensive experiments on three MSRC datasets demonstrate the effectiveness of our span-centric scheme and show that SpanQualifier achieves state-of-the-art results.
A random-walk based epidemiological model
Authors: Andrew Chu, Greg Huber, Aaron McGeever, Boris Veytsman, and David Yllanes
Date: 2020-11-06 00:00:00
Proceeding: november 6 2020 11
Tasks: 2D Human Pose Estimation, model

Random walkers on a two-dimensional square lattice are used to explore the spatio-temporal growth of an epidemic. We have
found that a simple random-walk system generates nontrivial dynamics compared with traditional well-mixed models. Phase
diagrams characterizing the long-term behaviors of the epidemics are calculated numerically. The phase boundary separating
those sets of parameters leading to outbreaks dying out and those leading to indefinite growth is mapped out in detail. The
functional dependence of the basic reproductive number R0 on the model’s defining parameters reveals the role of spatial
fluctuations and leads to a novel expression for R0. Special attention is given to simulations of inter-regional transmission of
the contagion. The attack rate and the (growing) radius of gyration of the affected zones are used as measures of the severity
of the outbreaks, in cases where R0 is not sufficiently prescriptive to chart the epidemic dynamics.
Masked Relation Learning for DeepFake Detection
Authors: Ziming Yang, Jian Liang, Yuting Xu, Xiao-Yu Zhang, Ran He
Date: 2023-02-27 00:00:00
Proceeding: 2023 2023 2
Tasks: Binary Classification, DeepFake Detection, Face Swapping, Graph Classification, Relation

Abstract— DeepFake detection aims to differentiate falsified
faces from real ones. Most approaches formulate it as a binary
classification problem by solely mining the local artifacts and
inconsistencies of face forgery, which neglect the relation across
local regions. Although several recent works explore local relation
learning for DeepFake detection, they overlook the propagation
of relational information and lead to limited performance gains.
To address these issues, this paper provides a new perspective
by formulating DeepFake detection as a graph classification
problem, in which each facial region corresponds to a vertex.
But relational information with large redundancy hinders the
expressiveness of graphs. Inspired by the success of masked
modeling, we propose Masked Relation Learning which decreases
the redundancy to learn informative relational features. Specifically, a spatiotemporal attention module is exploited to learn the
attention features of multiple facial regions. A relation learning
module masks partial correlations between regions to reduce
redundancy and then propagates the relational information
across regions to capture the irregularity from a global view of
the graph. We empirically discover that a moderate masking rate
(e.g., 50%) brings the best performance gain. Experiments verify
the effectiveness of Masked Relation Learning and demonstrate
that our approach outperforms the state of the art by 2% AUC on
the cross-dataset DeepFake video detection. Code will be available
at https://github.com/zimyang/MaskRelation.
Index Terms— Multimedia forensics, DeepFake detection,
masked learning, relation feature.
Establishment of NLP-Based Greenwashing Pattern Detection Service
Authors: Ji Sue Kim, Jun Bo Sim, Yoon Ju Kim, Min Kyu Park, Se Jong Oh, and Ill Chul Doo
Date: 2023-06-02 00:00:00
Proceeding: Advances in Computer Science and Ubiquitous Computing 2023 6
Tasks: Sentence

This study aims to establish a search service that provides greenwashing 
pattern detection and infographic of enterprises through AI-based NLP technology. 
The purpose of it is to provide decision indicators by checking data on greenwashing trends of companies by users, including investment institutions and general 
consumers. We used a deep learning model to adopted evaluation factors to score 
the ‘greenwashing’ and collected the title of media data with active monitoring of 
companies based on those factors. In addition, construct greenwashing sentence 
discrimination model and a corporate greenwashing scoring model using the BERT 
model. As a result, a service provided scores using data collected through its keywords 
through visualization and the process of calculation when users input the name of 
the company.
Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy
Authors: Annys, A., Jannis, D. & Verbeeck, J.
Date: 2023-08-22 00:00:00
Proceeding: Scientific Reports 2023 8

Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner. One of the persisting limitations of EELS is the requirement for manual identification of core-loss edges and their corresponding elements. This can be especially bothersome in spectrum imaging, where a large amount of spectra are recorded when spatially scanning over a sample area. This paper introduces a synthetic dataset with 736,000 labeled EELS spectra, computed from available generalized oscillator strength tables, that represents 107 K, L, M or N core-loss edges and 80 chemical elements. Generic lifetime broadened peaks are used to mimic the fine structure due to band structure effects present in experimental core-loss edges. The proposed dataset is used to train and evaluate a series of neural network architectures, being a multilayer perceptron, a convolutional neural network, a U-Net, a residual neural network, a vision transformer and a compact convolutional transformer. An ensemble of neural networks is used to further increase performance. The ensemble network is used to demonstrate fully automated elemental mapping in a spectrum image, both by directly mapping the predicted elemental content and by using the predicted content as input for a physical model-based mapping.
Evolutionary stability of cooperation by the leading eight norms in indirect reciprocity under noisy and private assessment
Authors: Yuma Fujimoto, Hisashi Ohtsuki
Date: 2023-10-19 00:00:00

Indirect reciprocity is a mechanism that explains large-scale cooperation in human societies. In indirect reciprocity, an individual chooses whether or not to cooperate with another based on reputation information, and others evaluate the action as good or bad. Under what evaluation rule (called ``social norm'') cooperation evolves has long been of central interest in the literature. It has been reported that if individuals can share their evaluations (i.e., public reputation), social norms called ``leading eight'' can be evolutionarily stable. On the other hand, when they cannot share their evaluations (i.e., private assessment), the evolutionary stability of cooperation is still in question. To tackle this problem, we create a novel method to analyze the reputation structure in the population under private assessment. Specifically, we characterize each individual by two variables, ``goodness'' (what proportion of the population considers the individual as good) and ``self-reputation'' (whether an individual thinks of him/herself as good or bad), and analyze the stochastic process of how these two variables change over time. We discuss evolutionary stability of each of the leading eight social norms by studying the robustness against invasions of unconditional cooperators and defectors. We identify key pivots in those social norms for establishing a high level of cooperation or stable cooperation against mutants. Our finding gives an insight into how human cooperation is established in a real-world society.
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System
Authors: Tae Jin Park, He Huang, Ante Jukic, Kunal Dhawan, Krishna C. Puvvada, Nithin Koluguri, Nikolay Karpov, Aleksandr Laptev, Jagadeesh Balam, Boris Ginsburg
Date: 2023-10-18 00:00:00
Tasks: Automatic Speech Recognition, speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

We present the NVIDIA NeMo team's multi-channel speech recognition system for the 7th CHiME Challenge Distant Automatic Speech Recognition (DASR) Task, focusing on the development of a multi-channel, multi-speaker speech recognition system tailored to transcribe speech from distributed microphones and microphone arrays. The system predominantly comprises of the following integral modules: the Speaker Diarization Module, Multi-channel Audio Front-End Processing Module, and the ASR Module. These components collectively establish a cascading system, meticulously processing multi-channel and multi-speaker audio input. Moreover, this paper highlights the comprehensive optimization process that significantly enhanced our system's performance. Our team's submission is largely based on NeMo toolkits and will be publicly available.
Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation
Authors: Tae Jin Park, He Huang, Coleman Hooper, Nithin Koluguri, Kunal Dhawan, Ante Jukic, Jagadeesh Balam, Boris Ginsburg
Date: 2023-10-18 00:00:00
Tasks: Action Detection, Activity Detection, speaker-diarization, Speaker Diarization, Synthetic Data Generation

We introduce a sophisticated multi-speaker speech data simulator, specifically engineered to generate multi-speaker speech recordings. A notable feature of this simulator is its capacity to modulate the distribution of silence and overlap via the adjustment of statistical parameters. This capability offers a tailored training environment for developing neural models suited for speaker diarization and voice activity detection. The acquisition of substantial datasets for speaker diarization often presents a significant challenge, particularly in multi-speaker scenarios. Furthermore, the precise time stamp annotation of speech data is a critical factor for training both speaker diarization and voice activity detection. Our proposed multi-speaker simulator tackles these problems by generating large-scale audio mixtures that maintain statistical properties closely aligned with the input parameters. We demonstrate that the proposed multi-speaker simulator generates audio mixtures with statistical properties that closely align with the input parameters derived from real-world statistics. Additionally, we present the effectiveness of speaker diarization and voice activity detection models, which have been trained exclusively on the generated simulated datasets.
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
Authors: Anwar Said, Mudassir Shabbir, Tyler Derr, Waseem Abbas, Xenofon Koutsoukos
Date: 2023-10-10 00:00:00
Tasks: Graph Classification, Graph Embedding, Node Classification

Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks. However, for many network applications, such node-level information may be missing or unreliable, thereby limiting the applicability and efficacy of GNNs. To address this limitation, we present a novel approach denoted as Ego-centric Spectral subGraph Embedding Augmentation (ESGEA), which aims to enhance and design node features, particularly in scenarios where information is lacking. Our method leverages the topological structure of the local subgraph to create topology-aware node features. The subgraph features are generated using an efficient spectral graph embedding technique, and they serve as node features that capture the local topological organization of the network. The explicit node features, if present, are then enhanced with the subgraph embeddings in order to improve the overall performance. ESGEA is compatible with any GNN-based architecture and is effective even in the absence of node features. We evaluate the proposed method in a social network graph classification task where node attributes are unavailable, as well as in a node classification task where node features are corrupted or even absent. The evaluation results on seven datasets and eight baseline models indicate up to a 10% improvement in AUC and a 7% improvement in accuracy for graph and node classification tasks, respectively.
A Centralized Voltage Controller for Offshore Wind Plants: NY State Grid Case Study
Authors: Lin Zhu, Bruno Leonardi, Aboutaleb Haddadi, Sudipta Dutta, Alberto Del Rosso, Victor Paduani, Hossein Hooshyar
Date: 2023-10-19 00:00:00

This paper proposes a centralized multi-plant reactive power and voltage controller to support voltage control in the interconnected onshore power system. This controller utilizes a hierarchical control structure consisting of a master controller and multiple slave controllers. To validate the proposed method, a realistic planning case of the New York State grid is created for the year 2035, in which nearly 9,500 MW AC or DC connected offshore wind resources are modeled. The performance of the proposed controller is analyzed in the large-scale model under three realistic disturbance scenarios: generator loss, load ramps, and load steps. Results demonstrate how the controller can adequately perform under disturbances to share reactive support proportionally among plants based on their ratings and improve grid voltage stability margins.