pwc-archive/papers-with-abstracts huggingface dataset:
Transport map unadjusted Langevin algorithms: learning and discretizing perturbed samplers
Authors: Benjamin J. Zhang, Youssef M. Marzouk, Konstantinos Spiliopoulos
Date: 2023-02-14 00:00:00

Langevin dynamics are widely used in sampling high-dimensional, non-Gaussian distributions whose densities are known up to a normalizing constant. In particular, there is strong interest in unadjusted Langevin algorithms (ULA), which directly discretize Langevin dynamics to estimate expectations over the target distribution. We study the use of transport maps that approximately normalize a target distribution as a way to precondition and accelerate the convergence of Langevin dynamics. We show that in continuous time, when a transport map is applied to Langevin dynamics, the result is a Riemannian manifold Langevin dynamics (RMLD) with metric defined by the transport map. We also show that applying a transport map to an irreversibly-perturbed ULA results in a geometry-informed irreversible perturbation (GiIrr) of the original dynamics. These connections suggest more systematic ways of learning metrics and perturbations, and also yield alternative discretizations of the RMLD described by the map, which we study. Under appropriate conditions, these discretized processes can be endowed with non-asymptotic bounds describing convergence to the target distribution in 2-Wasserstein distance. Illustrative numerical results complement our theoretical claims.
Online Detection of Changes in Moment-Based Projections: When to Retrain Deep Learners or Update Portfolios?
Authors: Ansgar Steland
Date: 2023-02-14 00:00:00
Tasks: Time Series, Time Series Analysis

Sequential monitoring of high-dimensional nonlinear time series is studied for a projection of the second-moment matrix, a problem interesting in its own right and specifically arising in finance and deep learning. Open-end as well as closed-end monitoring is studied under mild assumptions on the training sample and the observations of the monitoring period. Asymptotics is based on Gaussian approximations of projected partial sums allowing for an estimated projection vector. Estimation is studied both for classical non-$\ell_0$-sparsity as well as under sparsity. For the case that the optimal projection depends on the unknown covariance matrix, hard- and soft-thresholded estimators are studied. Applications in finance and training of deep neural networks are discussed. The proposed detectors typically allow to reduce dramatically the required computational costs as illustrated by monitoring synthetic data.
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
Authors: Andreas Krämer, Aleksander P. Durumeric, Nicholas E. Charron, Yaoyi Chen, Cecilia Clementi, Frank Noé
Date: 2023-02-14 00:00:00
Tasks: All

Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average. We show that there is flexibility in how to map all-atom forces to the CG representation, and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force-fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins Chignolin and Tryptophan Cage and published as open-source code.
Detection-Recovery Gap for Planted Dense Cycles
Authors: Cheng Mao, Alexander S. Wein, Shenduo Zhang
Date: 2023-02-13 00:00:00

Planted dense cycles are a type of latent structure that appears in many applications, such as small-world networks in social sciences and sequence assembly in computational biology. We consider a model where a dense cycle with expected bandwidth $n \tau$ and edge density $p$ is planted in an Erd\H{o}s-R\'enyi graph $G(n,q)$. We characterize the computational thresholds for the associated detection and recovery problems for the class of low-degree polynomial algorithms. In particular, a gap exists between the two thresholds in a certain regime of parameters. For example, if $n^{-3/4} \ll \tau \ll n^{-1/2}$ and $p = C q = \Theta(1)$ for a constant $C>1$, the detection problem is computationally easy while the recovery problem is hard for low-degree algorithms.
Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections
Authors: Srishti Palani, Aakanksha Naik, Doug Downey, Amy X. Zhang, Jonathan Bragg, Joseph Chee Chang
Date: 2023-02-13 00:00:00
Tasks: Descriptive, Navigate, Re-Ranking

Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes increasingly challenging. Meanwhile, authors summarize prior research in papers' related work sections, though this is scoped to support a single paper. A formative study found that while reading multiple related work paragraphs helps overview a topic, it is hard to navigate overlapping and diverging references and research foci. In this work, we design a system, Relatedly, that scaffolds exploring and reading multiple related work paragraphs on a topic, with features including dynamic re-ranking and highlighting to spotlight unexplored dissimilar information, auto-generated descriptive paragraph headings, and low-lighting of redundant information. From a within-subjects user study (n=15), we found that scholars generate more coherent, insightful, and comprehensive topic outlines using Relatedly compared to a baseline paper list.
Enhancing User Personalization in Conversational Recommenders
Authors: Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee
Date: 2023-02-13 00:00:00
Tasks: Attribute, Conversational Recommendation

Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. Through extensive experiments on four frequently used datasets, we find the proposed framework not only outperforms all the state-of-the-art conversational recommenders (in terms of both recommendation performance and conversation efficiency), but also provides a more personalized experience for the user under the proposed multi-groundtruth multi-round conversational recommendation setting.
The Heritage Digital Twin: a bicycle made for two. The integration of digital methodologies into cultural heritage research
Authors: Franco Niccolucci, Béatrice Markhoff, Maria Theodoridou, Achille Felicetti, Sorin Hermon
Date: 2023-02-03 00:00:00

The paper concerns the definition of a novel ontology for cultural heritage based on the concept of digital twin. The ontology, called Heritage Digital Twin ontology, is a compatible extension of the well-known CIDOC CRM ISO standard for cultural heritage documentation and incorporates all the different documentation systems presently in use for cultural heritage documentation. In the authors' view, it supports documentation interoperability at a higher level than the ones currently in use and enables effective cooperation among different users.
Adversarial Learning with Mask Reconstruction for Text-Guided Image Inpainting
Authors: Xingcai Wu, Yucheng Xie, Jiaqi Zeng, Zhenguo Yang, Yi Yu, Qing Li, and Wenyin Liu
Date: 2021-07-28 00:00:00
Proceeding: Conference 2021 7
Tasks: Image Inpainting, Sentence

Text-guided image inpainting aims to complete the corrupted patches coherent with both visual and textual context. On one hand, existing works focus on surrounding pixels of the corrupted patches without considering the objects in the image, resulting in the characteristics of objects described in text being painted on non-object regions. On the other hand, the redundant information in text may distract the generation of objects of interest in the restored image. In this paper, we propose an adversarial learning framework with mask reconstruction (ALMR) for image inpainting with textual guidance, which consists of a two-stage generator and dual discriminators. The two-stage generator aims to restore coarse-grained and fine-grained images, respectively. In particular, we devise a dual-attention module (DAM) to incorporate the word-level and sentence-level textual features as guidance on generating the coarse-grained and fine-grained details in the two stages. Furthermore, we design a mask reconstruction module (MRM) to penalize the restoration of the objects of interest with the given textual descriptions about the objects. For adversarial training, we exploit global and local discriminators for the whole image and corrupted patches, respectively. Extensive experiments conducted on CUB-200-2011, Oxford-102 and CelebA-HQ show the outperformance of the proposed ALMR (e.g., FID value is reduced from 29.69 to 14.69 compared with the state-of-the-art approach on CUB-200-2011). Codes are available at \href{https://github.com/GaranWu/ALMR}
Entropy-Driven Mixed-Precision Quantization for Deep Network Design
Authors: Zhenhong Sun, Ce Ge, Junyan Wang, Ming Lin, Hesen Chen, Hao Li, Xiuyu Sun
Date: 2022-11-28 00:00:00
Proceeding: Conference on Neural Information Processing Systems 2022 11
Tasks: CPU, Face Detection, GPU, Hardware Aware Neural Architecture Search, Image Classification, Neural Architecture Search, Quantization

Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is challenging due to the limited computational resources, such as limited SRAM memory and Flash storage. Previous works re-design a small network for IoT devices, and then compress the network size by mixed-precision quantization. This two-stage procedure cannot optimize the architecture and the corresponding quantization jointly, leading to sub-optimal tiny deep models. In this work, we propose a one-stage solution that optimizes both jointly and automatically. The key idea of our approach is to cast the joint architecture design and quantization as an Entropy Maximization process. Particularly, our algorithm automatically designs a tiny deep model such that: 1) Its representation capacity measured by entropy is maximized under the given computational budget; 2) Each layer is assigned with a proper quantization precision; 3) The overall design loop can be done on CPU, and no GPU is required. More impressively, our method can directly search high-expressiveness architecture for IoT devices within less than half a CPU hour. Extensive experiments on three widely adopted benchmarks, ImageNet, VWW and WIDER FACE, demonstrate that our method can achieve the state-of-the-art performance in the tiny deep model regime. Code and pre-trained models are available at https://github.com/alibaba/lightweight-neural-architecture-search.
GDFM: Gene Vectors Embodied Deep Attentional Factorization Machines for Interaction prediction
Authors: Sameen Mansha, Tayyab Khalid, Faisal Kamiran, Masroor Hussain, Syed Fawad Hussain, and Hongzhi Yin.
Date: 2021-11-21 00:00:00
Proceeding: Proceedings of the 30th ACM International Conference on Information and Knowledge Management 2021 11
Tasks: Gene Interaction Prediction

Gene Network Graphs (GNGs) are comprised of biomedical data.
Deriving structural information from these graphs remains a prime
area of research in the domain of biomedical and health informatics.
In this paper, we propose Gene Vectors Embodied Deep Attentional
Factorization Machines (GDFMs) for the gene to gene interaction
prediction.We first initialize GDFM with vector embeddings
learned from gene locality configuration and an expression equivalence
criterion that preserves their innate similar traits. GDFM
uses an attention-based mechanism that manipulates different positions,
to learn the representation of sequence, before calculating
the pairwise factorized interactions. We further use hidden layers,
batch normalization, and dropout to stabilize the performance of
our deep structured architecture. An extensive comparison with
several state-of-the-art approaches, using Ecoli and Yeast datasets
for gene-gene interaction prediction shows the significance of our
proposed framework.
Refining a -nearest neighbor graph for a computationally efficient spectral clustering
Authors: Mashaan Alshammari, John Stavrakakis, Masahiro Takatsuka
Date: 2021-02-06 00:00:00
Proceeding: Pattern Recognition 2021 2
Tasks: Clustering, Computational Efficiency, Graph Clustering, Graph Embedding, graph partitioning, Quantization, Spectral Graph Clustering

Spectral clustering became a popular choice for data clustering for its ability of uncovering clusters of different shapes. However, it is not always preferable over other clustering methods due to its computational demands. One of the effective ways to bypass these computational demands is to perform spectral clustering on a subset of points (data representatives) then generalize the clustering outcome, this is known as approximate spectral clustering (ASC). ASC uses sampling or quantization to select data representatives. This makes it vulnerable to 1) performance inconsistency (since these methods have a random step either in initialization or training), 2) local statistics loss (because the pairwise similarities are extracted from data representatives instead of data points). We proposed a refined version of 
-nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency. Local statistics were exploited to keep the edges that do not violate the intra-cluster distances and nullify all other edges in the 
-nearest neighbor graph. We also introduced an optional step to automatically select the number of clusters 
. The proposed method was tested on synthetic and real datasets. Compared to ASC methods, the proposed method delivered a consistent performance despite significant reduction of edges
Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?
Authors: Gengmo Zhou, Zhifeng Gao, Zhewei Wei, Hang Zheng, Guolin Ke
Date: 2023-02-14 00:00:00
Tasks: Deep Learning, Drug Discovery

Molecular conformation generation (MCG) is a fundamental and important problem in drug discovery. Many traditional methods have been developed to solve the MCG problem, such as systematic searching, model-building, random searching, distance geometry, molecular dynamics, Monte Carlo methods, etc. However, they have some limitations depending on the molecular structures. Recently, there are plenty of deep learning based MCG methods, which claim they largely outperform the traditional methods. However, to our surprise, we design a simple and cheap algorithm (parameter-free) based on the traditional methods and find it is comparable to or even outperforms deep learning based MCG methods in the widely used GEOM-QM9 and GEOM-Drugs benchmarks. In particular, our design algorithm is simply the clustering of the RDKIT-generated conformations. We hope our findings can help the community to revise the deep learning methods for MCG. The code of the proposed algorithm could be found at https://gist.github.com/ZhouGengmo/5b565f51adafcd911c0bc115b2ef027c.
Netizens, Academicians, and Information Professionals' Opinions About AI With Special Reference To ChatGPT
Authors: Subaveerapandiyan A, Vinoth A, Neelam Tiwary
Date: 2023-02-01 00:00:00

This study aims to understand the perceptions and opinions of academicians towards ChatGPT-3 by collecting and analyzing social media comments, and a survey was conducted with library and information science professionals. The research uses a content analysis method and finds that while ChatGPT-3 can be a valuable tool for research and writing, it is not 100% accurate and should be cross-checked. The study also finds that while some academicians may not accept ChatGPT-3, most are starting to accept it. The study is beneficial for academicians, content developers, and librarians.
Sensing-Assisted Receivers for Resilient-By-Design 6G MU-MIMO Uplink
Authors: Vlad C. Andrei, Xinyang Li, Ullrich J. Mönich, Holger Boche
Date: 2023-02-14 00:00:00

We address the resilience of future 6G MIMO communications by considering an uplink scenario where multiple legitimate transmitters try to communicate with a base station in the presence of an adversarial jammer. The jammer possesses full knowledge about the system and the physical parameters of the legitimate link, while the base station only knows the UL-channels and the angle-of-arrival (AoA) of the jamming signals. Furthermore, the legitimate transmitters are oblivious to the fact that jamming takes place, thus the burden of guaranteeing resilience falls on the receiver. For this case we derive one optimal jamming strategy that aims to minimize the rate of the strongest user and multiple receive strategies, one based on a lower bound on the achievable signal-to-interference-to-noise-ratio (SINR), one based on a zero-forcing (ZF) design, and one based on a minimum SINR constraint. Numerical studies show that the proposed anti-jamming approaches ensure that the sum rate of the system is much higher than without protection, even when the jammer has considerably more transmit power and even if the jamming signals come from the same direction as those of the legitimate users.
Analytical Model of Nonlinear Fiber Propagation for General Dual-Polarization Four-Dimensional Modulation Format
Authors: Zhiwei Liang, Bin Chen, Yi Lei, Gabriele Liga, Alex Alvarado
Date: 2023-02-14 00:00:00

Coherent dual-polarization (DP) optical transmission systems encode information on the four available degrees of freedom of an optical field: the two polarization states, each with two quadrature components. Such systems naturally operate based on a four-dimensional (4D) signal space. Having a general analytical model to accurately estimate nonlinear interference (NLI) is key to analyze such transmission systems as well as to study how different DP-4D formats are affected by NLI. However, the available models in the literature are not completely general. They either do not apply to the entire DP-4D formats or do not consider all the NLI contributions. In this paper, we develop a model that applies to all DP-4D modulation formats with independent symbols. Our model takes self-channel interference, cross-channel interference and multiple-channel interference effects into account. As an application of our model, we further study the effects of signal-noise interactions in long-haul transmission via the proposed model. When compared to previous results in the literature, our model is more accurate at predicting the contribution of NLI for both low and high dispersion fibers in single- and multi-channel transmission systems. For the NLI, we report an average gap from split step Fourier simulation results below 0.15 dB. The simulation results further show that by considering signal-noise interactions, the proposed model in long-haul transmission can reduce the transmission reach prediction error by 4%.
Parametric Differential Machine Learning for Pricing and Calibration
Authors: Arun Kumar Polala, Bernhard Hientzsch
Date: 2023-02-13 00:00:00

Differential machine learning (DML) is a recently proposed technique that uses samplewise state derivatives to regularize least square fits to learn conditional expectations of functionals of stochastic processes as functions of state variables. Exploiting the derivative information leads to fewer samples than a vanilla ML approach for the same level of precision. This paper extends the methodology to parametric problems where the processes and functionals also depend on model and contract parameters, respectively. In addition, we propose adaptive parameter sampling to improve relative accuracy when the functionals have different magnitudes for different parameter sets. For calibration, we construct pricing surrogates for calibration instruments and optimize over them globally. We discuss strategies for robust calibration. We demonstrate the usefulness of our methodology on one-factor Cheyette models with benchmark rate volatility specification with an extra stochastic volatility factor on (two-curve) caplet prices at different strikes and maturities, first for parametric pricing, and then by calibrating to a given caplet volatility surface. To allow convenient and efficient simulation of processes and functionals and in particular the corresponding computation of samplewise derivatives, we propose to specify the processes and functionals in a low-code way close to mathematical notation which is then used to generate efficient computation of the functionals and derivatives in TensorFlow.
$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees
Authors: Zhuohuan Wu, Sheng Cheng, Pan Zhao, Aditya Gahlawat, Kasey A. Ackerman, Arun Lakshmanan, Chengyu Yang, Jiahao Yu, Naira Hovakimyan
Date: 2023-02-14 00:00:00

Quadrotors that can operate predictably in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications. We present L1Quad, a control architecture that ensures uniformly bounded transient response of the quadrotor's uncertain dynamics on the special Euclidean group SE(3). By leveraging the geometric controller and the L1 adaptive controller, the L1Quad architecture provides a theoretically justified framework for the design and analysis of quadrotor's tracking controller in the presence of nonlinear (time- and state-dependent) uncertainties on both the translational and rotational dynamics. In addition, we validate the performance of the L1Quad architecture through extensive experiments for eleven types of uncertainties across various trajectories. The results demonstrate that the L1Quad can achieve consistently small tracking errors despite the uncertainties and disturbances and significantly outperforms existing state-of-the-art controllers.