pwc-archive/papers-with-abstracts huggingface dataset:
A geometric analysis of the impact of large but finite switching rates on vaccination evolutionary games
Authors: Rossella Della Marca, Alberto D'Onofrio, Mattia Sensi, Sara Sottile
Date: 2023-03-24 00:00:00

In contemporary society, social networks accelerate decision dynamics causing a rapid switch of opinions in a number of fields, including the prevention of infectious diseases by means of vaccines. This means that opinion dynamics can nowadays be much faster than the spread of epidemics. Hence, we propose a Susceptible-Infectious-Removed epidemic model coupled with an evolutionary vaccination game embedding the public health system efforts to increase vaccine uptake. This results in a global system ``epidemic model + evolutionary game''. The epidemiological novelty of this work is that we assume that the switching to the strategy ``pro vaccine'' depends on the incidence of the disease. As a consequence of the above-mentioned accelerated decisions, the dynamics of the system acts on two different scales: a fast scale for the vaccine decisions and a slower scale for the spread of the disease. Another, and more methodological, element of novelty is that we apply Geometrical Singular Perturbation Theory (GSPT) to such a two-scale model and we then compare the geometric analysis with the Quasi-Steady-State Approximation (QSSA) approach, showing a criticality in the latter. Later, we apply the GSPT approach to the disease prevalence-based model already studied in (Della Marca and d'Onofrio, Comm Nonl Sci Num Sim, 2021) via the QSSA approach by considering medium-large values of the strategy switching parameter.
Polyhedral geometry and combinatorics of an autocatalytic ecosystem
Authors: Praful Gagrani, Victor Blanco, Eric Smith, David Baum
Date: 2023-03-24 00:00:00

Developing a mathematical understanding of autocatalysis in reaction networks has both theoretical and practical implications. We review definitions of autocatalytic networks and prove some properties for minimal autocatalytic subnetworks (MASs). We show that it is possible to classify MASs in equivalence classes, and develop mathematical results about their behavior. We also provide linear-programming algorithms to exhaustively enumerate them and a scheme to visualize their polyhedral geometry and combinatorics. We then define cluster chemical reaction networks, a framework for coarse-graining real chemical reactions with positive integer conservation laws. We find that the size of the list of minimal autocatalytic subnetworks in a maximally connected cluster chemical reaction network with one conservation law grows exponentially in the number of species. We end our discussion with open questions concerning an ecosystem of autocatalytic subnetworks and multidisciplinary opportunities for future investigation.
On the Optimality of Misspecified Spectral Algorithms
Authors: Haobo Zhang, Yicheng Li, Qian Lin
Date: 2023-03-27 00:00:00

In the misspecified spectral algorithms problem, researchers usually assume the underground true function $f_{\rho}^{*} \in [\mathcal{H}]^{s}$, a less-smooth interpolation space of a reproducing kernel Hilbert space (RKHS) $\mathcal{H}$ for some $s\in (0,1)$. The existing minimax optimal results require $\|f_{\rho}^{*}\|_{L^{\infty}}<\infty$ which implicitly requires $s > \alpha_{0}$ where $\alpha_{0}\in (0,1)$ is the embedding index, a constant depending on $\mathcal{H}$. Whether the spectral algorithms are optimal for all $s\in (0,1)$ is an outstanding problem lasting for years. In this paper, we show that spectral algorithms are minimax optimal for any $\alpha_{0}-\frac{1}{\beta} < s < 1$, where $\beta$ is the eigenvalue decay rate of $\mathcal{H}$. We also give several classes of RKHSs whose embedding index satisfies $ \alpha_0 = \frac{1}{\beta} $. Thus, the spectral algorithms are minimax optimal for all $s\in (0,1)$ on these RKHSs.
A note on retrodiction and machine evolution
Authors: Gustavo Caetano-Anollés
Date: 2023-03-26 00:00:00

Biomolecular communication demands that interactions between parts of a molecular system act as scaffolds for message transmission. It also requires an evolving and organized system of signs - a communicative agency - for creating and transmitting meaning. Here I explore the need to dissect biomolecular communication with retrodiction approaches that make claims about the past given information that is available in the present. While the passage of time restricts the explanatory power of retrodiction, the use of molecular structure in biology offsets information erosion. This allows description of the gradual evolutionary rise of structural and functional innovations in RNA and proteins. The resulting chronologies can also describe the gradual rise of molecular machines of increasing complexity and computation capabilities. For example, the accretion of rRNA substructures and ribosomal proteins can be traced in time and placed within a geological timescale. Phylogenetic, algorithmic and theoretical-inspired accretion models can be reconciled into a congruent evolutionary model. Remarkably, the time of origin of enzymes, functional RNA, non-ribosomal peptide synthetase (NRPS) complexes, and ribosomes suggest they gradually climbed Chomsky's hierarchy of formal grammars, supporting the gradual complexification of machines and communication in molecular biology. Future retrodiction approaches and in-depth exploration of theoretical models of computation will need to confirm such evolutionary progression.
A Bioinformatics Study for Recognition of Hub Genes and Pathways in Pancreatic Ductal Adenocarcinoma
Authors: Atefeh Akbarnia Dafrazi, Tahmineh Mehrabi, Fatemeh Malekinejad
Date: 2023-03-25 00:00:00
Tasks: Diagnostic, Retrieval

Background: The aim of this study is to use bioinformatics to discover the biomarkers associated with patients with Pancreatic Ductal Adenocarcinoma(PDAC). Material and Methods: GSE28735, GSE15471, and GSE62452 are gene microarray datasets drived from the GEO database, included 153 PDAC samples and 145 normal samples. By analyzing both Gene Ontology (GO) and the Kyoto Encyclopedia of Genes (KEGG), for screening DEGs has provided information about their biological function. Protein-protein interactions (PPI) of DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. UALCAN was also used to perform prognostic analyses. Results: In Pancreatic ductal adenocarcinoma, we discovered 2264 upregulated DEGs (uDEGs) and 723 downregulated DEGs (dDEGs). The Gene Ontology (GO) indicated that extracellular matrix organization, extracellular structure organization, collagen catabolic process and also the uDEGs are enriched in focal adhesion, PI3K-Akt signaling pathway, ECM-receptor interaction by KEGG analysis. The top ten hub genes were identified from the PPI network: COL1A1, COL3A1, COL1A2, FN1, COL5A2, ITGA3, FBN1, MET, COL6A3, and BGN. These 10 hub genes are highly upregulated in pancreatic ductal adenocarcinoma, according to GEPIA research. The UALCAN prognostic analysis of the 10 hub genes revealed that two of the elevated genes, ITGA3 and MET, significantly shortened PDAC patient survival time. Pancreatic ductal adenocarcinoma is linked to Focal adhesion, the PI3K-Akt signaling pathway, and ECM-receptor interaction, according to Module analysis from the PPI network. Conclusion: This investigation identified hub genes and important signal pathways, which adds to our knowledge of molecular mechanisms and could be exploited as diagnostic and therapeutic biomarkers for Pancreatic ductal adenocarcinoma.
Generalized epidemic model incorporating non-Markovian infection processes and waning immunity
Authors: Qihui Yang, Joan Saldaña, Caterina Scoglio
Date: 2023-03-27 00:00:00

The Markovian approach, which assumes exponentially distributed interinfection times, is dominant in epidemic modeling. However, this assumption is unrealistic as an individual's infectiousness depends on its viral load and varies over time. In this paper, we present a Susceptible-Infected-Recovered-Vaccinated-Susceptible epidemic model incorporating non-Markovian infection processes. The model can be easily adapted to accurately capture the generation time distributions of emerging infectious diseases, which is essential for accurate epidemic prediction. We observe noticeable variations in the transient behavior under different infectiousness profiles and the same basic reproduction number R0. The theoretical analyses show that only R0 and the mean immunity period of the vaccinated individuals have an impact on the critical vaccination rate needed to achieve herd immunity. A vaccination level at the critical vaccination rate can ensure a very low incidence among the population in case of future epidemics, regardless of the infectiousness profiles.
Reference standard analysis of multiple new and old plasma clearance models and renal clearance with special attention to measurement of reduced glomerular filtration rate
Authors: Carl A. Wesolowski
Date: 2023-03-27 00:00:00
Tasks: Management

Nine models were evaluated as candidate glomerular filtration rate (GFR) reference standards in three datasets using [$^{51}$Cr(EDTA)]$^-$ or [$^{169}$Yb(DTPA)]$^{2-}$ anions in 98 studies. Noncompartmental methods formed an upper limit for estimating mass excreted and voluntary urine collection formed a lower limit. For current models and methods, reduced GFR in adults resulted in inflated clearance estimates. Two different logarithmic models with exponential tails were created and may have underestimated reduced clearance. The logarithmic formulae can be used with only two plasma samples, and fit 13 studies totalling 162 plasma samples drawn from 5 min to 24 h with an 8\% standard deviation of residuals compared to 20\% error for monoexponentials. For shorter times (4 or 5 h) the fit errors decreased but the ratio of errors remained at circa 2.5 times lesser for the logarithmic versus monoexponential models. Adaptively regularised gamma variate, Tk-GV, models that are well documented, but not in common use, were largely contained within the reference extreme values, were unbiased for different levels of clearance and were the only models to be uncorrelated to volume of distribution from mean residence time divided by weight. Using Tk-GV as a candidate reference standard, potentially better methods for routine clinical usage were discussed. Prospective clinical testing, and metabolic scaling of decreased renal function is advised for potential changes to patient triage.
The Elasticity of Quantitative Investment
Authors: Carter Davis
Date: 2023-03-25 00:00:00
Tasks: counterfactual

What is the demand elasticity of statistical arbitrageurs that invest according to the advice of modern cross-sectional asset pricing models? Thirteen models from the literature exhibit strikingly inelastic demand, in contrast to classical models that rely on statistical arbitrageurs to create elastic market demand for assets. This inelasticity arises from the difficulty of trading against price changes. A quantitative equilibrium model shows that aggregate demand remains inelastic even with these statistical arbitrageurs in the market.
On the Connection between Temperature and Volatility in Ideal Agent Systems
Authors: Christoph J. Börner, Ingo Hoffmann, John H. Stiebel
Date: 2023-03-27 00:00:00
Tasks: Econometrics

Models for spin systems known from statistical physics are applied by analogy in econometrics in the form of agent-based models. Researchers suggest that the state variable temperature $T$ corresponds to volatility $\sigma$ in capital market theory problems. To the best of our knowledge, this has not yet been theoretically derived, for example, for an ideal agent system. In the present paper, we derive the exact algebraic relation between $T$ and $\sigma$ for an ideal agent system and discuss implications and limitations.
DreamStone: Image as Stepping Stone for Text-Guided 3D Shape Generation
Authors: Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, Chi-Wing Fu
Date: 2023-03-24 00:00:00
Tasks: 3D Shape Generation

In this paper, we present a new text-guided 3D shape generation approach DreamStone that uses images as a stepping stone to bridge the gap between text and shape modalities for generating 3D shapes without requiring paired text and 3D data. The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between rendered images and the input text. Besides, to extend beyond the generative capability of the SVR model, we design a text-guided 3D shape stylization module that can enhance the output shapes with novel structures and textures. Further, we exploit pre-trained text-to-image diffusion models to enhance the generative diversity, fidelity, and stylization capability. Our approach is generic, flexible, and scalable, and it can be easily integrated with various SVR models to expand the generative space and improve the generative fidelity. Extensive experimental results demonstrate that our approach outperforms the state-of-the-art methods in terms of generative quality and consistency with the input text. Codes and models are released at https://github.com/liuzhengzhe/DreamStone-ISS.
Unsupervised Pre-Training For Data-Efficient Text-to-Speech On Low Resource Languages
Authors: Seongyeon Park, Myungseo Song, Bohyung Kim, Tae-Hyun Oh
Date: 2023-03-28 00:00:00
Tasks: Data Augmentation, text-to-speech, Text to Speech, Unsupervised Pre-training

Neural text-to-speech (TTS) models can synthesize natural human speech when trained on large amounts of transcribed speech. However, collecting such large-scale transcribed data is expensive. This paper proposes an unsupervised pre-training method for a sequence-to-sequence TTS model by leveraging large untranscribed speech data. With our pre-training, we can remarkably reduce the amount of paired transcribed data required to train the model for the target downstream TTS task. The main idea is to pre-train the model to reconstruct de-warped mel-spectrograms from warped ones, which may allow the model to learn proper temporal assignment relation between input and output sequences. In addition, we propose a data augmentation method that further improves the data efficiency in fine-tuning. We empirically demonstrate the effectiveness of our proposed method in low-resource language scenarios, achieving outstanding performance compared to competing methods. The code and audio samples are available at: https://github.com/cnaigithub/SpeechDewarping
Transformer and Snowball Graph Convolution Learning for Brain functional network Classification
Authors: Jinlong Hu, Yangmin Huang, Shoubin Dong
Date: 2023-03-28 00:00:00
Tasks: Graph Classification

Advanced deep learning methods, especially graph neural networks (GNNs), are increasingly expected to learn from brain functional network data and predict brain disorders. In this paper, we proposed a novel Transformer and snowball encoding networks (TSEN) for brain functional network classification, which introduced Transformer architecture with graph snowball connection into GNNs for learning whole-graph representation. TSEN combined graph snowball connection with graph Transformer by snowball encoding layers, which enhanced the power to capture multi-scale information and global patterns of brain functional networks. TSEN also introduced snowball graph convolution as position embedding in Transformer structure, which was a simple yet effective method for capturing local patterns naturally. We evaluated the proposed model by two large-scale brain functional network datasets from autism spectrum disorder and major depressive disorder respectively, and the results demonstrated that TSEN outperformed the state-of-the-art GNN models and the graph-transformer based GNN models.
Comparison of HDR quality metrics in Per-Clip Lagrangian multiplier optimisation with AV1
Authors: Vibhoothi, François Pitié, Angeliki Katsenou, Yeping Su, Balu Adsumilli, Anil Kokaram
Date: 2023-03-28 00:00:00

The complexity of modern codecs along with the increased need of delivering high-quality videos at low bitrates has reinforced the idea of a per-clip tailoring of parameters for optimised rate-distortion performance. While the objective quality metrics used for Standard Dynamic Range (SDR) videos have been well studied, the transitioning of consumer displays to support High Dynamic Range (HDR) videos, poses a new challenge to rate-distortion optimisation. In this paper, we review the popular HDR metrics DeltaE100 (DE100), PSNRL100, wPSNR, and HDR-VQM. We measure the impact of employing these metrics in per-clip direct search optimisation of the rate-distortion Lagrange multiplier in AV1. We report, on 35 HDR videos, average Bjontegaard Delta Rate (BD-Rate) gains of 4.675%, 2.226%, and 7.253% in terms of DE100, PSNRL100, and HDR-VQM. We also show that the inclusion of chroma in the quality metrics has a significant impact on optimisation, which can only be partially addressed by the use of chroma offsets.
Multi-Behavior Recommendation with Cascading Graph Convolution Networks
Authors: Zhiyong Cheng, Sai Han, Fan Liu, Lei Zhu, Zan Gao, Yuxin Peng
Date: 2023-03-28 00:00:00

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior's embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively.
Regularize implicit neural representation by itself
Authors: Zhemin Li, Hongxia Wang, Deyu Meng
Date: 2023-03-27 00:00:00
Proceeding: CVPR 2023 1

This paper proposes a regularizer called Implicit Neural Representation Regularizer (INRR) to improve the generalization ability of the Implicit Neural Representation (INR). The INR is a fully connected network that can represent signals with details not restricted by grid resolution. However, its generalization ability could be improved, especially with non-uniformly sampled data. The proposed INRR is based on learned Dirichlet Energy (DE) that measures similarities between rows/columns of the matrix. The smoothness of the Laplacian matrix is further integrated by parameterizing DE with a tiny INR. INRR improves the generalization of INR in signal representation by perfectly integrating the signal's self-similarity with the smoothness of the Laplacian matrix. Through well-designed numerical experiments, the paper also reveals a series of properties derived from INRR, including momentum methods like convergence trajectory and multi-scale similarity. Moreover, the proposed method could improve the performance of other signal representation methods.
Your Diffusion Model is Secretly a Zero-Shot Classifier
Authors: Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, Deepak Pathak
Date: 2023-03-28 00:00:00
Proceeding: ICCV 2023 1
Tasks: Domain Generalization, Fine-Grained Image Classification, Image Classification, Image Generation, Relational Reasoning, Visual Reasoning, zero-shot-classification, Zero-Shot Learning, Zero-Shot Transfer Image Classification

The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a staggering variety of prompts and exhibit impressive compositional generalization abilities. Almost all use cases thus far have solely focused on sampling; however, diffusion models can also provide conditional density estimates, which are useful for tasks beyond image generation. In this paper, we show that the density estimates from large-scale text-to-image diffusion models like Stable Diffusion can be leveraged to perform zero-shot classification without any additional training. Our generative approach to classification, which we call Diffusion Classifier, attains strong results on a variety of benchmarks and outperforms alternative methods of extracting knowledge from diffusion models. Although a gap remains between generative and discriminative approaches on zero-shot recognition tasks, our diffusion-based approach has significantly stronger multimodal compositional reasoning ability than competing discriminative approaches. Finally, we use Diffusion Classifier to extract standard classifiers from class-conditional diffusion models trained on ImageNet. Our models achieve strong classification performance using only weak augmentations and exhibit qualitatively better "effective robustness" to distribution shift. Overall, our results are a step toward using generative over discriminative models for downstream tasks. Results and visualizations at https://diffusion-classifier.github.io/
ASIC: Aligning Sparse in-the-wild Image Collections
Authors: Kamal Gupta, Varun Jampani, Carlos Esteves, Abhinav Shrivastava, Ameesh Makadia, Noah Snavely, Abhishek Kar
Date: 2023-03-28 00:00:00
Proceeding: ICCV 2023 1
Tasks: Object, Semantic correspondence

We present a method for joint alignment of sparse in-the-wild image collections of an object category. Most prior works assume either ground-truth keypoint annotations or a large dataset of images of a single object category. However, neither of the above assumptions hold true for the long-tail of the objects present in the world. We present a self-supervised technique that directly optimizes on a sparse collection of images of a particular object/object category to obtain consistent dense correspondences across the collection. We use pairwise nearest neighbors obtained from deep features of a pre-trained vision transformer (ViT) model as noisy and sparse keypoint matches and make them dense and accurate matches by optimizing a neural network that jointly maps the image collection into a learned canonical grid. Experiments on CUB and SPair-71k benchmarks demonstrate that our method can produce globally consistent and higher quality correspondences across the image collection when compared to existing self-supervised methods. Code and other material will be made available at \url{https://kampta.github.io/asic}.