pwc-archive/papers-with-abstracts huggingface dataset:
Formal models of memory based on temporally-varying representations
Authors: Marc W. Howard
Date: 2022-01-05 00:00:00
Tasks: Learning Theory

The idea that memory behavior relies on a gradually-changing internal state has a long history in mathematical psychology. This chapter traces this line of thought from statistical learning theory in the 1950s, through distributed memory models in the latter part of the 20th century and early part of the 21st century through to modern models based on a scale-invariant temporal history. We discuss the neural phenomena consistent with this form of representation and sketch the kinds of cognitive models that can be constructed using it and connections with formal models of various memory tasks.
Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight
Authors: Olivia Zahn, Jorge Bustamante Jr., Callin Switzer, Thomas Daniel, J. Nathan Kutz
Date: 2022-01-05 00:00:00
Tasks: Model Predictive Control, Network Pruning

Insect flight is a strongly nonlinear and actuated dynamical system. As such, strategies for understanding its control have typically relied on either model-based methods or linearizations thereof. Here we develop a framework that combines model predictive control on an established flight dynamics model and deep neural networks (DNN) to create an efficient method for solving the inverse problem of flight control. We turn to natural systems for inspiration since they inherently demonstrate network pruning with the consequence of yielding more efficient networks for a specific set of tasks. This bio-inspired approach allows us to leverage network pruning to optimally sparsify a DNN architecture in order to perform flight tasks with as few neural connections as possible, however, there are limits to sparsification. Specifically, as the number of connections falls below a critical threshold, flight performance drops considerably. We develop sparsification paradigms and explore their limits for control tasks. Monte Carlo simulations also quantify the statistical distribution of network weights during pruning given initial random weights of the DNNs. We demonstrate that on average, the network can be pruned to retain approximately 7% of the original network weights, with statistical distributions quantified at each layer of the network. Overall, this work shows that sparsely connected DNNs are capable of predicting the forces required to follow flight trajectories. Additionally, sparsification has sharp performance limits.
Egg shell quality and bone status as affected by environmental temperature, Ca and non-phytate P intake and in vitro limestone solubility in Single-Comb White Leghorn hens
Authors: Bingfan Zhang, Jordan Weil, Antonio Beita Guerra, Pramir Maharjan, Katie Hilton, Nawin Suesuttajit, Diego A. Martinez, Craig N. Coon
Date: 2022-01-05 00:00:00

Environmental temperature (ET) often changes the nutrient intake/output for layers. Changing feed formulations based on ET may need to be utilized to obtain optimum performance, shell quality and bone status. This study was conducted to investigate the effects of temperature, Ca intake, non-phytate P (NPP) intake and in vitro limestone solubility (LS) on egg-shell quality and bone status in commercial White Leghorn hens. Egg mass and shell weight per unit surface area (SWUSA) decreased with increasing ET (p lower than 0.05), especially when ET was 29.7 C (cycling mean ET)or a constant ET was 32.2 C. Feeding layers a low soluble larger particle size limestone instead of a highly soluble limestone produced beneficial effects for SWUSA at the thermoneutral ET (21.1 C) but the beneficial effect was less or disappeared when ET was higher than 26.6 C in EXP 1 and 2. Feeding layers 245 and 353 mg NPP/h/d supported satisfactory bone status at 21.1 C, however layers housed at higher than 30 C needed an additional intake of 50 mg NPP/h/d to support bone integrity. Results of EXP 1 and 2 indicates that 48 week old layers housed in thermoneutral or warmer ET require a minimum of 4.2 g Ca/h/d for maintaining optimum shell quality and bone integrity. Feeding low LS (34.1% in vitro solubility) improved egg shell quality only for hens housed in thermoneutral ET (21.1 C) and did not improve egg shell quality at higher ET (constant or cycling). Daily NPP intake of 245 and 353 mg/h/d supported optimum egg production and bone status at 21.1 C, respectively. A higher NPP and Ca intake may be required for bone status compared to egg production, especially in older hens.
Phenotypic correlation between external and internal egg quality characteristics in 85-week-old laying hens
Authors: J. S. Inca, D. A. Martinez, C. Vilchez
Date: 2022-01-05 00:00:00

Background and Objective: Several studies confirm that the age of hens has a tremendous impact on external and internal egg quality characteristics. Egg production could be at serious risk if egg quality characteristics and age of hens are not seriously considered. This study was conducted to analyze the phenotypic correlations between some internal and external egg quality characteristics in old laying hens. Materials and Methods: A total of 288 eggs of 85-week-old Hy-Line Brown laying hens were collected during 3 weeks and their internal and external egg characteristics were evaluated. Results: Phenotypic correlations between egg quality characteristics in old laying hens indicate a negative impact on shell and albumen quality but not affected yolk quality characteristics. Conclusion: This study helps to understand that raising laying hens above 80 weeks would have a negative impact on egg quality characteristics.
Meta-analysis of commercial-scale trials as a means to improve decision-making processes in the poultry industry: a phytogenic feed additive case study
Authors: Diego A. Martinez, Carol L. Ponce-de-Leon, Carlos Vilchez
Date: 2022-01-05 00:00:00
Tasks: Decision Making, Nutrition

Background and Objective: In the current study, we sought to determine the value of a meta-analysis to improve decision-making processes related to nutrition in the poultry industry. To this end, nine commercial size experiments were conducted to test the effect of a phytogenic feed additive and three approaches were applied to the data. Materials and Methods: In all experiments, 1-day-old male Cobb 500 chicks were used and fed corn-soybean meal diets. Two dietary treatments were tested: T1, control diet and T2, control diet + feed additive at a 0.05% inclusion rate. The experimental units were broiler houses (7 experiments), floor pens (1 experiment) and cages (1 experiment). The response variables were final body weight, feed intake, feed conversion ratio, mortality and production efficiency. Analyses of variance of data from each and all the experiments were performed using SAS under completely randomized non-blocked or blocked designs, respectively. The meta-analyses were performed in R programming language. Results: No statistically significant effects were found in the evaluated variables in any of the independent experiments (p>0.12), nor following the application of a block design (p>0.08). The meta-analyses showed no statistically significant global effects in terms of final body weight (p>0.19), feed intake (p>0.23), mortality (p>0.09), or European Production Efficiency Factor (p>0.08); however, a positive global effect was found with respect to feed conversion ratio (p<0.046). Conclusion: This meta-analysis demonstrated that the phytogenic feed additive improved the efficiency of birds to convert feed to body weight (35 g less feed per 1 kg of body weight obtained). Thus, the use of meta-analyses in commercial-scale poultry trials can increase statistical power and as a result, help to detect statistical differences if they exist.
Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling
Authors: Alexander P. Browning, Thomas D. Lewin, Ruth E. Baker, Philip K. Maini, Eduardo G. Moros, Jimmy Caudell, Helen M. Byrne, Heiko Enderling
Date: 2022-01-06 00:00:00
Tasks: Decision Making

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient's course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations.
Stochastic measure distortions induced by quantile processes for risk quantification and valuation
Authors: Holly Brannelly, Andrea Macrina, Gareth W. Peters
Date: 2021-12-10 00:00:00

We develop a novel stochastic valuation and premium calculation principle based on probability measure distortions that are induced by quantile processes in continuous time. Necessary and sufficient conditions are derived under which the quantile processes satisfy first- and second-order stochastic dominance. The introduced valuation principle relies on stochastic ordering so that the valuation risk-loading, and thus risk premiums, generated by the measure distortion is an ordered parametric family. The quantile processes are generated by a composite map consisting of a distribution and a quantile function. The distribution function accounts for model risk in relation to the empirical distribution of the risk process, while the quantile function models the response to the risk source as perceived by, e.g., a market agent. This gives rise to a system of subjective probability measures that indexes a stochastic valuation principle susceptible to probability measure distortions. We use the Tukey-$gh$ family of quantile processes driven by Brownian motion in an example that demonstrates stochastic ordering. We consider the conditional expectation under the distorted measure as a member of the time-consistent class of dynamic valuation principles, and extend it to the setting where the driving risk process is multivariate. This requires the introduction of a copula function in the composite map for the construction of quantile processes, which presents another new element in the risk quantification and modelling framework based on probability measure distortions induced by quantile processes.
SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation
Authors: Shumao Pang, Chunlan Pang, Lei Zhao, Yangfan Chen, Zhihai Su, Yujia Zhou, Meiyan Huang, Wei Yang, Hai Lu, Qianjin Feng
Date: 2020-09-21 00:00:00
Proceeding: IEEE Transactions on Medical Imaging 2020 9
Tasks: Segmentation

Spine parsing (i.e., multi-class segmentation of vertebrae and intervertebral discs (IVDs)) for volumetric magnetic resonance (MR) image plays a significant role in various spinal disease diagnoses and treatments of spine disorders, yet is still a challenge due to the inter-class similarity and intra-class variation of spine images. Existing fully convolutional network based methods failed to explicitly exploit the dependencies between different spinal structures. In this article, we propose a novel two-stage framework named SpineParseNet to achieve automated spine parsing for volumetric MR images. The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement. In 3D GCSN, region pooling is employed to project the image representation to graph representation, in which each node representation denotes a specific spinal structure. The adjacency matrix of the graph is designed according to the connection of spinal structures. The graph representation is evolved by graph convolutions. Subsequently, the proposed region unpooling module re-projects the evolved graph representation to a semantic image representation, which facilitates the 3D GCSN to generate reliable coarse segmentation. Finally, the 2D ResUNet refines the segmentation. Experiments on T2-weighted volumetric MR images of 215 subjects show that SpineParseNet achieves impressive performance with mean Dice similarity coefficients of 87.32 ± 4.75%, 87.78 ± 4.64%, and 87.49 ± 3.81% for the segmentations of 10 vertebrae, 9 IVDs, and all 19 spinal structures respectively. The proposed method has great potential in clinical spinal disease diagnoses and treatments.
Exciting-Inhibition Network for Person Reidentification in Internet of Things
Authors: Meixia Fu, Songlin Sun, Qilian Liang, Xiaoyun Tong, Qiang Liu
Date: 2021-10-15 00:00:00
Proceeding: IEEE Internet of Things Journal 2021 10
Tasks: Person Re-Identification

Person reidentification (re-ID), which aims at recognizing the pedestrians captured by multiple nonoverlapping
cameras, has attracted more interest due to its significant and
potential application in the Internet of Things like intelligent
visual surveillance. However, person reID is still a challenging
problem in the situations of various pose, similar appearances,
partial occlusion, etc. To handle these obstacles, in this article, we
investigate an innovative exciting-inhibition network (EINet) that
is a two-branch network composed of the exciting branch and
the inhibition branch. The channel-spatial attention block that
recalibrates the relationship between channels and highlights features at different spatial positions is used in the exciting branch.
A novel Soft Batch DropBlock that randomly selects a continuous region of the intermediate feature maps at the same location
is applied in the inhibition branch to inhibit the trivial by an
inhibitive mask and reinforce learning the remaining regions.
We integrate the comprehensive features from both branches
for evaluation and show the performance of EINet intuitively
using the visualization method. Abundant experiments demonstrate the state-of-the-art performance by comparing with the
previous methods on three popular person re-ID benchmarks. For
example, our method obtains 95.64% Rank-1 and 88.75% mean
average precision (mAP) on Market-1501, and 77.00% Rank-1
and 74.51% mAP on CUHK03-Detect in the single query mode,
respectively.
Index Terms—Channel-spatial attention block (CSAB),
exciting-inhibition network (EINet), Internet of Things (IoT),
person reidentification (re-ID), soft batch dropblock.
Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials
Authors: Yang Deng*, Juncheng Dong*, Simiao Ren*, Omar Khatib, Mohammadreza Soltani, Vahid Tarokh, Willie Padilla, Jordan Malof
Date: 2021-11-06 00:00:00
Proceeding: NeurIPS 2021 11
Tasks: Benchmarking, Neural Network simulation

Artificial electromagnetic materials (AEMs), including metamaterials, derive their electromagnetic properties from geometry rather than chemistry. With the appropriate geometric design, AEMs have achieved exotic properties not realizable with conventional materials (e.g., cloaking or negative refractive index). However, understanding the relationship between the AEM structure and its properties is often poorly understood. While computational electromagnetic simulation (CEMS) may help design new AEMs, its use is limited due to its long computational time. Recently, it has been shown that deep learning can be an alternative solution to infer the relationship between an AEM geometry and its properties using a (relatively) small pool of CEMS data. However, the limited publicly released datasets and models and no widely-used benchmark for comparison have made using deep learning approaches even more difficult. Furthermore, configuring CEMS for a specific problem requires substantial expertise and time, making reproducibility challenging. Here, we develop a collection of three classes of AEM problems: metamaterials, nanophotonics, and color filter designs. We also publicly release software, allowing other researchers to conduct additional simulations for each system easily. Finally, we conduct experiments on our benchmark datasets with three recent neural network architectures: the multilayer perceptron (MLP), MLP-mixer, and transformer. We identify the methods and models that generalize best over the three problems to establish the best practice and baseline results upon which future research can build.
Fer2013 Recognition - ResNet18 With Tricks
Authors: Xiaojian Yuan
Date: 2021-12-29 00:00:00
Proceeding: None 2021 12
Tasks: Facial Expression Recognition (FER)

This work is the final project of the Computer Vision Course of USTC. However, I achieve the highest single-network classification accuracy on FER2013 based on ResNet18. To my best knowledge, this work achieves state-of-the-art single-network accuracy of 73.70 % on FER2013 without using extra training data, which exceeds the previous work [1] of 73.28%.
Analysis of Kepler Objects of Interest using Machine Learning for Exoplanet Identification
Authors: Aditeya Baral, Ameya Rajendra Bhamare, Saarthak Agarwal
Date: 2021-06-27 00:00:00
Proceeding: International Conference on Intelligent Technologies (CONIT) 2021 6
Tasks: BIG-bench Machine Learning, Classification

For several decades, planet identification has only been performed by astronomical experts and researchers with the help of specialized equipment. With the advent of computational methods and access to satellite data from space missions, this trend has changed. For instance, NASA’s Exoplanet Exploration program has provided us with vast amounts of data on celestial objects to assist in space exploration. One such mission of interest is the Kepler mission. Over 4000 such transiting exoplanets have been identified since the mission commenced in 2007. It has provided us with an extensive database of discoveries that help in computing planet occurrence rates as a function of an object’s parameters such as the size, insolation flux, star type and orbital period. This information is catalogued in the Cumulative Kepler Object of Information dataset. Four basic models have been compared. Namely, Support Vector Machines, Random Forest Classifiers, AdaBoost and Deep Neural Networks. The AdaBoost classifier was selected as the optimum machine learning model and returned an F-1 score of 0.98.
Negative Evidence Matters in Interpretable Histology Image Classification
Authors: Soufiane Belharbi, Marco Pedersoli, Ismail Ben Ayed, Luke McCaffrey, Eric Granger
Date: 2022-01-07 00:00:00
Tasks: Classification, Decoder, image-classification, Image Classification, Weakly-supervised Learning

Using only global image-class labels, weakly-supervised learning methods, such as class activation mapping, allow training CNNs to jointly classify an image, and locate regions of interest associated with the predicted class. However, without any guidance at the pixel level, such methods may yield inaccurate regions. This problem is known to be more challenging with histology images than with natural ones, since objects are less salient, structures have more variations, and foreground and background regions have stronger similarities. Therefore, computer vision methods for visual interpretation of CNNs may not directly apply. In this paper, a simple yet efficient method based on a composite loss is proposed to learn information from the fully negative samples (i.e., samples without positive regions), and thereby reduce false positives/negatives. Our new loss function contains two complementary terms: the first exploits positive evidence collected from the CNN classifier, while the second leverages the fully negative samples from training data. In particular, a pre-trained CNN is equipped with a decoder that allows refining the regions of interest. The CNN is exploited to collect both positive and negative evidence at the pixel level to train the decoder. Our method called NEGEV benefits from the fully negative samples that naturally occur in the data, without any additional supervision signals beyond image-class labels. Extensive experiments show that our proposed method can substantial outperform related state-of-art methods on GlaS (public benchmark for colon cancer), and Camelyon16 (patch-based benchmark for breast cancer using three different backbones). Our results highlight the benefits of using both positive and negative evidence, the first obtained from a classifier, and the other naturally available in datasets.
Sign Language Video Retrieval with Free-Form Textual Queries
Authors: Amanda Duarte, Samuel Albanie, Xavier Giró-i-Nieto, Gül Varol
Date: 2022-01-07 00:00:00
Proceeding: CVPR 2022 1
Tasks: Form, Retrieval, Sentence, Sign Language Retrieval, Video Retrieval

Systems that can efficiently search collections of sign language videos have been highlighted as a useful application of sign language technology. However, the problem of searching videos beyond individual keywords has received limited attention in the literature. To address this gap, in this work we introduce the task of sign language retrieval with free-form textual queries: given a written query (e.g., a sentence) and a large collection of sign language videos, the objective is to find the signing video in the collection that best matches the written query. We propose to tackle this task by learning cross-modal embeddings on the recently introduced large-scale How2Sign dataset of American Sign Language (ASL). We identify that a key bottleneck in the performance of the system is the quality of the sign video embedding which suffers from a scarcity of labeled training data. We, therefore, propose SPOT-ALIGN, a framework for interleaving iterative rounds of sign spotting and feature alignment to expand the scope and scale of available training data. We validate the effectiveness of SPOT-ALIGN for learning a robust sign video embedding through improvements in both sign recognition and the proposed video retrieval task.
Automated Dissipation Control for Turbulence Simulation with Shell Models
Authors: Ann-Kathrin Dombrowski, Klaus-Robert Müller, Wolf Christian Müller
Date: 2022-01-07 00:00:00
Tasks: BIG-bench Machine Learning

The application of machine learning (ML) techniques, especially neural networks, has seen tremendous success at processing images and language. This is because we often lack formal models to understand visual and audio input, so here neural networks can unfold their abilities as they can model solely from data. In the field of physics we typically have models that describe natural processes reasonably well on a formal level. Nonetheless, in recent years, ML has also proven useful in these realms, be it by speeding up numerical simulations or by improving accuracy. One important and so far unsolved problem in classical physics is understanding turbulent fluid motion. In this work we construct a strongly simplified representation of turbulence by using the Gledzer-Ohkitani-Yamada (GOY) shell model. With this system we intend to investigate the potential of ML-supported and physics-constrained small-scale turbulence modelling. Instead of standard supervised learning we propose an approach that aims to reconstruct statistical properties of turbulence such as the self-similar inertial-range scaling, where we could achieve encouraging experimental results. Furthermore we discuss pitfalls when combining machine learning with differential equations.