pwc-archive/papers-with-abstracts huggingface dataset:
An ecosystem-based hypothesis for climatic effects on surplus production in California sardine (Sardinops sagax) and environmentally dependent surplus production models
Authors: Larry D Jacobson, Steven J Bograd, Richard H Parrish, Roy Mendelssohn, and Franklin B Schwing
Date: 2005-08-01 00:00:00
Proceeding: Canadian Journal of Fisheries and Aquatic Sciences 2005 8

We used environmentally dependent surplus production (EDSP) models to test hypotheses linking changes in habitat area, carrying capacity and surplus production in California sardine (Sardinops sagax). Habitat area (with mean sea surface temperatures of 14–16 °C) was centered off Oregon, Washington, and British Columbia during July–December and off southern and central California during January–June. Habitat area increased during El Niño and decreased during La Niña events. EDSP models fit better than a conventional Fox surplus production model without environmental data. Our estimated fishing mortality rate at maximum sustained yield FMSY = 0.099·year–1 was consistent with other estimates. Maximum sustained yield (MSY) and stock biomass for MSY (BMSY) depend on habitat area and environmental conditions. Negative surplus production occurred when biomass was high and habitat area declined abruptly. Managers might monitor habitat area to anticipate changes in the California sardine stock and changes in the California Current ecosystem. Periods of high productivity appear easier to identify than periods of negative productivity. Models that incorporate environmental effects on both recruitment and survival and mortality of adult fish appear useful in studying climatic effects on fishery surplus production.
Stock assessment for the western winter-spring cohort of neon flying squid (Ommastrephes bartramii) using environmentally dependent surplus production models
Authors: Jintao Wang, Wei Yu, Xinjun Chen, Yong Chen
Date: 2016-01-01 00:00:00
Proceeding: Scientia Marina 2016 1
Tasks: Management

The western winter-spring cohort of neon flying squid, Ommastrephes bartramii, is targeted by Chinese squidjigging fisheries in the northwest Pacific from August to November. Because this squid has a short lifespan and is an ecological opportunist, the dynamics of its stock is greatly influenced by the environmental conditions, which need to be considered 
in its assessment and management. In this study, an environmentally dependent surplus production (EDSP) model was developed to evaluate the stock dynamics of O. bartramii. Temporal variability of favourable spawning habitat with sea surface 
temperature (SST) of 21-25°C (Ps) was assumed to influence carrying capacity (K), while temporal variability in favourable 
feeding habitat areas with different SST ranges in different months (Pf) was assumed to influence intrinsic growth rate (r). 
The parameters K and r in the EDSP model were thus assumed to be linked to temporal variability in the proportion of Ps and 
Pf, respectively. According to Deviance Information Criterion values, the estimated EDSP model with Ps was considered to 
be better than the conventional surplus production model or other EDSP models. For this model, the maximum sustainable 
yield (MSY) varied from 210000 to 262500 t and biomass at MSY level varied from 360000 to 450000 t. The fishing mortality rates of O. bartramii from 2003 to 2013 were much lower than the fishing mortality at target level and MSY level (Ftar and 
FMSY) and stock biomass was higher than BMSY, suggesting that this squid was not in the status of overfishing and stock was 
not overfished. The management reference points in the EDSP model for O. bartramii were more conservative than those in 
the conventional model. This study suggests that the environmental conditions on the spawning grounds should be considered 
in squid stock assessment and management in the northwest Pacific Ocean.
AugStatic - A Light-Weight Image Augmentation Library
Authors: Allena Venkata Sai Abhishek, Dr. Venkateswara Rao Gurrala
Date: 2022-05-01 00:00:00
Proceeding: Journal of Emerging Technologies and Innovative Research (JETIR) 2022 5
Tasks: Classification, Data Augmentation, Data Visualization, Detecting Image Manipulation, Image Augmentation, Image Classification, Image Cropping, Image Denoising, Image Generation, Image Manipulation, Image Manipulation Detection, Image Matting, Image Morphing, image smoothing, Image Stitching, Image-Variation, ROI-based image generation

The rapid exponential increase in the data led to an abrupt mix of various data types, leading to a deficiency of helpful information. Creating new data with the existing different types of data are presented in this paper. Augmentation is adding up or modifying the dataset with extra data. There are many types of augmentation done for various kinds of datasets. Augmentation has been widely used in multiple pre-processing steps of diverse machine learning pipelines. Many libraries or packages are made for augmentation called augmentation libraries. There are many salient features that each library supports. This paper seeks to enhance the library that makes the AugStatic library much more lightweight and efficient. AugStatic is a custom-built image augmentation library with lower computation costs and more extraordinary salient features compared to other image augmentation libraries. This framework can be used for NumPy arrays and tensors too.
Emotion recognition in the times of COVID19: Coping with face masks
Authors: Roberto Magherini, Elisa Mussi, Michaela Servi *, Yary Volpe
Date: 2022-06-26 00:00:00
Proceeding: 16 June 2022 6
Tasks: Emotion Recognition

Emotion recognition through machine learning techniques is a widely investigated research field, however the recent obligation to wear a face mask, following the COVID19 health emergency, precludes the application of systems developed so far. Humans naturally communicate their emotions through the mouth; therefore, the intelligent systems developed to date for identifying emotions of a subject primarily rely on this area in addition to other anatomical features (eyes, forehead, etc..). However, if the subject is wearing a face mask this region is no longer visible. For this reason, the goal of this work is to develop a tool able to compensate for this shortfall. The proposed tool uses the AffectNet dataset which is composed of eight class of emotions. The iterative training strategy relies on well-known convolutional neural network architectures to identify five sub-classes of emotions: following a pre-processing phase the architecture is trained to perform the task on the eight-class dataset, which is then recategorized into five classes allowing to obtain 96.92% of accuracy on the testing set. This strategy is compared to the most frequently used learning strategies and finally integrated within a real time application that allows to detect faces within a frame, determine if the subjects are wearing a face mask and recognize for each one the current emotion.
Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation
Authors: Nabeel Khalid, Fabian Schmeisser, Mohammadmahdi Koochali, Mohsin Munir, Christoffer Edlund, Timothy R Jackson, Johan Trygg, Rickard Sjögren, Andreas Dengel & Sheraz Ahmed
Date: 2022-07-25 00:00:00
Proceeding: MIUA 2022 7
Tasks: Cell Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation, Weakly supervised segmentation

Identifying cells in microscopic images is a crucial step toward studying image-based cell biology research. Cell instance segmentation provides an opportunity to study the shape, structure, form, and size of cells. Deep learning approaches for cell instance segmentation rely on the instance segmentation mask for each cell, which is a labor-intensive and expensive task. An ample amount of unlabeled microscopic data is available in the cell biology domain, but due to the tedious and exorbitant nature of the annotations needed for the cell instance segmentation approaches, the full potential of the data is not explored. This paper presents a weakly supervised approach, which can perform cell instance segmentation by using only point and bounding box-based annotation. This enormously reduces the annotation efforts. The proposed approach is evaluated on a benchmark dataset i.e., LIVECell, whereby only using a bounding box and randomly generated points on each cell, it achieved the mean average precision score of 43.53% which is as good as the full supervised segmentation method trained with complete segmentation mask. In addition, it is 3.71 times faster to annotate with a bounding box and point in comparison to full mask annotation.
Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention
Authors: Shuan Chen, Yousung Jung
Date: 2021-08-05 00:00:00
Proceeding: JACS Au 2021 8
Tasks: Retrosynthesis, Single-step retrosynthesis

As a fundamental problem in chemistry, retrosynthesis aims at designing reaction pathways and intermediates for a target compound. The goal of artificial intelligence (AI)-aided retrosynthesis is to automate this process by learning from the previous chemical reactions to make new predictions. Although several models have demonstrated their potentials for automated retrosynthesis, there is still a significant need to further enhance the prediction accuracy to a more practical level. Here we propose a local retrosynthesis framework called LocalRetro, motivated by the chemical intuition that the molecular changes occur mostly locally during the chemical reactions. This differs from nearly all existing retrosynthesis methods that suggest reactants based on the global structures of the molecules, often containing fine details not directly relevant to the reactions. This local concept yields local reaction templates involving the atom and bond edits. Because the remote functional groups can also affect the overall reaction path as a secondary aspect, the proposed locally encoded retrosynthesis model is then further refined to account for the nonlocal effects of chemical reaction through a global attention mechanism. Our model shows a promising 89.5 and 99.2% round-trip accuracy at top-1 and top-5 predictions for the USPTO-50K dataset containing 50 016 reactions. We further demonstrate the validity of LocalRetro on a large dataset containing 479 035 reactions (UTPTO-MIT) with comparable round-trip top-1 and top-5 accuracy of 87.0 and 97.4%, respectively. The practical application of the model is also demonstrated by correctly predicting the synthesis pathways of five drug candidate molecules from various literature.
A generalized-template-based graph neural network for accurate organic reactivity prediction
Authors: Shuan Chen, Yousung Jung
Date: 2022-09-15 00:00:00
Proceeding: Nature Machine Intelligence 2022 9
Tasks: Chemical Reaction Prediction, Graph Neural Network

The reliable prediction of chemical reactivity remains in the realm of knowledgeable synthetic chemists. Automating this process by using artificial intelligence could accelerate synthesis design in future digital laboratories. While several machine learning approaches have demonstrated promising results, most current models deviate from how human chemists analyse and predict reactions based on electronic changes. Here, we propose a chemistry-motivated graph neural network called LocalTransform, which learns organic reactivity based on generalized reaction templates to describe the net changes in electron configuration between the reactants and products. The proposed concept dramatically reduces the number of reaction rules and exhibits state-of-the-art product prediction accuracy. In addition to the built-in interpretability of the generalized reaction templates, the high score–accuracy correlation of the model allows users to assess the uncertainty of the machine predictions.
Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction
Authors: Caner Erden
Date: 2023-03-01 00:00:00
Proceeding: International Journal of Environmental Science and Technology 2023 3
Tasks: Air Pollution Prediction, Deep Learning, Hyperparameter Optimization, Time Series, Time Series Forecasting, Time Series Prediction

Since air pollution negatively affects human health and causes serious diseases, accurate air pollution prediction is essential regarding environmental sustainability. Although conventional statistical and machine learning methods have been widely used for air quality forecasting, they have limitations in finding nonlinear relations and modeling sequential data. In recent years, deep learning methods such as long short-term memory, recurrent neural networks, and gated recurrent units have been successfully applied in several research areas, including time-series forecasting. In this study, deep learning algorithm is employed to predict the PM2.5 dataset, including air pollutants (NO, NO2, NOX, O3, PM2.5, SO, and SO2) and meteorological features (wind speed, wind direction, and air temperature) in Istanbul metropolitan. Deep learning algorithms have many hyperparameters such as learning and dropout rate, the number of hidden layers and units in each hidden layer, activation function, loss function, and optimizer that need to be optimized in order to achieve optimal training performance. Therefore, a genetic algorithm-based hyperparameter optimization approach is proposed to find the best parameter combination. The prediction results of deep learning algorithms are compared with default hyperparameters and random search algorithms to confirm the efficacy of the genetic algorithm approach. The proposed method outperforms the other configurations, with the MSE error reduced by 13.38% and 55.30% for testing performance, respectively. The experimental results revealed that genetic algorithms are promising and applicable in hyperparameter optimization of deep neural network models, especially in air quality forecasting.
Superadditivity and Convex Optimization for Globally Optimal Cell Segmentation using Deformable Shape Models
Authors: Leonid Kostrykin, Karl Rohr
Date: 2022-06-23 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2022 6
Tasks: Cell Segmentation, Computational Efficiency, Image Segmentation, Segmentation

Cell nuclei segmentation is challenging due to shape variation and closely clustered or partially overlapping objects. Most previous methods are not globally optimal, limited to elliptical models, or are computationally expensive. In this work, we introduce a globally optimal approach based on deformable shape models and global energy minimization for cell nuclei segmentation and cluster splitting. We propose an implicit parameterization of deformable shape models and show that it leads to a convex energy. Convex energy minimization yields the global solution independently of the initialization, is fast, and robust. To jointly perform cell nuclei segmentation and cluster splitting, we developed a novel iterative global energy minimization method, which leverages the inherent property of superadditivity of the convex energy. This property exploits the lower bound of the energy of the union of the models and improves the computational efficiency. Our method provably determines a solution close to global optimality. In addition, we derive a closed-form solution of the proposed global minimization based on the superadditivity property for non-clustered cell nuclei. We evaluated our method using fluorescence microscopy images of five different cell types comprising various challenges, and performed a quantitative comparison with previous methods. Our method achieved state-of-the-art or improved performance.
Nuclei Segmentation In Microscope Cell Images: A Hand-Segmented Dataset And Comparison Of Algorithms
Authors: Luis Pedro Coelho, Aabid Shariff, Robert F. Murphy
Date: 2009-06-28 00:00:00
Proceeding: IEEE International Symposium on Biomedical Imaging 2009 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is an essential step in many image analysis pipelines and many algorithms have been proposed to solve this problem. However, they are often evaluated subjectively or based on a small number of examples. To fill this gap, we hand-segmented a set of 97 fluorescence microscopy images (a total of 4009 cells) and objectively evaluated some previously proposed segmentation algorithms. We focus on algorithms appropriate for high-throughput settings, where only minimal user intervention is feasible. The hand-labeled dataset (and all software used to compare methods) is publicly available to enable others to use it as a benchmark for newly proposed algorithms.
Semi-Weakly Supervised Object Kinematic Motion Prediction
Authors: Gengxin Liu, Qian Sun, Haibin Huang, Chongyang Ma, Yulan Guo, Li Yi, Hui Huang, Ruizhen Hu
Date: 2023-03-31 00:00:00
Proceeding: CVPR 2023 1
Tasks: Graph Neural Network, motion prediction, Object, Prediction, Segmentation, Semantic Segmentation, Weakly-supervised Learning

Given a 3D object, kinematic motion prediction aims to identify the mobile parts as well as the corresponding motion parameters. Due to the large variations in both topological structure and geometric details of 3D objects, this remains a challenging task and the lack of large scale labeled data also constrain the performance of deep learning based approaches. In this paper, we tackle the task of object kinematic motion prediction problem in a semi-weakly supervised manner. Our key observations are two-fold. First, although 3D dataset with fully annotated motion labels is limited, there are existing datasets and methods for object part semantic segmentation at large scale. Second, semantic part segmentation and mobile part segmentation is not always consistent but it is possible to detect the mobile parts from the underlying 3D structure. Towards this end, we propose a graph neural network to learn the map between hierarchical part-level segmentation and mobile parts parameters, which are further refined based on geometric alignment. This network can be first trained on PartNet-Mobility dataset with fully labeled mobility information and then applied on PartNet dataset with fine-grained and hierarchical part-level segmentation. The network predictions yield a large scale of 3D objects with pseudo labeled mobility information and can further be used for weakly-supervised learning with pre-existing segmentation. Our experiments show there are significant performance boosts with the augmented data for previous method designed for kinematic motion prediction on 3D partial scans.
3D Human Pose Estimation via Intuitive Physics
Authors: Shashank Tripathi, Lea Müller, Chun-Hao P. Huang, Omid Taheri, Michael J. Black, Dimitrios Tzionas
Date: 2023-03-31 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D Human Pose Estimation, Pose Estimation

Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore the fact that bodies are typically supported by the scene. A physics engine can be used to enforce physical plausibility, but these are not differentiable, rely on unrealistic proxy bodies, and are difficult to integrate into existing optimization and learning frameworks. In contrast, we exploit novel intuitive-physics (IP) terms that can be inferred from a 3D SMPL body interacting with the scene. Inspired by biomechanics, we infer the pressure heatmap on the body, the Center of Pressure (CoP) from the heatmap, and the SMPL body's Center of Mass (CoM). With these, we develop IPMAN, to estimate a 3D body from a color image in a "stable" configuration by encouraging plausible floor contact and overlapping CoP and CoM. Our IP terms are intuitive, easy to implement, fast to compute, differentiable, and can be integrated into existing optimization and regression methods. We evaluate IPMAN on standard datasets and MoYo, a new dataset with synchronized multi-view images, ground-truth 3D bodies with complex poses, body-floor contact, CoM and pressure. IPMAN produces more plausible results than the state of the art, improving accuracy for static poses, while not hurting dynamic ones. Code and data are available for research at https://ipman.is.tue.mpg.de.
A Survey of Large Language Models
Authors: Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, YiFan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen
Date: 2023-03-31 00:00:00
Tasks: Language Modeling, Language Modelling, Survey

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.
Constrained Optimization of Rank-One Functions with Indicator Variables
Authors: Soroosh Shafiee, Fatma Kılınç-Karzan
Date: 2023-03-31 00:00:00

Optimization problems involving minimization of a rank-one convex function over constraints modeling restrictions on the support of the decision variables emerge in various machine learning applications. These problems are often modeled with indicator variables for identifying the support of the continuous variables. In this paper we investigate compact extended formulations for such problems through perspective reformulation techniques. In contrast to the majority of previous work that relies on support function arguments and disjunctive programming techniques to provide convex hull results, we propose a constructive approach that exploits a hidden conic structure induced by perspective functions. To this end, we first establish a convex hull result for a general conic mixed-binary set in which each conic constraint involves a linear function of independent continuous variables and a set of binary variables. We then demonstrate that extended representations of sets associated with epigraphs of rank-one convex functions over constraints modeling indicator relations naturally admit such a conic representation. This enables us to systematically give perspective formulations for the convex hull descriptions of these sets with nonlinear separable or non-separable objective functions, sign constraints on continuous variables, and combinatorial constraints on indicator variables. We illustrate the efficacy of our results on sparse nonnegative logistic regression problems.