pwc-archive/papers-with-abstracts huggingface dataset:
Collision cross section specificity for small molecule identification workflows
Authors: Jamie Nunez, Eva Brayfindley, Sean M. Colby, Monee McGrady, Kristin H. Jarman, Ryan S. Renslow, Thomas O. Metz
Date: 2021-11-04 00:00:00
Tasks: Specificity

The physical-chemical property of molecular collision cross section (CCS) is increasingly used to assist in small molecule identification; however, questions remain regarding the extent of its true utility in contributing to such identifications, especially given its correlation with mass. To investigate the contribution of CCS to uniqueness within a given library, we measured its discriminatory capacity as a function of error in CCS values (from measurement or prediction), CCS variance, parent mass, mass error, and/or reference database size using a multi-directional grid search. While experimental CCS databases exist, they are currently small; thus, we used a CCS prediction tool, DarkChem, to provide theoretical CCS values for use in this study. These predicted CCS values were then modified to mirror experimental variance. By augmenting our search within a library based on mass alone with CCS at a variety of accuracies, we found that, (i) the use of multiple adducts (i.e. alternative ionized forms of the same parent compound) for the same molecule, compared to using a single adduct, greatly improves specificity and (ii) even a single CCS leads to a significant specificity boost when low CCS error (e.g. 1% composite error) can be achieved. Based on these results, we recommend using multiple adducts to build up evidence of presence, as each adduct supplies additional information per dimension. Additionally, the utility of ion mobility spectrometry when coupled with mass spectrometry should still be considered, regardless of whether CCS is considered as an identification metric, due to advantages such as increased peak resolution, sensitivity (e.g. from reducing load on the detector at any given time), improvements in data-independent MS/MS spectra acquisition, and cleaner tandem mass spectral fragmentation patterns.
Sequence learning, prediction, and replay in networks of spiking neurons
Authors: Younes Bouhadjar, Dirk J. Wouters, Markus Diesmann, Tom Tetzlaff
Date: 2021-11-05 00:00:00

Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on electrophysiological and behavioral data.
A SEIR model with time-varying coefficients for analysing the SARS-CoV-2 epidemic
Authors: P. Girardi, C. Gaetan
Date: 2021-11-04 00:00:00

In this study, we propose a time-dependent Susceptible-Exposed-Infected-Recovered (SEIR) model for the analysis of the SARS-CoV-2 epidemic outbreak in three different countries, the United States of America, Italy and Iceland using public data inherent the numbers of the epidemic wave. Since several types and grades of actions were adopted by the governments, including travel restrictions, social distancing, or limitation of movement, we want to investigate how these measures can affect the epidemic curve of the infectious population. The parameters of interest for the SEIR model were estimated employing a composite likelihood approach. Moreover, standard errors have been corrected for temporal dependence. The adoption of restrictive measures results in flatten epidemic curves, and the future evolution indicated a decrease in the number of cases.
A nutritional strategy to promote gilthead seabream performance under low temperatures
Authors: Rita Teodósio, Cláudia Aragão, Rita Colen, Raquel Carrilho, Jorge Dias, Sofia Engrola
Date: 2021-11-05 00:00:00

Gilthead seabream (Sparus aurata) is vulnerable to low water temperature, which may occur in the Southern Europe and Mediterranean region during Winter. Fish are poikilothermic animals, so feed intake, digestion, metabolism and ultimately growth are affected by water temperature. This study aimed to evaluate growth performance, feed utilisation, nutrient apparent digestibility, and N losses to the environment in seabream juveniles reared under low temperature (13 degrees Celsius). Three isolipid and isoenergetic diets were formulated: a commercial-like diet (COM) with 44% crude protein and 27.5% fishmeal; and 2 diets with 42% CP (ECO and ECOSup), reduced FM inclusion, and 15% poultry meal. ECOSup diet was supplemented with a mix of feed additives intended to promote fish growth and feed intake. The ECO diets presented lower production costs than the COM diet and included more sustainable ingredients. Seabream juveniles (154.5 g) were randomly assigned to triplicate tanks and fed the diets for 84 days. Fish fed the ECOSup and COM diets attained a similar final body weight. ECOSup fed fish presented significantly higher HSI than COM fed fish, probably due to higher hepatic glycogen reserves. The VSI of ECOSup fed fish were significantly lower compared to COM fed fish, which is a positive achievement from a consumer point of view. Nutrient digestibility was similar in ECOSup and COM diets. Feeding fish with the ECO diets resulted in lower faecal N losses when compared to COM fed fish. Feeding seabream with an eco-friendly diet with a mix of feed additives promoted growth, improved fish nutritional status and minimised N losses to the environment whilst lowering production costs. Nutritional strategies that ultimately promote feed intake and diet utilisation are valuable tools that may help conditioning fish to sustain growth even under adverse conditions.
Decrease of capital guarantees in life insurance products: can reinsurance stop it?
Authors: Marcos Escobar-Anel, Yevhen Havrylenko, Michel Kschonnek, Rudi Zagst
Date: 2021-11-05 00:00:00

We analyze the potential of reinsurance for reversing the current trend of decreasing capital guarantees in life insurance products. Providing an insurer with an opportunity to shift part of the financial risk to a reinsurer, we solve the insurer's dynamic investment-reinsurance optimization problem under simultaneous Value-at-Risk and no-short-selling constraints. We introduce the concept of guarantee-equivalent utility gain and use it to compare life insurance products with and without reinsurance. Our numerical studies indicate that the optimally managed reinsurance allows the insurer to offer significantly higher capital guarantees to clients without any loss in the insurer's expected utility. The longer the investment horizon and the less risk-averse the insurer, the more prominent the reinsurance benefit.
Cyber Risk Frequency, Severity and Insurance Viability
Authors: Matteo Malavasi, Gareth W. Peters, Pavel V. Shevchenko, Stefan Trück, Jiwook Jang, Georgy Sofronov
Date: 2021-11-05 00:00:00
Tasks: Additive models, regression

In this study an exploration of insurance risk transfer is undertaken for the cyber insurance industry in the United States of America, based on the leading industry dataset of cyber events provided by Advisen. We seek to address two core unresolved questions. First, what factors are the most significant covariates that may explain the frequency and severity of cyber loss events and are they heterogeneous over cyber risk categories? Second, is cyber risk insurable in regards to the required premiums, risk pool sizes and how would this decision vary with the insured companies industry sector and size? We address these questions through a combination of regression models based on the class of Generalised Additive Models for Location Shape and Scale (GAMLSS) and a class of ordinal regressions. These models will then form the basis for our analysis of frequency and severity of cyber risk loss processes. We investigate the viability of insurance for cyber risk using a utility modelling framework with premium calculated by classical certainty equivalence analysis utilising the developed regression models. Our results provide several new key insights into the nature of insurability of cyber risk and rigorously address the two insurance questions posed in a real data driven case study analysis.
Converting tabular data into images for deep learning with convolutional neural networks
Authors: Yitan Zhu, Thomas Brettin, Fangfang Xia, Alexander Partin, Maulik Shukla, Hyunseung Yoo, Yvonne A. Evrard, James H. Doroshow, Rick L. Stevens
Date: 2021-07-01 00:00:00
Proceeding: Scientific Reports 2021 7

Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.
Omnipose: a high-precision, morphology-independent solution for bacterial cell segmentation
Authors: Kevin J. Cutler, Carsen Stringer, Paul A. Wiggins, Joseph D. Mougous
Date: 2021-11-05 00:00:00
Proceeding: bioRxiv 2021 11
Tasks: Cell Segmentation, Vocal Bursts Intensity Prediction

Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the context of characterizing extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.
Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review
Authors: Rakhmatulin, Ildar
Date: 2021-11-08 00:00:00
Proceeding: Remote Sens 2021 11
Tasks: Object Detection, Transfer Learning

Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.
Multi-target Joint Tracking and Classification Using the Trajectory PHD Filter
Authors: Shaoxiu Wei, Boxiang Zhang, Wei Yi
Date: 2021-11-06 00:00:00

To account for joint tracking and classification (JTC) of multiple targets from observation sets in presence of detection uncertainty, noise and clutter, this paper develops a new trajectory probability hypothesis density (TPHD) filter, which is referred to as the JTC-TPHD filter. The JTC-TPHD filter classifies different targets based on their motion models and each target is assigned with multiple class hypotheses. By using this strategy, we can not only obtain the category information of the targets, but also a more accurate trajectory estimation than the traditional TPHD filter. The JTC-TPHD filter is derived by finding the best Poisson posterior approximation over trajectories on an augmented state space using the Kullback-Leibler divergence (KLD) minimization. The Gaussian mixture is adopted for the implementation, which is referred to as the GM-JTC-TPHD filter. The L-scan approximation is also presented for the GM-JTC-TPHD filter, which possesses lower computational burden. Simulation results show that the GM-JTC-TPHD filter can classify targets correctly and obtain accurate trajectory estimation.
Trajectory PHD Filter with Unknown Detection Profile and Clutter Rate
Authors: Shaoxiu Wei, Boxiang Zhang, Wei Yi
Date: 2021-11-06 00:00:00

In this paper, we derive the robust TPHD (R-TPHD) filter, which can adaptively learn the unknown detection profile history and clutter rate. The R-TPHD filter is derived by obtaining the best Poisson posterior density approximation over trajectories on hybrid and augmented state space by minimizing the Kullback-Leibler divergence (KLD). Because of the huge computational burden and the short-term stability of the detection profile, we also propose the R-TPHD filter with unknown detection profile only at current time as an approximation. The Beta-Gaussian mixture model is proposed for the implementation, which is referred to as the BG-R-TPHD filter and we also propose a L-scan approximation for the BG-R-TPHD filter, which possesses lower computational burden.
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
Authors: Bicheng Ying, Kun Yuan, Hanbin Hu, Yiming Chen, Wotao Yin
Date: 2021-11-08 00:00:00
Tasks: Deep Learning

Decentralized algorithm is a form of computation that achieves a global goal through local dynamics that relies on low-cost communication between directly-connected agents. On large-scale optimization tasks involving distributed datasets, decentralized algorithms have shown strong, sometimes superior, performance over distributed algorithms with a central node. Recently, developing decentralized algorithms for deep learning has attracted great attention. They are considered as low-communication-overhead alternatives to those using a parameter server or the Ring-Allreduce protocol. However, the lack of an easy-to-use and efficient software package has kept most decentralized algorithms merely on paper. To fill the gap, we introduce BlueFog, a python library for straightforward, high-performance implementations of diverse decentralized algorithms. Based on a unified abstraction of various communication operations, BlueFog offers intuitive interfaces to implement a spectrum of decentralized algorithms, from those using a static, undirected graph for synchronous operations to those using dynamic and directed graphs for asynchronous operations. BlueFog also adopts several system-level acceleration techniques to further optimize the performance on the deep learning tasks. On mainstream DNN training tasks, BlueFog reaches a much higher throughput and achieves an overall $1.2\times \sim 1.8\times$ speedup over Horovod, a state-of-the-art distributed deep learning package based on Ring-Allreduce. BlueFog is open source at https://github.com/Bluefog-Lib/bluefog.
Natural Adversarial Objects
Authors: Felix Lau, Nishant Subramani, Sasha Harrison, Aerin Kim, Elliot Branson, Rosanne Liu
Date: 2021-11-07 00:00:00
Tasks: Object, object-detection, Object Detection

Although state-of-the-art object detection methods have shown compelling performance, models often are not robust to adversarial attacks and out-of-distribution data. We introduce a new dataset, Natural Adversarial Objects (NAO), to evaluate the robustness of object detection models. NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence. The mean average precision (mAP) of EfficientDet-D7 drops 74.5% when evaluated on NAO compared to the standard MSCOCO validation set. Moreover, by comparing a variety of object detection architectures, we find that better performance on MSCOCO validation set does not necessarily translate to better performance on NAO, suggesting that robustness cannot be simply achieved by training a more accurate model. We further investigate why examples in NAO are difficult to detect and classify. Experiments of shuffling image patches reveal that models are overly sensitive to local texture. Additionally, using integrated gradients and background replacement, we find that the detection model is reliant on pixel information within the bounding box, and insensitive to the background context when predicting class labels. NAO can be downloaded at https://drive.google.com/drive/folders/15P8sOWoJku6SSEiHLEts86ORfytGezi8.
Transformer Based Bengali Chatbot Using General Knowledge Dataset
Authors: Abu Kaisar Mohammad Masum, Sheikh Abujar, Sharmin Akter, Nushrat Jahan Ria, Syed Akhter Hossain
Date: 2021-11-06 00:00:00
Tasks: Chatbot, Decoder, General Knowledge

An AI chatbot provides an impressive response after learning from the trained dataset. In this decade, most of the research work demonstrates that deep neural models superior to any other model. RNN model regularly used for determining the sequence-related problem like a question and it answers. This approach acquainted with everyone as seq2seq learning. In a seq2seq model mechanism, it has encoder and decoder. The encoder embedded any input sequence, and the decoder embedded output sequence. For reinforcing the seq2seq model performance, attention mechanism added into the encoder and decoder. After that, the transformer model has introduced itself as a high-performance model with multiple attention mechanism for solving the sequence-related dilemma. This model reduces training time compared with RNN based model and also achieved state-of-the-art performance for sequence transduction. In this research, we applied the transformer model for Bengali general knowledge chatbot based on the Bengali general knowledge Question Answer (QA) dataset. It scores 85.0 BLEU on the applied QA data. To check the comparison of the transformer model performance, we trained the seq2seq model with attention on our dataset that scores 23.5 BLEU.
Federated Learning Based on Dynamic Regularization
Authors: Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama
Date: 2021-11-08 00:00:00
Proceeding: ICLR 2021 1
Tasks: Federated Learning

We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round. We view Federated Learning problem primarily from a communication perspective and allow more device level computations to save transmission costs. We point out a fundamental dilemma, in that the minima of the local-device level empirical loss are inconsistent with those of the global empirical loss. Different from recent prior works, that either attempt inexact minimization or utilize devices for parallelizing gradient computation, we propose a dynamic regularizer for each device at each round, so that in the limit the global and device solutions are aligned. We demonstrate both through empirical results on real and synthetic data as well as analytical results that our scheme leads to efficient training, in both convex and non-convex settings, while being fully agnostic to device heterogeneity and robust to large number of devices, partial participation and unbalanced data.
Robust Deep Reinforcement Learning for Quadcopter Control
Authors: Aditya M. Deshpande, Ali A. Minai, Manish Kumar
Date: 2021-11-06 00:00:00
Tasks: Deep Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (RL) has made it possible to solve complex robotics problems using neural networks as function approximators. However, the policies trained on stationary environments suffer in terms of generalization when transferred from one environment to another. In this work, we use Robust Markov Decision Processes (RMDP) to train the drone control policy, which combines ideas from Robust Control and RL. It opts for pessimistic optimization to handle potential gaps between policy transfer from one environment to another. The trained control policy is tested on the task of quadcopter positional control. RL agents were trained in a MuJoCo simulator. During testing, different environment parameters (unseen during the training) were used to validate the robustness of the trained policy for transfer from one environment to another. The robust policy outperformed the standard agents in these environments, suggesting that the added robustness increases generality and can adapt to non-stationary environments. Codes: https://github.com/adipandas/gym_multirotor