pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Deep MIMO Detection
Authors: Qiang Hu, Feifei Gao, Hao Zhang, Geoffrey Y. Li, Zongben Xu
Date: 2021-05-11 00:00:00

Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has been deemed as a promising technique for future wireless communications. However, most DL-based detection algorithms are lack of theoretical interpretation on internal mechanisms and could not provide general guidance on network design. In this paper, we analyze the performance of DL-based MIMO detection to better understand its strengths and weaknesses. We investigate two different architectures: a data-driven DL detector with a neural network activated by rectifier linear unit (ReLU) function and a model-driven DL detector from unfolding a traditional iterative detection algorithm. We demonstrate that data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires enough training samples to converge in time-varying channels. On the other hand, the model-driven DL detector utilizes model expert knowledge to alleviate the impact of channels and establish a relatively reliable detection method with a small set of training data. Due to its model specific property, the performance of model-driven DL detector is largely determined by the underlying iterative detection algorithm, which is usually suboptimal compared to the MAP detector. Simulation results confirm our analytical results and demonstrate the effectiveness of DL-based MIMO detection for both linear and nonlinear signal systems.
SHREC 2021: Retrieval and classification of protein surfaces equipped with physical and chemical properties
Authors: Andrea Raffo, Ulderico Fugacci, Silvia Biasotti, Walter Rocchia, Yonghuai Liu, Ekpo Otu, Reyer Zwiggelaar, David Hunter, Evangelia I. Zacharaki, Eleftheria Psatha, Dimitrios Laskos, Gerasimos Arvanitis, Konstantinos Moustakas, Tunde Aderinwale, Charles Christoffer, Woong-Hee Shin, Daisuke Kihara, Andrea Giachetti, Huu-Nghia Nguyen, Tuan-Duy Nguyen, Vinh-Thuyen Nguyen-Truong, Danh Le-Thanh, Hai-Dang Nguyen, Minh-Triet Tran
Date: 2021-05-11 00:00:00
Tasks: Information Retrieval, Retrieval

This paper presents the methods that have participated in the SHREC 2021 contest on retrieval and classification of protein surfaces on the basis of their geometry and physicochemical properties. The goal of the contest is to assess the capability of different computational approaches to identify different conformations of the same protein, or the presence of common sub-parts, starting from a set of molecular surfaces. We addressed two problems: defining the similarity solely based on the surface geometry or with the inclusion of physicochemical information, such as electrostatic potential, amino acid hydrophobicity, and the presence of hydrogen bond donors and acceptors. Retrieval and classification performances, with respect to the single protein or the existence of common sub-sequences, are analysed according to a number of information retrieval indicators.
Retro Drug Design: From Target Properties to Molecular Structures
Authors: Yuhong Wang, Sam Michael, Ruili Huang, Jinghua Zhao, Katlin Recabo, Danielle Bougie, Qiang Shu, Paul Shinn, Hongmao Sun
Date: 2021-05-11 00:00:00
Tasks: Drug Design, Drug Discovery, valid

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate {\mu} opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.
ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning
Authors: Yeji Wang, Shuo Wu, Yanwen Duan, Yong Huang
Date: 2021-04-17 00:00:00
Tasks: Drug Design, Molecular Docking, Prediction, Protein-Ligand Affinity Prediction

Motivation: Protein-ligand affinity prediction is an important part of structure-based drug design. It includes molecular docking and affinity prediction. Although molecular dynamics can predict affinity with high accuracy at present, it is not suitable for large-scale virtual screening. The existing affinity prediction and evaluation functions based on deep learning mostly rely on experimentally-determined conformations. Results: We build a predictive model of protein-ligand affinity through the ResNet neural network with added attention mechanism. The resulting ResAtom-Score model achieves Pearson's correlation coefficient R = 0.833 on the CASF-2016 benchmark test set. At the same time, we evaluated the performance of a variety of existing scoring functions in combination with ResAtom-Score in the absence of experimentally-determined conformations. The results show that the use of {\Delta}VinaRF20 in combination with ResAtom-Score can achieve affinity prediction close to scoring functions in the presence of experimentally-determined conformations. These results suggest that ResAtom system may be used for in silico screening of small molecule ligands with target proteins in the future. Availability: https://github.com/wyji001/ResAtom
The quest to quantify selective and synergistic effects of plasma for cancer treatment: Insights from mathematical modeling
Authors: Charlotta Bengtson, Annemie Bogaerts
Date: 2021-05-11 00:00:00

Cold atmospheric plasma (CAP) and plasma-treated liquids (PTLs) have recently become a promising option for cancer treatment, but the underlying mechanisms of the anti-cancer effect are still to a large extent unknown. Although hydrogen peroxide has been recognized as the major anti-cancer agent of PTL and may enable selectivity in a certain concentration regime, the co-existence of nitrite can create a synergistic effect. We develop a mathematical model to describe the key species and features of the cellular response towards PTL. From the numerical solutions, we define a number of dependent variables, which represent feasible measures to quantify cell susceptibility in terms of the hydrogen peroxide membrane diffusion rate constant and the intracellular catalase concentration. For each of these dependent variables, we investigate the regimes of selective versus non-selective, and of synergistic versus non-synergistic effect to evaluate their potential role as a measure of cell susceptibility. Our results suggest that the maximal intracellular hydrogen peroxide concentration, which in the selective regime is almost four times greater for the most susceptible cells compared to the most resistant cells, could be used to quantify the cell susceptibility towards exogenous hydrogen peroxide. We believe our theoretical approach brings novelty to the field of plasma oncology, and more broadly, to the field of redox biology, by proposing new ways to quantify the selective and synergistic anti-cancer effect of PTL in terms of inherent cell features.
Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution
Authors: Stefan Dasbach, Tom Tetzlaff, Markus Diesmann, Johanna Senk
Date: 2021-05-11 00:00:00

The representation of the natural-density, heterogeneous connectivity of neuronal network models at relevant spatial scales remains a challenge for Computational Neuroscience and Neuromorphic Computing. In particular, the memory demands imposed by the vast number of synapses in brain-scale network simulations constitutes a major obstacle. Limiting the number resolution of synaptic weights appears to be a natural strategy to reduce memory and compute load. In this study, we investigate the effects of a limited synaptic-weight resolution on the dynamics of recurrent spiking neuronal networks resembling local cortical circuits, and develop strategies for minimizing deviations from the dynamics of networks with high-resolution synaptic weights. We mimic the effect of a limited synaptic weight resolution by replacing normally distributed synaptic weights by weights drawn from a discrete distribution, and compare the resulting statistics characterizing firing rates, spike-train irregularity, and correlation coefficients with the reference solution. We show that a naive discretization of synaptic weights generally leads to a distortion of the spike-train statistics. Only if the weights are discretized such that the mean and the variance of the total synaptic input currents are preserved, the firing statistics remains unaffected for the types of networks considered in this study. For networks with sufficiently heterogeneous in-degrees, the firing statistics can be preserved even if all synaptic weights are replaced by the mean of the weight distribution. We conclude that even for simple networks with non-plastic neurons and synapses, a discretization of synaptic weights can lead to substantial deviations in the firing statistics, unless the discretization is performed with care and guided by a rigorous validation process.
Normative brain mapping of interictal intracranial EEG to localise epileptogenic tissue
Authors: Peter N Taylor, Christoforos A Papasavvas, Thomas W Owen, Gabrielle M Schroeder, Frances E Hutchings, Fahmida A Chowdhury, Beate Diehl, John S Duncan, Andrew W McEvoy, Anna Miserocchi, Jane de Tisi, Sjoerd B Vos, Matthew C Walker, Yujiang Wang
Date: 2021-05-10 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localising epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner. To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 subjects (21,598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We hypothesised that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures post-operatively. We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions which were spared by surgery were more abnormal than those resected only in patients with persistent post-operative seizures (t=-3.6, p=0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (AUC=0.75 p=0.0003). Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyond.
Can an Agency Role-Reversal Lead to an Organizational Collapse?; A Study Proposal
Authors: Yossi Haimberg
Date: 2021-05-10 00:00:00

The Principal-Agent Theory model is widely used to explain governance role where there is a separation of ownership and control, as it defines clear boundaries between governance and executives. However, examination of recent corporate failure reveals the concerning contribution of the Board of Directors to such failures and calls into question governance effectiveness in the presence of a powerful and charismatic CEO. This study proposes a framework for analyzing the relationship between the Board of Directors and the CEO, and how certain relationships affect the power structure and behavior of the Board, which leads to a role reversal in the Principal-Agent Theory, as the Board assumes the role of the CEO's agent. This study's results may help create a red flag for a board and leader's behavior that may result in governance failure.
Winterization of Texan power system infrastructure is profitable but risky
Authors: Katharina Gruber, Tobias Gauster, Peter Regner, Gregor Laaha, Johannes Schmidt
Date: 2021-04-21 00:00:00
Tasks: Time Series, Time Series Analysis

We deliver the first analysis of the 2021 cold spell in Texas which combines temperature dependent load estimates with temperature dependent estimates of power plant outages to understand the frequency of loss of load events, using a 71 year long time series of climate data. The expected revenue from full winterization is 11.74bn$ over a 30 years investment period. We find that large-scale winterization, in particular of gas infrastructure and gas power plants, would be profitable, as related costs for winterization are substantially lower. At the same moment, the associated investment risks are high due to the low-frequency of events - the 2021 event was the largest and we observe only 8 other similar ones for the simulated 71 years. As risks to investors are considerable, regulatory measures may be necessary to enforce winterization.
Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs
Authors: Manash Pratim Das, Anirudh Vemula, Mayank Pathak, Sandip Aine, Maxim Likhachev
Date: 2021-03-13 00:00:00
Tasks: Decision Making, Multi-class Classification

Consider a truck filled with boxes of varying size and unknown mass and an industrial robot with end-effectors that can unload multiple boxes from any reachable location. In this work, we investigate how would the robot with the help of a simulator, learn to maximize the number of boxes unloaded by each action. Most high-fidelity robotic simulators like ours are time-consuming. Therefore, we investigate the above learning problem with a focus on minimizing the number of simulation runs required. The optimal decision-making problem under this setting can be formulated as a multi-class classification problem. However, to obtain the outcome of any action requires us to run the time-consuming simulator, thereby restricting the amount of training data that can be collected. Thus, we need a data-efficient approach to learn the classifier and generalize it with a minimal amount of data. A high-fidelity physics-based simulator is common in general for complex manipulation tasks involving multi-body interactions. To this end, we train an optimal decision tree as the classifier, and for each branch of the decision tree, we reason about the confidence in the decision using a Probably Approximately Correct (PAC) framework to determine whether more simulator data will help reach a certain confidence level. This provides us with a mechanism to evaluate when simulation can be avoided for certain decisions, and when simulation will improve the decision making. For the truck unloading problem, our experiments show that a significant reduction in simulator runs can be achieved using the proposed method as compared to naively running the simulator to collect data to train equally performing decision trees.
Keep Me Around: Intron Retention Detection and Analysis
Authors: Harold Pimentel, John G. Conboy, Lior Pachter
Date: 2015-10-02 00:00:00

We present a tool, keep me around (kma), a suite of python scripts and an R package that finds retained introns in RNA-Seq experiments and incorporates biological replicates to reduce the number of false positives when detecting retention events. kma uses the results of existing quantification tools that probabilistically assign multi-mapping reads, thus interfacing easily with transcript quantification pipelines. The data is represented in a convenient, database style format that allows for easy aggregation across introns, genes, samples, and conditions to allow for further exploratory analysis.
The Greedy and Recursive Search for Morphological Productivity
Authors: Caleb Belth, Sarah Payne, Deniz Beser, Jordan Kodner, Charles Yang
Date: 2021-05-12 00:00:00

As children acquire the knowledge of their language's morphology, they invariably discover the productive processes that can generalize to new words. Morphological learning is made challenging by the fact that even fully productive rules have exceptions, as in the well-known case of English past tense verbs, which features the -ed rule against the irregular verbs. The Tolerance Principle is a recent proposal that provides a precise threshold of exceptions that a productive rule can withstand. Its empirical application so far, however, requires the researcher to fully specify rules defined over a set of words. We propose a greedy search model that automatically hypothesizes rules and evaluates their productivity over a vocabulary. When the search for broader productivity fails, the model recursively subdivides the vocabulary and continues the search for productivity over narrower rules. Trained on psychologically realistic data from child-directed input, our model displays developmental patterns observed in child morphology acquisition, including the notoriously complex case of German noun pluralization. It also produces responses to nonce words that, despite receiving only a fraction of the training data, are more similar to those of human subjects than current neural network models' responses are.
Probabilistic Loss and its Online Characterization for Simplified Decision Making Under Uncertainty
Authors: Andrey Zhitnikov, Vadim Indelman
Date: 2021-05-12 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

It is a long-standing objective to ease the computation burden incurred by the decision making process. Identification of this mechanism's sensitivity to simplification has tremendous ramifications. Yet, algorithms for decision making under uncertainty usually lean on approximations or heuristics without quantifying their effect. Therefore, challenging scenarios could severely impair the performance of such methods. In this paper, we extend the decision making mechanism to the whole by removing standard approximations and considering all previously suppressed stochastic sources of variability. On top of this extension, our key contribution is a novel framework to simplify decision making while assessing and controlling online the simplification's impact. Furthermore, we present novel stochastic bounds on the return and characterize online the effect of simplification using this framework on a particular simplification technique - reducing the number of samples in belief representation for planning. Finally, we verify the advantages of our approach through extensive simulations.
Exploring the Similarity of Representations in Model-Agnostic Meta-Learning
Authors: Thomas Goerttler, Klaus Obermayer
Date: 2021-05-12 00:00:00
Proceeding: ICLR Workshop Learning_to_Learn 2021 5
Tasks: Few-Shot Learning, Meta-Learning

In past years model-agnostic meta-learning (MAML) has been one of the most promising approaches in meta-learning. It can be applied to different kinds of problems, e.g., reinforcement learning, but also shows good results on few-shot learning tasks. Besides their tremendous success in these tasks, it has still not been fully revealed yet, why it works so well. Recent work proposes that MAML rather reuses features than rapidly learns. In this paper, we want to inspire a deeper understanding of this question by analyzing MAML's representation. We apply representation similarity analysis (RSA), a well-established method in neuroscience, to the few-shot learning instantiation of MAML. Although some part of our analysis supports their general results that feature reuse is predominant, we also reveal arguments against their conclusion. The similarity-increase of layers closer to the input layers arises from the learning task itself and not from the model. In addition, the representations after inner gradient steps make a broader change to the representation than the changes during meta-training.
Building a Question and Answer System for News Domain
Authors: Sandipan Basu, Aravind Gaddala, Pooja Chetan, Garima Tiwari, Narayana Darapaneni, Sadwik Parvathaneni, Anwesh Reddy Paduri
Date: 2021-05-12 00:00:00
Tasks: Articles, Question Answering, Sentence, Transfer Learning

This project attempts to build a Question- Answering system in the News Domain, where Passages will be News articles, and anyone can ask a Question against it. We have built a span-based model using an Attention mechanism, where the model predicts the answer to a question as to the position of the start and end tokens in a paragraph. For training our model, we have used the Stanford Question and Answer (SQuAD 2.0) dataset[1]. To do well on SQuAD 2.0, systems must not only answer questions when possible but also determine when no answer is supported by the paragraph and abstain from answering. Our model architecture comprises three layers- Embedding Layer, RNN Layer, and the Attention Layer. For the Embedding layer, we used GloVe and the Universal Sentence Encoder. For the RNN Layer, we built variations of the RNN Layer including bi-LSTM and Stacked LSTM and we built an Attention Layer using a Context to Question Attention and also improvised on the innovative Bidirectional Attention Layer. Our best performing model which uses GloVe Embedding combined with Bi-LSTM and Context to Question Attention achieved an F1 Score and EM of 33.095 and 33.094 respectively. We also leveraged transfer learning and built a Transformer based model using BERT. The BERT-based model achieved an F1 Score and EM of 57.513 and 49.769 respectively. We concluded that the BERT model is superior in all aspects of answering various types of questions.