pwc-archive/papers-with-abstracts huggingface dataset:
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Authors: Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, Boris Kozinsky
Date: 2021-01-08 00:00:00

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.
Progressive Interpretation Synthesis: Interpreting Task Solving by Quantifying Previously Used and Unused Information
Authors: Zhengqi He, Taro Toyoizumi
Date: 2021-01-08 00:00:00

A deep neural network is a good task solver, but it is difficult to make sense of its operation. People have different ideas about how to form the interpretation about its operation. We look at this problem from a new perspective where the interpretation of task solving is synthesized by quantifying how much and what previously unused information is exploited in addition to the information used to solve previous tasks. First, after learning several tasks, the network acquires several information partitions related to each task. We propose that the network, then, learns the minimal information partition that supplements previously learned information partitions to more accurately represent the input. This extra partition is associated with un-conceptualized information that has not been used in previous tasks. We manage to identify what un-conceptualized information is used and quantify the amount. To interpret how the network solves a new task, we quantify as meta-information how much information from each partition is extracted. We implement this framework with the variational information bottleneck technique. We test the framework with the MNIST and the CLEVR dataset. The framework is shown to be able to compose information partitions and synthesize experience-dependent interpretation in the form of meta-information. This system progressively improves the resolution of interpretation upon new experience by converting a part of the un-conceptualized information partition to a task-related partition. It can also provide a visual interpretation by imaging what is the part of previously un-conceptualized information that is needed to solve a new task.
Artificial Intelligence enabled Smart Learning
Authors: Faisal Khan, Debdeep Bose
Date: 2021-01-08 00:00:00

Artificial Intelligence (AI) is a discipline of computer science that deals with machine intelligence. It is essential to bring AI into the context of learning because it helps in analysing the enormous amounts of data that is collected from individual students, teachers and academic staff. The major priorities of implementing AI in education are making innovative use of existing digital technologies for learning, and teaching practices that significantly improve traditional educational methods. The main problem with traditional learning is that it cannot be suited to every student in class. Some students may grasp the concepts well, while some may have difficulties in understanding them and some may be more auditory or visual learners. The World Bank report on education has indicated that the learning gap created by this problem causes many students to drop out (World Development Report, 2018). Personalised learning has been able to solve this grave problem.
Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge
Authors: Takuma Yoneda, Charles Schaff, Takahiro Maeda, Matthew Walter
Date: 2021-01-08 00:00:00
Tasks: Motion Planning

This report describes our winning submission to the Real Robot Challenge (https://real-robot-challenge.com/). The Real Robot Challenge is a three-phase dexterous manipulation competition that involves manipulating various rectangular objects with the TriFinger Platform. Our approach combines motion planning with several motion primitives to manipulate the object. For Phases 1 and 2, we additionally learn a residual policy in simulation that applies corrective actions on top of our controller. Our approach won first place in Phase 2 and Phase 3 of the competition. We were anonymously known as `ardentstork' on the competition leaderboard (https://real-robot-challenge.com/leader-board). Videos and our code can be found at https://github.com/ripl-ttic/real-robot-challenge.
Approximate Query Processing for Group-By Queries based on Conditional Generative Models
Authors: Meifan Zhang, Hongzhi Wang
Date: 2021-01-08 00:00:00
Tasks: Data Visualization

The Group-By query is an important kind of query, which is common and widely used in data warehouses, data analytics, and data visualization. Approximate query processing is an effective way to increase the querying efficiency on big data. The answer to a group-by query involves multiple values, which makes it difficult to provide sufficiently accurate estimations for all the groups. Stratified sampling improves the accuracy compared with the uniform sampling, but the samples chosen for some special queries cannot work for other queries. Online sampling chooses samples for the given query at query time, but it requires a long latency. Thus, it is a challenge to achieve both accuracy and efficiency at the same time. Facing such challenge, in this work, we propose a sample generation framework based on a conditional generative model. The sample generation framework can generate any number of samples for the given query without accessing the data. The proposed framework based on the lightweight model can be combined with stratified sampling and online aggregation to improve the estimation accuracy for group-by queries. The experimental results show that our proposed methods are both efficient and accurate.
Slow manifolds in recurrent networks encode working memory efficiently and robustly
Authors: Elham Ghazizadeh, ShiNung Ching
Date: 2021-01-08 00:00:00

Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience and machine intelligence. We train thousands of recurrent neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new dynamical hypotheses regarding how working memory function is encoded in both natural and artificial neural networks.
When does the Physarum Solver Distinguish the Shortest Path from other Paths: the Transition Point and its Applications
Authors: Yusheng Huang, Dong Chu, Joel Weijia Lai, Yong Deng, Kang Hao Cheong
Date: 2021-01-08 00:00:00

Physarum solver, also called the physarum polycephalum inspired algorithm (PPA), is a newly developed bio-inspired algorithm that has an inherent ability to find the shortest path in a given graph. Recent research has proposed methods to develop this algorithm further by accelerating the original PPA (OPPA)'s path-finding process. However, when does the PPA ascertain that the shortest path has been found? Is there a point after which the PPA could distinguish the shortest path from other paths? By innovatively proposing the concept of the dominant path (D-Path), the exact moment, named the transition point (T-Point), when the PPA finds the shortest path can be identified. Based on the D-Path and T-Point, a newly accelerated PPA named OPPA-D using the proposed termination criterion is developed which is superior to all other baseline algorithms according to the experiments conducted in this paper. The validity and the superiority of the proposed termination criterion is also demonstrated. Furthermore, an evaluation method is proposed to provide new insights for the comparison of different accelerated OPPAs. The breakthrough of this paper lies in using D-path and T-point to terminate the OPPA. The novel termination criterion reveals the actual performance of this OPPA. This OPPA is the fastest algorithm, outperforming some so-called accelerated OPPAs. Furthermore, we explain why some existing works inappropriately claim to be accelerated algorithms is in fact a product of inappropriate termination criterion, thus giving rise to the illusion that the method is accelerated.
Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks
Authors: Zhenzhong Wang, Haokai Hong, Kai Ye, Min Jiang, Kay Chen Tan
Date: 2021-01-08 00:00:00
Tasks: Diversity, Evolutionary Algorithms, Generative Adversarial Network, Multiobjective Optimization

Large-scale multiobjective optimization problems (LSMOPs) are characterized as involving hundreds or even thousands of decision variables and multiple conflicting objectives. An excellent algorithm for solving LSMOPs should find Pareto-optimal solutions with diversity and escape from local optima in the large-scale search space. Previous research has shown that these optimal solutions are uniformly distributed on the manifold structure in the low-dimensional space. However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting in poor diversity, local optima, and inefficient searches. In this work, a generative adversarial network (GAN)-based manifold interpolation framework is proposed to learn the manifold and generate high-quality solutions on this manifold, thereby improving the performance of evolutionary algorithms. We compare the proposed algorithm with several state-of-the-art algorithms on large-scale multiobjective benchmark functions. Experimental results have demonstrated the significant improvements achieved by this framework in solving LSMOPs.
Phase Transitions in Frequency Agile Radar Using Compressed Sensing
Authors: Yuhan Li, Tianyao Huang, Xingyu Xu, Yimin Liu, Yonina C. Eldar
Date: 2020-12-30 00:00:00
Tasks: compressed sensing

FAR has improved anti-jamming performance over traditional pulse-Doppler radars under complex electromagnetic circumstances. To reconstruct the range-Doppler information in FAR, many compressed sensing (CS) methods including standard and block sparse recovery have been applied. In this paper, we study phase transitions of range-Doppler recovery in FAR using CS. In particular, we derive closed-form phase transition curves associated with block sparse recovery and complex Gaussian matrices, based on prior results of standard sparse recovery under real Gaussian matrices. We further approximate the obtained curves with elementary functions of radar and target parameters, facilitating practical applications of these curves. Our results indicate that block sparse recovery outperforms the standard counterpart when targets occupy more than one range cell, which are often referred to as extended targets. Simulations validate the availability of these curves and their approximations in FAR, which benefit the design of the radar parameters.
Impact of High PV Penetration on the Inter-area Oscillations in the U.S. Eastern Interconnection
Authors: Shutang You
Date: 2021-01-08 00:00:00

This study explores the impact of high PV penetration on the inter-area oscillation modes of large-scale power grids. A series of dynamic models with various PV penetration levels are developed based on a detailed model representing the U.S. Eastern Interconnection (EI). Transient simulations are performed to investigate the change of inter-area oscillation modes with PV penetration. The impact of PV control strategies and parameter settings on inter-area oscillations is studied. This study finds that as PV increases, the damping of the dominant oscillation mode decreases monotonically. It is also observed that the mode shape varies with the PV control strategy and new oscillation modes may emerge under inappropriate parameter settings in PV plant controls.
Evolution of default genetic control mechanisms
Authors: William Bains, Enrico Borriello, Dirk Schulze-Makuch
Date: 2021-01-08 00:00:00

We present a model of the evolution of control systems in a genome under environmental constraints. The model conceptually follows the Jacob and Monod model of gene control. Genes contain control elements which respond to the internal state of the cell as well as the environment to control expression of a coding region. Control and coding regions evolve to maximize a fitness function between expressed coding sequences and the environment. 118 runs of the model run to an average of 1.4 x 10^6 `generations' each with a range of starting parameters probed the conditions under which genomes evolved a `default style' of control. Unexpectedly, the control logic that evolved was not significantly correlated to the complexity of the environment. Genetic logic was strongly correlated with genome complexity and with the fraction of genes active in the cell at any one time. More complex genomes correlated with the evolution of genetic controls in which genes were active (`default on'), and a low fraction of genes being expressed correlated with a genetic logic in which genes were biased to being inactive unless positively activated (`default off' logic). We discuss how this might relate to the evolution of the complex eukaryotic genome, which operates in a `default off' mode.
Firearms Law and Fatal Police Shootings: A Panel Data Analysis
Authors: Marco Rogna, Diep Bich Nguyen
Date: 2021-01-08 00:00:00

Among industrialized countries, U.S. holds two somehow inglorious records: the highest rate of fatal police shootings and the highest rate of deaths related to firearms. The latter has been associated with strong diffusion of firearms ownership largely due to loose legislation in several member states. The present paper investigates the relation between firearms legislation\diffusion and the number of fatal police shooting episodes using a seven-year panel dataset. While our results confirm the negative impact of stricter firearms regulations found in previous cross-sectional studies, we find that the diffusion of guns ownership has no statistically significant effect. Furthermore, regulations pertaining to the sphere of gun owner accountability seem to be the most effective in reducing fatal police shootings.
Bee Cluster 3D: A system to monitor the temperature in a hive over time
Authors: Olivier Besson, Hervé Mugnier, Aurélien Neveux, Gaëlle Rey, Sully Vitry
Date: 2021-01-08 00:00:00

A new system, Bee Cluster 3D, allowing the study of the time evolution of the 3D temperature distribution in a bee hive is presented. This system can be used to evaluate the cluster size and the location of the queen during winter. In summer, the device can be used to quantify the size of the brood nest and the breeding activity of the queen. The system does not disturb the activity of the colony and can be used on any hive. This electronic system was developed to be non-intrusive, miniaturized, and energy autonomous.
Does external medical review reduce disability insurance inflow?
Authors: Helge Liebert
Date: 2021-01-08 00:00:00

This paper investigates the effects of introducing external medical review for disability insurance (DI) in a system relying on treating physician testimony for eligibility determination. Using a unique policy change and administrative data from Switzerland, I show that medical review reduces DI incidence by 23%. Incidence reductions are closely tied to difficult-to-diagnose conditions, suggesting inaccurate assessments by treating physicians. Due to a partial benefit system, reductions in full benefit awards are partly offset by increases in partial benefits. More intense screening also increases labor market participation. Existing benefit recipients are downgraded and lose part of their benefit income when scheduled medical reviews occur. Back-of-the-envelope calculations indicate that external medical review is highly cost-effective. Under additional assumptions, the results provide a lower bound of the effect on the false positive award error rate.
Market Making with Stochastic Liquidity Demand: Simultaneous Order Arrival and Price Change Forecasts
Authors: Agostino Capponi, José E. Figueroa-López, Chuyi Yu
Date: 2021-01-08 00:00:00
Tasks: Management

We provide an explicit characterization of the optimal market making strategy in a discrete-time Limit Order Book (LOB). In our model, the number of filled orders during each period depends linearly on the distance between the fundamental price and the market maker's limit order quotes, with random slope and intercept coefficients. The high-frequency market maker (HFM) incurs an end-of-the-day liquidation cost resulting from linear price impact. The optimal placement strategy incorporates in a novel and parsimonious way forecasts about future changes in the asset's fundamental price. We show that the randomness in the demand slope reduces the inventory management motive, and that a positive correlation between demand slope and investors' reservation prices leads to wider spreads. Our analysis reveals that the simultaneous arrival of buy and sell market orders (i) reduces the shadow cost of inventory, (ii) leads the HFM to reduce price pressures to execute larger flows, and (iii) introduces patterns of nonlinearity in the intraday dynamics of bid and ask spreads. Our empirical study shows that the market making strategy outperforms those which ignores randomness in demand, simultaneous arrival of buy and sell market orders, and local drift in the fundamental price.
A predictive model for the identification of learning styles in MOOC environments
Authors: Brahim Hmedna, Ali El Mezouary, Omar Baz
Date: 2019-10-12 00:00:00
Tasks: Anomaly Detection, Automatic Machine Learning Model Selection, Clustering Algorithms Evaluation, Event data classification

Massive online open course (MOOC) platform generates a large amount of data, which provides many opportunities for studying the behaviors of learners. In parallel, recent advancements in machine learning techniques and big data analysis have created new opportunities for a better understanding of how learners behave and learn in environments known for their massiveness and openness. The work is about predicting learners’ learning styles based on their learning traces. The Felder Silverman learning style model (FSLSM) is adopted since it is one of the most commonly used models in technology-enhanced learning. In order to attend our objective, we analyzed data collected from the edX course “statistical learning” (session Winter 2015 and Winter 2016), administered via Stanford’s Logunita platform. The results show that decision tree performs best for all 3 dimensions, with an accuracy of higher than 98% and a reduced risk of overfitting the training data.
Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing
Authors: Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, Thien Huu Nguyen
Date: 2021-01-09 00:00:00
Proceeding: EACL 2021 2
Tasks: Dependency Parsing, Language Modeling, Language Modelling, Lemmatization, Morphological Tagging, Multilingual NLP, Named Entity Recognition (NER), Part-Of-Speech Tagging, Sentence, Sentence segmentation, Sequential sentence segmentation

We introduce Trankit, a light-weight Transformer-based Toolkit for multilingual Natural Language Processing (NLP). It provides a trainable pipeline for fundamental NLP tasks over 100 languages, and 90 pretrained pipelines for 56 languages. Built on a state-of-the-art pretrained language model, Trankit significantly outperforms prior multilingual NLP pipelines over sentence segmentation, part-of-speech tagging, morphological feature tagging, and dependency parsing while maintaining competitive performance for tokenization, multi-word token expansion, and lemmatization over 90 Universal Dependencies treebanks. Despite the use of a large pretrained transformer, our toolkit is still efficient in memory usage and speed. This is achieved by our novel plug-and-play mechanism with Adapters where a multilingual pretrained transformer is shared across pipelines for different languages. Our toolkit along with pretrained models and code are publicly available at: https://github.com/nlp-uoregon/trankit. A demo website for our toolkit is also available at: http://nlp.uoregon.edu/trankit. Finally, we create a demo video for Trankit at: https://youtu.be/q0KGP3zGjGc.
Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies
Authors: Sophia Gu
Date: 2021-01-09 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Over the past decades, researchers have been pushing the limits of Deep Reinforcement Learning (DRL). Although DRL has attracted substantial interest from practitioners, many are blocked by having to search through a plethora of available methodologies that are seemingly alike, while others are still building RL agents from scratch based on classical theories. To address the aforementioned gaps in adopting the latest DRL methods, I am particularly interested in testing out if any of the recent technology developed by the leads in the field can be readily applied to a class of optimal trading problems. Unsurprisingly, many prominent breakthroughs in DRL are investigated and tested on strategic games: from AlphaGo to AlphaStar and at about the same time, OpenAI Five. Thus, in this writing, I want to show precisely how to use a DRL library that is initially built for games in a fundamental trading problem; mean reversion. And by introducing a framework that incorporates economically-motivated function properties, I also demonstrate, through the library, a highly-performant and convergent DRL solution to decision-making financial problems in general.