pwc-archive/papers-with-abstracts huggingface dataset:
Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments
Authors: Daochen Zha, Wenye Ma, Lei Yuan, Xia Hu, Ji Liu
Date: 2021-01-20 00:00:00
Proceeding: ICLR 2021 1
Tasks: MuJoCo

Exploration under sparse reward is a long-standing challenge of model-free reinforcement learning. The state-of-the-art methods address this challenge by introducing intrinsic rewards to encourage exploration in novel states or uncertain environment dynamics. Unfortunately, methods based on intrinsic rewards often fall short in procedurally-generated environments, where a different environment is generated in each episode so that the agent is not likely to visit the same state more than once. Motivated by how humans distinguish good exploration behaviors by looking into the entire episode, we introduce RAPID, a simple yet effective episode-level exploration method for procedurally-generated environments. RAPID regards each episode as a whole and gives an episodic exploration score from both per-episode and long-term views. Those highly scored episodes are treated as good exploration behaviors and are stored in a small ranking buffer. The agent then imitates the episodes in the buffer to reproduce the past good exploration behaviors. We demonstrate our method on several procedurally-generated MiniGrid environments, a first-person-view 3D Maze navigation task from MiniWorld, and several sparse MuJoCo tasks. The results show that RAPID significantly outperforms the state-of-the-art intrinsic reward strategies in terms of sample efficiency and final performance. The code is available at https://github.com/daochenzha/rapid
Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches
Authors: Vinayak Sharma, Jorge Angel Gonzalez Ordiano, Ralf Mikut, Umit Cali
Date: 2021-01-20 00:00:00
Tasks: Decision Making

The high penetration of volatile renewable energy sources such as solar make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic forecasts are an example of these methods, as they assist energy planners in their decision-making process by providing them with information about the uncertainty of future power generation. Currently, there is a trend towards the use of deep learning probabilistic forecasting methods. However, the point at which the more complex deep learning methods should be preferred over more simple approaches is not yet clear. Therefore, the current article presents a simple comparison between a long short-term memory neural network and other more simple approaches. The comparison consists of training and comparing models able to provide one-day-ahead probabilistic forecasts for a solar power system. Moreover, the current paper makes use of an open-source dataset provided during the Global Energy Forecasting Competition of 2014 (GEFCom14).
Automatic Differentiation via Effects and Handlers: An Implementation in Frank
Authors: Jesse Sigal
Date: 2021-01-20 00:00:00

Automatic differentiation (AD) is an important family of algorithms which enables derivative based optimization. We show that AD can be simply implemented with effects and handlers by doing so in the Frank language. By considering how our implementation behaves in Frank's operational semantics, we show how our code performs the dynamic creation of programs during evaluation.
Improving type information inferred by decompilers with supervised machine learning
Authors: Javier Escalada, Ted Scully, Francisco Ortin
Date: 2021-01-19 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Type Prediction

In software reverse engineering, decompilation is the process of recovering source code from binary files. Decompilers are used when it is necessary to understand or analyze software for which the source code is not available. Although existing decompilers commonly obtain source code with the same behavior as the binaries, that source code is usually hard to interpret and certainly differs from the original code written by the programmer. Massive codebases could be used to build supervised machine learning models aimed at improving existing decompilers. In this article, we build different classification models capable of inferring the high-level type returned by functions, with significantly higher accuracy than existing decompilers. We automatically instrument C source code to allow the association of binary patterns with their corresponding high-level constructs. A dataset is created with a collection of real open-source applications plus a huge number of synthetic programs. Our system is able to predict function return types with a 79.1% F1-measure, whereas the best decompiler obtains a 30% F1-measure. Moreover, we document the binary patterns used by our classifier to allow their addition in the implementation of existing decompilers.
Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer
Authors: Yuri Koide, Arjun J. Kaithakkal, Matthias Schniewind, Bradley P. Ladewig, Alexander Stroh, Pascal Friederich
Date: 2021-01-19 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation

Numerical simulation of fluids plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems. The calculation of heat transfer in fluid flow in simple flat channels is a relatively easy task for various simulation methods. However, once the channel geometry becomes more complex, numerical simulations become a bottleneck in optimizing wall geometries. We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number. We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations. We use the CNN models in a virtual high-throughput screening approach to explore a large number of possible, randomly generated wall architectures. Data Augmentation was applied to existing geometries data to add generated new training data which have the same number of parameters of heat transfer to improve the model's generalization. The general approach is not only applicable to simple flow setups as presented here but can be extended to more complex tasks, such as multiphase or even reactive unit operations in chemical engineering.
Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation
Authors: Yuki Fujimoto, Kenji Fukushima, Koichi Murase
Date: 2021-01-20 00:00:00
Tasks: Data Augmentation, Deep Learning

We discuss deep learning inference for the neutron star equation of state (EoS) using the real observational data of the mass and the radius. We make a quantitative comparison between the conventional polynomial regression and the neural network approach for the EoS parametrization. For our deep learning method to incorporate uncertainties in observation, we augment the training data with noise fluctuations corresponding to observational uncertainties. Deduced EoSs can accommodate a weak first-order phase transition, and we make a histogram for likely first-order regions. We also find that our observational data augmentation has a byproduct to tame the overfitting behavior. To check the performance improved by the data augmentation, we set up a toy model as the simplest inference problem to recover a double-peaked function and monitor the validation loss. We conclude that the data augmentation could be a useful technique to evade the overfitting without tuning the neural network architecture such as inserting the dropout.
Neural-based Modeling for Performance Tuning of Spark Data Analytics
Authors: Khaled Zaouk, Fei Song, Chenghao Lyu, Yanlei Diao
Date: 2021-01-20 00:00:00
Tasks: Diversity

Cloud data analytics has become an integral part of enterprise business operations for data-driven insight discovery. Performance modeling of cloud data analytics is crucial for performance tuning and other critical operations in the cloud. Traditional modeling techniques fail to adapt to the high degree of diversity in workloads and system behaviors in this domain. In this paper, we bring recent Deep Learning techniques to bear on the process of automated performance modeling of cloud data analytics, with a focus on Spark data analytics as representative workloads. At the core of our work is the notion of learning workload embeddings (with a set of desired properties) to represent fundamental computational characteristics of different jobs, which enable performance prediction when used together with job configurations that control resource allocation and other system knobs. Our work provides an in-depth study of different modeling choices that suit our requirements. Results of extensive experiments reveal the strengths and limitations of different modeling methods, as well as superior performance of our best performing method over a state-of-the-art modeling tool for cloud analytics.
Introduction to Normalizing Flows for Lattice Field Theory
Authors: Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
Date: 2021-01-20 00:00:00
Tasks: BIG-bench Machine Learning

This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows. The ideas and approaches proposed in arXiv:1904.12072, arXiv:2002.02428, and arXiv:2003.06413 are reviewed and a concrete implementation of the framework is presented. We apply this framework to a lattice scalar field theory and to U(1) gauge theory, explicitly encoding gauge symmetries in the flow-based approach to the latter. This presentation is intended to be interactive and working with the attached Jupyter notebook is recommended.
On Provable Backdoor Defense in Collaborative Learning
Authors: Ximing Qiao, Yuhua Bai, Siping Hu, Ang Li, Yiran Chen, Hai Li
Date: 2021-01-19 00:00:00
Tasks: backdoor defense

As collaborative learning allows joint training of a model using multiple sources of data, the security problem has been a central concern. Malicious users can upload poisoned data to prevent the model's convergence or inject hidden backdoors. The so-called backdoor attacks are especially difficult to detect since the model behaves normally on standard test data but gives wrong outputs when triggered by certain backdoor keys. Although Byzantine-tolerant training algorithms provide convergence guarantee, provable defense against backdoor attacks remains largely unsolved. Methods based on randomized smoothing can only correct a small number of corrupted pixels or labels; methods based on subset aggregation cause a severe drop in classification accuracy due to low data utilization. We propose a novel framework that generalizes existing subset aggregation methods. The framework shows that the subset selection process, a deciding factor for subset aggregation methods, can be viewed as a code design problem. We derive the theoretical bound of data utilization ratio and provide optimal code construction. Experiments on non-IID versions of MNIST and CIFAR-10 show that our method with optimal codes significantly outperforms baselines using non-overlapping partition and random selection. Additionally, integration with existing coding theory results shows that special codes can track the location of the attackers. Such capability provides new countermeasures to backdoor attacks.
Synthesizing Context-free Grammars from Recurrent Neural Networks (Extended Version)
Authors: Daniel M. Yellin, Gail Weiss
Date: 2021-01-20 00:00:00

We present an algorithm for extracting a subclass of the context free grammars (CFGs) from a trained recurrent neural network (RNN). We develop a new framework, pattern rule sets (PRSs), which describe sequences of deterministic finite automata (DFAs) that approximate a non-regular language. We present an algorithm for recovering the PRS behind a sequence of such automata, and apply it to the sequences of automata extracted from trained RNNs using the L* algorithm. We then show how the PRS may converted into a CFG, enabling a familiar and useful presentation of the learned language. Extracting the learned language of an RNN is important to facilitate understanding of the RNN and to verify its correctness. Furthermore, the extracted CFG can augment the RNN in classifying correct sentences, as the RNN's predictive accuracy decreases when the recursion depth and distance between matching delimiters of its input sequences increases.
secureTF: A Secure TensorFlow Framework
Authors: Do Le Quoc, Franz Gregor, Sergei Arnautov, Roland Kunkel, Pramod Bhatotia, Christof Fetzer
Date: 2021-01-20 00:00:00
Tasks: BIG-bench Machine Learning, Cloud Computing

Data-driven intelligent applications in modern online services have become ubiquitous. These applications are usually hosted in the untrusted cloud computing infrastructure. This poses significant security risks since these applications rely on applying machine learning algorithms on large datasets which may contain private and sensitive information. To tackle this challenge, we designed secureTF, a distributed secure machine learning framework based on Tensorflow for the untrusted cloud infrastructure. secureTF is a generic platform to support unmodified TensorFlow applications, while providing end-to-end security for the input data, ML model, and application code. secureTF is built from ground-up based on the security properties provided by Trusted Execution Environments (TEEs). However, it extends the trust of a volatile memory region (or secure enclave) provided by the single node TEE to secure a distributed infrastructure required for supporting unmodified stateful machine learning applications running in the cloud. The paper reports on our experiences about the system design choices and the system deployment in production use-cases. We conclude with the lessons learned based on the limitations of our commercially available platform, and discuss open research problems for the future work.
Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning
Authors: Fayçal Ait Aoudia, Jakob Hoydis
Date: 2021-01-20 00:00:00

Orthogonal frequency division multiplexing (OFDM) is one of the dominant waveforms in wireless communication systems due to its efficient implementation. However, it suffers from a loss of spectral efficiency as it requires a cyclic prefix (CP) to mitigate inter-symbol interference (ISI) and pilots to estimate the channel. We propose in this work to address these drawbacks by learning a neural network (NN)-based receiver jointly with a constellation geometry and bit labeling at the transmitter, that allows CP-less and pilotless communication on top of OFDM without a significant loss in bit error rate (BER). Our approach enables at least 18% throughput gains compared to a pilot and CP-based baseline, and at least 4% gains compared to a system that uses a neural receiver with pilots but no CP.
RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery
Authors: Jingtao Li, Adnan Siraj Rakin, Zhezhi He, Deliang Fan, Chaitali Chakrabarti
Date: 2021-01-20 00:00:00

Adversarial attacks on Neural Network weights, such as the progressive bit-flip attack (PBFA), can cause a catastrophic degradation in accuracy by flipping a very small number of bits. Furthermore, PBFA can be conducted at run time on the weights stored in DRAM main memory. In this work, we propose RADAR, a Run-time adversarial weight Attack Detection and Accuracy Recovery scheme to protect DNN weights against PBFA. We organize weights that are interspersed in a layer into groups and employ a checksum-based algorithm on weights to derive a 2-bit signature for each group. At run time, the 2-bit signature is computed and compared with the securely stored golden signature to detect the bit-flip attacks in a group. After successful detection, we zero out all the weights in a group to mitigate the accuracy drop caused by malicious bit-flips. The proposed scheme is embedded in the inference computation stage. For the ResNet-18 ImageNet model, our method can detect 9.6 bit-flips out of 10 on average. For this model, the proposed accuracy recovery scheme can restore the accuracy from below 1% caused by 10 bit flips to above 69%. The proposed method has extremely low time and storage overhead. System-level simulation on gem5 shows that RADAR only adds <1% to the inference time, making this scheme highly suitable for run-time attack detection and mitigation.
预警！因忽视了这个细节，数十篇钙钛矿文章的数据恐存在问题
Authors: 搜狐网
Date: 2020-12-23 00:00:00

预警！因忽视了这个细节，数十篇钙钛矿文章的数据恐存在问题

有机无机杂化钙钛矿材料因其优异的光电性能近年来成为研究的热点，但是这类钙钛矿材料存在一个问题就是对电子束辐射特别敏感，在约100个电子每平方埃的电子剂量照射下，有机无机钙钛矿材料就会开始分解为卤化铅，例如甲胺铅碘（MAPbI3）钙钛矿开始分解为碘化铅（PbI2）。而平时我们进行透射电镜(TEM)进行表征时，电子束的剂量功率却高达数1000个电子每平方埃每秒，在高分辨条件下更是可能高达近万个电子每平方埃每秒，这意味着如果不使用低剂量成像技术，普通的TEM条件几乎不可能得到本征的钙钛矿结构，得到的将是分解后的卤化铅。
参考文献：Deng, Y.-H. Common Phase and Structure Misidentifications in High-Resolution TEM Characterization of Perovskite Materials. Condens. Matter 2021, 6, 1.

文章链接： https://www.mdpi.com/2410-3896/6/1/1

DOI：10.3390/condmat6010001
Remote photonic sensing of blood oxygen saturation via tracking of anomalies in micro-saccades patterns
Authors: Zeev Kalyuzhner, Sergey Agdarov, Aviya Bennett, Yafim Beiderman, and Zeev Zalevsky
Date: 2021-01-20 00:00:00
Tasks: BIG-bench Machine Learning

Speckle pattern analysis has been found by many researchers to be applicable to remote sensing of various biomedical parameters. This paper shows how analysis of dynamic differential speckle patterns scattered from subjects’ sclera illuminated by a laser beam allows extraction of micro-saccades movement in the human eye. Analysis of micro-saccades movement using advanced machine learning techniques based on convolutional neural networks offers a novel approach for non-contact assessment of human blood oxygen saturation level (SpO2). Early stages of hypoxia can rapidly progress into pneumonia and death, and lives can be saved by advance remote detection of reduced blood oxygen saturation.
Simplifying Object Segmentation with PixelLib Library
Authors: Ayoola Olafenwa
Date: 2021-01-20 00:00:00
Tasks: Image Classification, Instance Segmentation, Object, Object Detection, Scene Segmentation, Segmentation, Semantic Segmentation, Video Segmentation

PixelLib [1] is a library created to allow easy implementation of object segmentation in real life applications.  In this paper we discussed in detail how PixelLib makes it possible for developers to implement semantic segmentation, instance segmentation, and background editing in images and videos with great simplification.
Operation comfort vs. the importance of system components
Authors: Krzysztof J. Szajowski, Małgorzata Średnicka
Date: 2021-01-20 00:00:00

The purpose of this report is to look at the measures of importance of components in systems in terms of reliability. In the first work of Birnbaum (1968) on this subject, many interesting studies were created and important indicators were constructed that allowed to organize the components of complex systems. They are helpful in analyzing the reliability of the designed systems, establishing the principles of operation and maintenance. The significance measures presented here are collected and discussed regarding the motivation behind their creation. They concern an approach in which both elements and systems are binary, and the possibility of generalization to multistate systems is only mentioned. Among the discussed is one new proposal using the methods of game theory, combining the sensitivity to the structure of the system and the operational effects on the system performance. The presented severity measures use a knowledge of the system structure as well as reliability and wear and tear, and whether the components can be repaired and maintained.
Variational manifold learning from incomplete data: application to multislice dynamic MRI
Authors: Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal, Sarv Priya, Rolf Schulte, Mathews Jacob
Date: 2021-01-20 00:00:00
Tasks: Imputation

Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. Once learned, the density can be used for a variety of tasks, including data imputation. However, fully sampled data is often unavailable in a variety of problems, including the recovery of dynamic and high-resolution MRI data considered in this work. To overcome this problem, we introduce a novel variational approach to learn a manifold from undersampled data. The VAE uses a decoder fed by latent vectors, drawn from a conditional density estimated from the fully sampled images using an encoder. Since fully sampled images are not available in our setting, we approximate the conditional density of the latent vectors by a parametric model whose parameters are estimated from the undersampled measurements using back-propagation. We use the framework for the joint alignment and recovery of multislice free breathing and ungated cardiac MRI data from highly undersampled measurements. Most of the current self-gating and manifold cardiac MRI approaches consider the independent recovery of images from each slice; these methods are not capable of exploiting the inter-slice redundancies in the datasets and require sophisticated post-processing or manual approaches to align the images from different slices. By contrast, the proposed scheme is able to align the multislice data and exploit the redundancies. Experimental results demonstrate the utility of the proposed scheme in dynamic imaging alignment and reconstructions.
Non-genetic inheritance restraint of cell-to-cell variation
Authors: Harsh Vashistha, Maryam Kohram, Hanna Salman
Date: 2020-04-13 00:00:00

Heterogeneity in physical and functional characteristics of cells (e.g. size, cycle time, growth rate, protein concentration) proliferates within an isogenic population due to stochasticity in intracellular biochemical processes and in the distribution of resources during divisions. Conversely, it is limited in part by the inheritance of cellular components between consecutive generations. Here we introduce a new experimental method for measuring proliferation of heterogeneity in bacterial cell characteristics, based on measuring how two sister cells become different from each other over time. Our measurements provide the inheritance dynamics of different cellular properties, and the "inertia" of cells to maintain these properties along time. We find that inheritance dynamics are property-specific, and can exhibit long-term memory (~10 generations) that works to restrain variation among cells. Our results can reveal mechanisms of non-genetic inheritance in bacteria and help understand how cells control their properties and heterogeneity within isogenic cell populations.