OpenRaiser/Intern-Atlas huggingface dataset:
Single-Shot Pruning for Offline Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Deep Reinforcement Learning (RL) is a powerful framework for solving complex real-world problems. Large neural networks employed in the framework are traditionally associated with better generalization capabilities, but their increased size entails the drawbacks of extensive training duration, substantial hardware resources, and longer inference times. One way to tackle this problem is to prune neural networks leaving only the necessary parameters. State-of-the-art concurrent pruning techniques for imposing sparsity perform demonstrably well in applications where data distributions are fixed. However, they have not yet been substantially explored in the context of RL. We close the gap between RL and single-shot pruning techniques and present a general pruning approach to the Offline RL. We leverage a fixed dataset to prune neural networks before the start of RL training. We then run experiments varying the network sparsity level and evaluating the validity of pruning at initialization techniques in continuous control tasks. Our results show that with 95% of the network weights pruned, Offline-RL algorithms can still retain performance in the majority of our experiments. To the best of our knowledge, no prior work utilizing pruning in RL retained performance at such high levels of sparsity. Moreover, pruning at initialization techniques can be easily integrated into any existing Offline-RL algorithms without changing the learning objective.
Privacy-Protecting COVID-19 Exposure Notification Based on Cluster Events
Venue: arXiv cs.CR 2021
Year: 2021

We provide a rough sketch of a simple system design for exposure notification of COVID-19 infections based on copresence at cluster events -- locations and times where a threshold number of tested-positive (TP) individuals were present. Unlike other designs, such as DP3T or the Apple-Google exposure-notification system, this design does not track or notify based on detecting direct proximity to TP individuals. The design makes use of existing or in-development tests for COVID-19 that are relatively cheap and return results in less than an hour, and that have high specificity but may have lower sensitivity. It also uses readily available location tracking for mobile phones and similar devices. It reports events at which TP individuals were present but does not link events with individuals or with other events in an individual's history. Participating individuals are notified of detected cluster events. They can then compare these locally to their own location history. Detected cluster events can be publicized through public channels. Thus, individuals not participating in the reporting system can still be notified of exposure. A proper security analysis is beyond the scope of this design sketch. We do, however, discuss resistance to various adversaries and attacks on privacy as well as false-reporting attacks.
Multi-Dimensional Model Compression of Vision Transformer
Venue: arXiv cs.CV 2021
Year: 2021

Vision transformers (ViT) have recently attracted considerable attentions, but the huge computational cost remains an issue for practical deployment. Previous ViT pruning methods tend to prune the model along one dimension solely, which may suffer from excessive reduction and lead to sub-optimal model quality. In contrast, we advocate a multi-dimensional ViT compression paradigm, and propose to harness the redundancy reduction from attention head, neuron and sequence dimensions jointly. We firstly propose a statistical dependence based pruning criterion that is generalizable to different dimensions for identifying deleterious components. Moreover, we cast the multi-dimensional compression as an optimization, learning the optimal pruning policy across the three dimensions that maximizes the compressed model's accuracy under a computational budget. The problem is solved by our adapted Gaussian process search with expected improvement. Experimental results show that our method effectively reduces the computational cost of various ViT models. For example, our method reduces 40\% FLOPs without top-1 accuracy loss for DeiT and T2T-ViT models, outperforming previous state-of-the-arts.
Evaluating Deep Music Generation Methods Using Data Augmentation
Venue: arXiv cs.SD 2021
Year: 2021

Despite advances in deep algorithmic music generation, evaluation of generated samples often relies on human evaluation, which is subjective and costly. We focus on designing a homogeneous, objective framework for evaluating samples of algorithmically generated music. Any engineered measures to evaluate generated music typically attempt to define the samples' musicality, but do not capture qualities of music such as theme or mood. We do not seek to assess the musical merit of generated music, but instead explore whether generated samples contain meaningful information pertaining to emotion or mood/theme. We achieve this by measuring the change in predictive performance of a music mood/theme classifier after augmenting its training data with generated samples. We analyse music samples generated by three models -- SampleRNN, Jukebox, and DDSP -- and employ a homogeneous framework across all methods to allow for objective comparison. This is the first attempt at augmenting a music genre classification dataset with conditionally generated music. We investigate the classification performance improvement using deep music generation and the ability of the generators to make emotional music by using an additional, emotion annotation of the dataset. Finally, we use a classifier trained on real data to evaluate the label validity of class-conditionally generated samples.
iCaps: Iterative Category-level Object Pose and Shape Estimation
Venue: arXiv cs.CV 2021
Year: 2021

This paper proposes a category-level 6D object pose and shape estimation approach iCaps, which allows tracking 6D poses of unseen objects in a category and estimating their 3D shapes. We develop a category-level auto-encoder network using depth images as input, where feature embeddings from the auto-encoder encode poses of objects in a category. The auto-encoder can be used in a particle filter framework to estimate and track 6D poses of objects in a category. By exploiting an implicit shape representation based on signed distance functions, we build a LatentNet to estimate a latent representation of the 3D shape given the estimated pose of an object. Then the estimated pose and shape can be used to update each other in an iterative way. Our category-level 6D object pose and shape estimation pipeline only requires 2D detection and segmentation for initialization. We evaluate our approach on a publicly available dataset and demonstrate its effectiveness. In particular, our method achieves comparably high accuracy on shape estimation.
Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization
Venue: arXiv cs.DL 2021
Year: 2021

We utilize a recently developed topic modeling method called SeNMFk, extending the standard Non-negative Matrix Factorization (NMF) methods by incorporating the semantic structure of the text, and adding a robust system for determining the number of topics. With SeNMFk, we were able to extract coherent topics validated by human experts. From these topics, a few are relatively general and cover broad concepts, while the majority can be precisely mapped to specific scientific effects or measurement techniques. The topics also differ by ubiquity, with only three topics prevalent in almost 40 percent of the abstract, while each specific topic tends to dominate a small subset of the abstracts. These results demonstrate the ability of SeNMFk to produce a layered and nuanced analysis of large scientific corpora.
FIFA ranking: Evaluation and path forward
Venue: arXiv cs.IR 2021
Year: 2021

In this work we study the ranking algorithm used by Fédération Internationale de Football Association (FIFA); we analyze the parameters it currently uses, show the formal probabilistic model from which it can be derived, and optimize the latter. In particular, analyzing the games since the introduction of the algorithm in 2018, we conclude that the game's "importance" (as defined by FIFA) used in the algorithm is counterproductive from the point of view of the predictive capability of the algorithm. We also postulate the algorithm to be rooted in the formal modelling principle, where the Davidson model proposed in 1970 seems to be an excellent candidate, preserving the form of the algorithm currently used. The results indicate that the predictive capability of the algorithm is notably improved by using the home-field advantage and the explicit model for the draws in the game. Moderate, but notable improvement may be attained by introducing the weighting of the results with the goal differential, which although not rooted in a formal modelling principle, is compatible with the current algorithm and can be tuned to the characteristics of the football competition.
00
Venue: arXiv cs.CR 2021
Year: 2021

What is the funniest number in cryptography (Episode 2)? 0 [1]. The reason is that $\forall x, x \cdot 0 = 0$, i.e., the equation is satisfied no matter what $x$ is. We'll use zero to attack zero-knowledge proof (ZKP). In particular, we'll discuss a critical issue in a cutting-edge ZKP PLONK [2] C++ implementation which allows an attacker to create a forged proof that all verifiers will accept. We'll show how theory guides the attack's direction. In practice, the attack works like a charm and we'll show how the attack falls through a chain of perfectly aligned software cracks. In the same codebase, there is an independent critical ECDSA bug where (r, s) = (0, 0) is a valid signature for arbitrary keys and messages, but we won't discuss it further because it's a known ECDSA attack vector in the Google Wycheproof cryptanalysis project [3] that I worked on a few years ago. All bugs have been responsibly disclosed through the vendor's bug bounty program with total reward $\sim \$15,000$ (thank you).
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data
Venue: arXiv cs.LG 2021
Year: 2021

Corrupted labels and class imbalance are commonly encountered in practically collected training data, which easily leads to over-fitting of deep neural networks (DNNs). Existing approaches alleviate these issues by adopting a sample re-weighting strategy, which is to re-weight sample by designing weighting function. However, it is only applicable for training data containing only either one type of data biases. In practice, however, biased samples with corrupted labels and of tailed classes commonly co-exist in training data. How to handle them simultaneously is a key but under-explored problem. In this paper, we find that these two types of biased samples, though have similar transient loss, have distinguishable trend and characteristics in loss curves, which could provide valuable priors for sample weight assignment. Motivated by this, we delve into the loss curves and propose a novel probe-and-allocate training strategy: In the probing stage, we train the network on the whole biased training data without intervention, and record the loss curve of each sample as an additional attribute; In the allocating stage, we feed the resulting attribute to a newly designed curve-perception network, named CurveNet, to learn to identify the bias type of each sample and assign proper weights through meta-learning adaptively. The training speed of meta learning also blocks its application. To solve it, we propose a method named skip layer meta optimization (SLMO) to accelerate training speed by skipping the bottom layers. Extensive synthetic and real experiments well validate the proposed method, which achieves state-of-the-art performance on multiple challenging benchmarks.
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training
Venue: arXiv cs.LG 2021
Year: 2021

Understanding how the predictions of deep learning models are formed during the training process is crucial to improve model performance and fix model defects, especially when we need to investigate nontrivial training strategies such as active learning, and track the root cause of unexpected training results such as performance degeneration. In this work, we propose a time-travelling visual solution DeepVisualInsight (DVI), aiming to manifest the spatio-temporal causality while training a deep learning image classifier. The spatio-temporal causality demonstrates how the gradient-descent algorithm and various training data sampling techniques can influence and reshape the layout of learnt input representation and the classification boundaries in consecutive epochs. Such causality allows us to observe and analyze the whole learning process in the visible low dimensional space. Technically, we propose four spatial and temporal properties and design our visualization solution to satisfy them. These properties preserve the most important information when inverse-)projecting input samples between the visible low-dimensional and the invisible high-dimensional space, for causal analyses. Our extensive experiments show that, comparing to baseline approaches, we achieve the best visualization performance regarding the spatial/temporal properties and visualization efficiency. Moreover, our case study shows that our visual solution can well reflect the characteristics of various training scenarios, showing good potential of DVI as a debugging tool for analyzing deep learning training processes.
Neural Myerson Auction for Truthful and Energy-Efficient Autonomous Aerial Data Delivery
Venue: arXiv cs.GT 2021
Year: 2021

A successful deployment of drones provides an ideal solution for surveillance systems. Using drones for surveillance can provide access to areas that may be difficult or impossible to reach by humans or in-land vehicles gathering images or video recordings of a specific target in their coverage. Therefore, we introduces a data delivery drone to transfer collected surveillance data in harsh communication conditions. This paper proposes a Myerson auction-based asynchronous data delivery in an aerial distributed data platform in surveillance systems taking battery limitation and long flight constraints into account. In this paper, multiple delivery drones compete to offer data transfer to a single fixed-location surveillance drone. Our proposed Myerson auction-based algorithm, which uses the truthful second-price auction (SPA) as a baseline, is to maximize the seller's revenue while meeting several desirable properties, i.e., individual rationality and incentive compatibility while pursuing truthful operations. On top of these SPA-based operations, a deep learning-based framework is additionally designed for delivery performance improvements.
Olsson.wl : a Mathematica package for the computation of linear transformations of multivariable hypergeometric functions
Venue: arXiv cs.MS 2021
Year: 2021

We present the Olsson$.$wl Mathematica package which aims to find linear transformations for some classes of multivariable hypergeometric functions. It is based on a well-known method developed by P. O. M. Olsson in J. Math. Phys. 5, 420 (1964) in order to derive the analytic continuations of the Appell $F_1$ double hypergeometric series from the linear transformations of the Gauss $_2F_1$ hypergeometric function. We provide a brief description of Olsson's method and demonstrate the commands of the package, along with examples. We also provide a companion package, called ROC2$.$wl and dedicated to the derivation of the regions of convergence of double hypergeometric series. This package can be used independently of Olsson$.$wl.
Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands
Venue: arXiv cs.CV 2021
Year: 2021

In this paper we present our workflow to automatically reconstruct 3D building models based on 2D building polygons and a LiDAR point cloud. The workflow generates models at different levels of detail (LoDs) to support data requirements of different applications from one consistent source. Specific attention has been paid to make the workflow robust to quickly run a new iteration in case of improvements in an algorithm or in case new input data become available. The quality of the reconstructed data highly depends on the quality of the input data and is monitored in several steps of the process. A 3D viewer has been developed to view and download the openly available 3D data at different LoDs in different formats. The workflow has been applied to all 10 million buildings of The Netherlands. The 3D service will be updated after new input data becomes available.
Prognosis: Closed-Box Analysis of Network Protocol Implementations
Venue: arXiv cs.NI 2021
Year: 2021

We present Prognosis, a framework offering automated closed-box learning and analysis of models of network protocol implementations. Prognosis can learn models that vary in abstraction level from simple deterministic automata to models containing data operations, such as register updates, and can be used to unlock a variety of analysis techniques -- model checking temporal properties, computing differences between models of two implementations of the same protocol, or improving testing via model-based test generation. Prognosis is modular and easily adaptable to different protocols (e.g., TCP and QUIC) and their implementations. We use Prognosis to learn models of (parts of) three QUIC implementations -- Quiche (Cloudflare), Google QUIC, and Facebook mvfst -- and use these models to analyze the differences between the various implementations. Our analysis provides insights into different design choices and uncovers potential bugs. Concretely, we have found critical bugs in multiple QUIC implementations, which have been acknowledged by the developers.
Elephant-Human Conflict Mitigation: An Autonomous UAV Approach
Venue: arXiv cs.MA 2021
Year: 2021

Elephant-human conflict (EHC) is one of the major problems in most African and Asian countries. As humans overutilize natural resources for their development, elephants' living area continues to decrease; this leads elephants to invade the human living area and raid crops more frequently, costing millions of dollars annually. To mitigate EHC, in this paper, we propose an original solution that comprises of three parts: a compact custom low-power GPS tag that is installed on the elephants, a receiver stationed in the human living area that detects the elephants' presence near a farm, and an autonomous unmanned aerial vehicle (UAV) system that tracks and herds the elephants away from the farms. By utilizing proportional-integral-derivative controller and machine learning algorithms, we obtain accurate tracking trajectories at a real-time processing speed of 32 FPS. Our proposed autonomous system can save over 68 % cost compared with human-controlled UAVs in mitigating EHC.
Proxy System with JPEG Bitstream-Based File-Size Preserving Encryption for Cloud Photo Streams
Venue: arXiv cs.OH 2021
Year: 2021

In this paper, we propose a proxy system with JPEG bitstream-based file-size preserving encryption to securely store compressed images in cloud environments. The proposed system, which is settled between client's device and the Internet, allows us not only to have exact the same file size as that of original JPEG streams but also to maintain a predetermined image format. In an experiment, the proposed system is verified to be effective in two cloud photo steams: Google Photo and iCloud Photo.
On the Byzantine-Fault-Tolerant Consensus for Blockchain among Connected Vehicles
Venue: arXiv cs.DC 2021
Year: 2021

It is a critical matter for a blockchain system whether a Byzantine fault tolerance (BFT) can be guaranteed during a consensus process. Can connected vehicles (CVs) achieve the BFT consensus when the vehicles keep mobile? This paper seeks an answer to this fundamental question. It focuses on characterizing the impact of mobility on the performance of a BFT consensus among CVs.
COVID-19 and Online Learning Tools
Venue: arXiv cs.CY 2021
Year: 2021

Distance education has a long history. However, COVID-19 has created a new era of distance education. Due to the increasing demand, various distance learning solutions have been introduced for different distance education purposes. In this study, we investigated the impact of COVID-19 on global attention towards different distance learning-teaching tools. We used Google Trend search queries as a proxy to quantify the popularity and public interest towards different distance education solutions. Both visual and analytical approaches were used to analyze global-level web search queries during the COVID-19 pandemic. This can provide a fast first step guide to identifying the most popular online learning tools available for different educational purposes. The results allow the teachers to narrow down the search space and deepen their exploration of prominent distance education solutions to support their online teaching. The R code and data to reproduce the results of this work are available in the online supplementary materials.
Confronting the Carbon-footprint Challenge of Blockchain
Venue: arXiv cs.CY 2021
Year: 2021

The distributed consensus mechanism is the backbone of the rapidly developing blockchain network. Blockchain platforms consume vast amounts of electricity based on the current consensus mechanism of Proof of Work. Here, we point out an advanced consensus mechanism named Proof of Stake that can eliminate the extensive energy consumption of the current PoW-based blockchain. We comprehensively elucidate the current and projected energy consumption and carbon footprint of the PoW and PoS based Bitcoin and Ethereum blockchain platforms.
Efficient Training of Spiking Neural Networks with Temporally-Truncated Local Backpropagation through Time
Venue: arXiv cs.NE 2021
Year: 2021

Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known backpropagation through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking, making it impossible to exploit the potential of locally-supervised training methods. This work proposes an efficient and direct training algorithm for SNNs that integrates a locally-supervised training method with a temporally-truncated BPTT algorithm. The proposed algorithm explores both temporal and spatial locality in BPTT and contributes to significant reduction in computational cost including GPU memory utilization, main memory access and arithmetic operations. We thoroughly explore the design space concerning temporal truncation length and local training block size and benchmark their impact on classification accuracy of different networks running different types of tasks. The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on dynamic-vision-sensor (DVS) recorded datasets. In spite of resulting information loss, local training is capable of alleviating overfitting. The combined effect of temporal truncation and local training can lead to the slowdown of accuracy drop and even improvement in accuracy. In addition, training deep SNNs models such as AlexNet classifying CIFAR10-DVS dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory, 10.79% reduction in memory access, and 99.64% reduction in MAC operations compared to the standard end-to-end BPTT.