OpenRaiser/Intern-Atlas huggingface dataset:
Position-free Multiple-bounce Computations for Smith Microfacet BSDFs
Venue: arXiv cs.GR 2021
Year: 2021

Bidirectional Scattering Distribution Functions (BSDFs) encode how a material reflects or transmits the incoming light. The most commonly used model is the Microfacet BSDF. It computes material response from the micro-geometry of the surface assuming a single bounce on specular microfacets. The original model ignores multiple bounces on the micro-geometry, resulting in energy loss, especially with large roughness. In this paper, we present a position-free formulation of multiple bounces inside the micro-geometry, which eliminates this energy loss. We use an explicit mathematical definition of path space that describes single and multiple bounces in a uniform way. We then study the behavior of light on the different vertices and segments in path space, leading to an accurate and reciprocal multiple-bounce description of BSDFs. We also present practical, unbiased Monte-Carlo estimators to compute multiple scattering. Our method is less noisy than existing algorithms for computing multiple scattering. It is almost noise-free with a very-low sampling rate, from 2 to 4 samples per pixel.
preCICE v2: A Sustainable and User-Friendly Coupling Library
Venue: arXiv cs.MS 2021
Year: 2021

preCICE is a free/open-source coupling library. It enables creating partitioned multi-physics simulations by gluing together separate software packages. This paper summarizes the development efforts in preCICE of the past five years. During this time span, we have turned the software from a working prototype -- sophisticated numerical coupling methods and scalability on ten thousands of compute cores -- to a sustainable and user-friendly software project with a steadily-growing community. Today, we know through forum discussions, conferences, workshops, and publications of more than 100 research groups using preCICE. We cover the fundamentals of the software alongside a performance and accuracy analysis of different data mapping methods. Afterwards, we describe ready-to-use integration with widely-used external simulation software packages, tests and continuous integration from unit to system level, and community building measures, drawing an overview of the current preCICE ecosystem.
A verified algebraic representation of Cairo program execution
Venue: arXiv cs.CR 2021
Year: 2021

Cryptographic interactive proof systems provide an efficient and scalable means of verifying the results of computation on blockchain. A prover constructs a proof, off-chain, that the execution of a program on a given input terminates with a certain result. The prover then publishes a certificate that can be verified efficiently and reliably modulo commonly accepted cryptographic assumptions. The method relies on an algebraic encoding of execution traces of programs. Here we report on a verification of the correctness of such an encoding of the Cairo model of computation with respect to the STARK interactive proof system, using the Lean 3 proof assistant.
PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting
Venue: arXiv cs.LG 2021
Year: 2021

Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has great potential in seismic modeling and inversion. However, when dealing with high-frequency wavefields, its accuracy and training cost limits its applications. Thus, we propose a novel implementation of PINN using frequency upscaling and neuron splitting, which allows the neural network model to grow in size as we increase the frequency while leveraging the information from the pre-trained model for lower-frequency wavefields, resulting in fast convergence to high-accuracy solutions. Numerical results show that, compared to the commonly used PINN with random initialization, the proposed PINN exhibits notable superiority in terms of convergence and accuracy and can achieve neuron based high-frequency wavefield solutions with a two-hidden-layer model.
A Unifying Framework for Characterizing and Computing Width Measures
Venue: arXiv cs.DS 2021
Year: 2021

Algorithms for computing or approximating optimal decompositions for decompositional parameters such as treewidth or clique-width have so far traditionally been tailored to specific width parameters. Moreover, for mim-width, no efficient algorithms for computing good decompositions were known, even under highly restrictive parameterizations. In this work we identify F-branchwidth as a class of generic decompositional parameters that can capture mim-width, treewidth, clique-width as well as other measures. We show that while there is an infinite number of F-branchwidth parameters, only a handful of these are asymptotically distinct. We then develop fixed-parameter and kernelization algorithms (under several structural parameterizations) that can compute every possible F-branchwidth, providing a unifying framework that can efficiently obtain near-optimal tree-decompositions, k-expressions, as well as optimal mim-width decompositions.
Accelerating Encrypted Computing on Intel GPUs
Venue: arXiv cs.CR 2021
Year: 2021

Homomorphic Encryption (HE) is an emerging encryption scheme that allows computations to be performed directly on encrypted messages. This property provides promising applications such as privacy-preserving deep learning and cloud computing. Prior works have been proposed to enable practical privacy-preserving applications with architectural-aware optimizations on CPUs, GPUs and FPGAs. However, there is no systematic optimization for the whole HE pipeline on Intel GPUs. In this paper, we present the first-ever SYCL-based GPU backend for Microsoft SEAL APIs. We perform optimizations from instruction level, algorithmic level and application level to accelerate our HE library based on the Cheon, Kim, Kimand Song (CKKS) scheme on Intel GPUs. The performance is validated on two latest Intel GPUs. Experimental results show that our staged optimizations together with optimizations including low-level optimizations and kernel fusion accelerate the Number Theoretic Transform (NTT), a key algorithm for HE, by up to 9.93X compared with the naïve GPU baseline. The roofline analysis confirms that our optimized NTT reaches 79.8% and85.7% of the peak performance on two GPU devices. Through the highly optimized NTT and the assembly-level optimization, we obtain 2.32X - 3.05X acceleration for HE evaluation routines. In addition, our all-together systematic optimizations improve the performance of encrypted element-wise polynomial matrix multiplication application by up to 3.10X.
Collaborative Storytelling with Human Actors and AI Narrators
Venue: arXiv cs.HC 2021
Year: 2021

Large language models can be used for collaborative storytelling. In this work we report on using GPT-3 \cite{brown2020language} to co-narrate stories. The AI system must track plot progression and character arcs while the human actors perform scenes. This event report details how a novel conversational agent was employed as creative partner with a team of professional improvisers to explore long-form spontaneous story narration in front of a live public audience. We introduced novel constraints on our language model to produce longer narrative text and tested the model in rehearsals with a team of professional improvisers. We then field tested the model with two live performances for public audiences as part of a live theatre festival in Europe. We surveyed audience members after each performance as well as performers to evaluate how well the AI performed in its role as narrator. Audiences and performers responded positively to AI narration and indicated preference for AI narration over AI characters within a scene. Performers also responded positively to AI narration and expressed enthusiasm for the creative and meaningful novel narrative directions introduced to the scenes. Our findings support improvisational theatre as a useful test-bed to explore how different language models can collaborate with humans in a variety of social contexts.
Decentralized Role Assignment in Multi-Agent Teams via Empirical Game-Theoretic Analysis
Venue: arXiv cs.MA 2021
Year: 2021

We propose a method, based on empirical game theory, for a robot operating as part of a team to choose its role within the team without explicitly communicating with team members, by leveraging its knowledge about the team structure. To do this, we formulate the role assignment problem as a dynamic game, and borrow tools from empirical game-theoretic analysis to analyze such games. Based on this game-theoretic formulation, we propose a distributed controller for each robot to dynamically decide on the best role to take. We demonstrate our method in simulations of a collaborative planar manipulation scenario in which each agent chooses from a set of feedback control policies at each instant. The agents can effectively collaborate without communication to manipulate the object while also avoiding collisions using our method.
GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

To achieve an accurate assessment of root canal therapy, a fundamental step is to perform tooth root segmentation on oral X-ray images, in that the position of tooth root boundary is significant anatomy information in root canal therapy evaluation. However, the fuzzy boundary makes the tooth root segmentation very challenging. In this paper, we propose a novel end-to-end U-Net like Group Transformer Network (GT U-Net) for the tooth root segmentation. The proposed network retains the essential structure of U-Net but each of the encoders and decoders is replaced by a group Transformer, which significantly reduces the computational cost of traditional Transformer architectures by using the grouping structure and the bottleneck structure. In addition, the proposed GT U-Net is composed of a hybrid structure of convolution and Transformer, which makes it independent of pre-training weights. For optimization, we also propose a shape-sensitive Fourier Descriptor (FD) loss function to make use of shape prior knowledge. Experimental results show that our proposed network achieves the state-of-the-art performance on our collected tooth root segmentation dataset and the public retina dataset DRIVE. Code has been released at https://github.com/Kent0n-Li/GT-U-Net.
Towards Understanding Trends Manipulation in Pakistan Twitter
Venue: arXiv cs.IR 2021
Year: 2021

The rapid adoption of online social media platforms has transformed the way of communication and interaction. On these platforms, discussions in the form of trending topics provide a glimpse of events happening around the world in real-time. Also, these trends are used for political campaigns, public awareness, and brand promotions. Consequently, these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience. In this article, we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan. We propose 'Manipify', a framework for automatic detection and analysis of malicious users for Twitter trends. Our framework consists of three distinct modules: i) user classifier, ii) hashtag classifier, and ii) trend analyzer. The user classifier introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features. Also, the module classifies human and bot users. Next, the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories. Finally, the trend analyzer module examines users, hashtags, and tweets for hashtag reach, linguistic features and user behaviour. Our user classifier module achieves 0.91 accuracy in classifying the manipulators. We further test Manipify on the dataset comprising of 665 trending hashtags with 5.4 million tweets and 1.9 million users. The analysis of trends reveals that the trending panel is mostly dominated by political hashtags. In addition, our results show a higher contribution of human accounts in trend manipulation as compared to bots. Furthermore, we present two case studies of hashtag-wars and anti-state propaganda to implicate the real-world application of our research.
Deterministic Graph Coloring in the Streaming Model
Venue: arXiv cs.DS 2021
Year: 2021

Recent breakthroughs in graph streaming have led to the design of single-pass semi-streaming algorithms for various graph coloring problems such as $(Δ+1)$-coloring, degeneracy-coloring, coloring triangle-free graphs, and others. These algorithms are all randomized in crucial ways and whether or not there is any deterministic analogue of them has remained an important open question in this line of work. We settle this fundamental question by proving that there is no deterministic single-pass semi-streaming algorithm that given a graph $G$ with maximum degree $Δ$, can output a proper coloring of $G$ using any number of colors which is sub-exponential in $Δ$. Our proof is based on analyzing the multi-party communication complexity of a related communication game, using random graph theory type arguments that may be of independent interest. We complement our lower bound by showing that just one extra pass over the input allows one to recover an $O(Δ^2)$ coloring via a deterministic semi-streaming algorithm. This result is further extended to an $O(Δ)$ coloring in $O(\logΔ)$ passes even in dynamic streams.
Opportunistic Federation of CubeSat Constellations: a Game-Changing Paradigm Enabling Enhanced IoT Services in the Sky
Venue: arXiv cs.NI 2021
Year: 2021

Internet of Space Things (IoST) is a challenging paradigm, which is currently attracting great interest from the scientific and industrial communities. IoST is based on the integration of the space segment into the global Internet of Things (IoT) infrastructure. In the relevant literature, reference is generally made to multiple constellations of nanosatellite platforms, used to enable IoT services on a global scale, including also disadvantaged and poorly infrastructured areas. In this paper, we focus on multi-tenant IoT scenarios, wherein multiple CubeSats constellations are enabled to offer services by exploiting a dynamic federation model. The objective is to efficiently provide services in an IoST scenario by leveraging an effective cooperation strategy originally designed for terrestrial IoT networks, the Mobile-IoT-Federation-as-a-Service (MIFaaS) paradigm. We extend this vision to IoT satellite networks in order to allow a constellation of satellites to effectively execute tasks through a tight cooperative behaviour with other CubeSats constellations. The reported performance evaluation studies show that better performance, in terms of percentage of tasks successfully completed, can be achieved through the implementation of the proposed cooperation paradigm.
Moving Object Detection for Event-based vision using Graph Spectral Clustering
Venue: arXiv cs.CV 2021
Year: 2021

Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and enforcement. Neuromorphic Vision Sensors (NVS) are bio-inspired sensors that mimic the working of the human eye. Unlike conventional frame-based cameras, these sensors capture a stream of asynchronous 'events' that pose multiple advantages over the former, like high dynamic range, low latency, low power consumption, and reduced motion blur. However, these advantages come at a high cost, as the event camera data typically contains more noise and has low resolution. Moreover, as event-based cameras can only capture the relative changes in brightness of a scene, event data do not contain usual visual information (like texture and color) as available in video data from normal cameras. So, moving object detection in event-based cameras becomes an extremely challenging task. In this paper, we present an unsupervised Graph Spectral Clustering technique for Moving Object Detection in Event-based data (GSCEventMOD). We additionally show how the optimum number of moving objects can be automatically determined. Experimental comparisons on publicly available datasets show that the proposed GSCEventMOD algorithm outperforms a number of state-of-the-art techniques by a maximum margin of 30%.
Deep Neural Compression Via Concurrent Pruning and Self-Distillation
Venue: arXiv cs.LG 2021
Year: 2021

Pruning aims to reduce the number of parameters while maintaining performance close to the original network. This work proposes a novel \emph{self-distillation} based pruning strategy, whereby the representational similarity between the pruned and unpruned versions of the same network is maximized. Unlike previous approaches that treat distillation and pruning separately, we use distillation to inform the pruning criteria, without requiring a separate student network as in knowledge distillation. We show that the proposed {\em cross-correlation objective for self-distilled pruning} implicitly encourages sparse solutions, naturally complementing magnitude-based pruning criteria. Experiments on the GLUE and XGLUE benchmarks show that self-distilled pruning increases mono- and cross-lingual language model performance. Self-distilled pruned models also outperform smaller Transformers with an equal number of parameters and are competitive against (6 times) larger distilled networks. We also observe that self-distillation (1) maximizes class separability, (2) increases the signal-to-noise ratio, and (3) converges faster after pruning steps, providing further insights into why self-distilled pruning improves generalization.
iShape: A First Step Towards Irregular Shape Instance Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we introduce a brand new dataset to promote the study of instance segmentation for objects with irregular shapes. Our key observation is that though irregularly shaped objects widely exist in daily life and industrial scenarios, they received little attention in the instance segmentation field due to the lack of corresponding datasets. To fill this gap, we propose iShape, an irregular shape dataset for instance segmentation. iShape contains six sub-datasets with one real and five synthetics, each represents a scene of a typical irregular shape. Unlike most existing instance segmentation datasets of regular objects, iShape has many characteristics that challenge existing instance segmentation algorithms, such as large overlaps between bounding boxes of instances, extreme aspect ratios, and large numbers of connected components per instance. We benchmark popular instance segmentation methods on iShape and find their performance drop dramatically. Hence, we propose an affinity-based instance segmentation algorithm, called ASIS, as a stronger baseline. ASIS explicitly combines perception and reasoning to solve Arbitrary Shape Instance Segmentation including irregular objects. Experimental results show that ASIS outperforms the state-of-the-art on iShape. Dataset and code are available at https://ishape.github.io
Crypto Experts Advise What They Adopt
Venue: arXiv cs.CR 2021
Year: 2021

Previous studies have shown that developers regularly seek advice on online forums to resolve their cryptography issues. We investigated whether users who are active in cryptography discussions also use cryptography in practice. We collected the top 1% of responders who have participated in crypto discussions on Stack Overflow, and we manually analyzed their crypto contributions to open source projects on GitHub. We could identify 319 GitHub profiles that belonged to such crypto responders and found that 189 of them used cryptography in their projects. Further investigation revealed that the majority of analyzed users (i.e., 85%) use the same programming languages for crypto activity on Stack Overflow and crypto contributions on GitHub. Moreover, 90% of the analyzed users employed the same concept of cryptography in their projects as they advised about on Stack Overflow.
A Generalized Kalman Filter Augmented Deep-Learning based Approach for Autonomous Landing in MAVs
Venue: arXiv cs.RO 2021
Year: 2021

Autonomous landing systems for Micro Aerial Vehicles (MAV) have been proposed using various combinations of GPS-based, vision, and fiducial tag-based schemes. Landing is a critical activity that a MAV performs and poor resolution of GPS, degraded camera images, fiducial tags not meeting required specifications and environmental factors pose challenges. An ideal solution to MAV landing should account for these challenges and for operational challenges which could cause unplanned movements and landings. Most approaches do not attempt to solve this general problem but look at restricted sub-problems with at least one well-defined parameter. In this work, we propose a generalized end-to-end landing site detection system using a two-stage training mechanism, which makes no pre-assumption about the landing site. Experimental results show that we achieve comparable accuracy and outperform existing methods for the time required for landing.
Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims
Venue: arXiv cs.CL 2021
Year: 2021

We present an overview of the second edition of the CheckThat! Lab at CLEF 2019. The lab featured two tasks in two different languages: English and Arabic. Task 1 (English) challenged the participating systems to predict which claims in a political debate or speech should be prioritized for fact-checking. Task 2 (Arabic) asked to (A) rank a given set of Web pages with respect to a check-worthy claim based on their usefulness for fact-checking that claim, (B) classify these same Web pages according to their degree of usefulness for fact-checking the target claim, (C) identify useful passages from these pages, and (D) use the useful pages to predict the claim's factuality. CheckThat! provided a full evaluation framework, consisting of data in English (derived from fact-checking sources) and Arabic (gathered and annotated from scratch) and evaluation based on mean average precision (MAP) and normalized discounted cumulative gain (nDCG) for ranking, and F1 for classification. A total of 47 teams registered to participate in this lab, and fourteen of them actually submitted runs (compared to nine last year). The evaluation results show that the most successful approaches to Task 1 used various neural networks and logistic regression. As for Task 2, learning-to-rank was used by the highest scoring runs for subtask A, while different classifiers were used in the other subtasks. We release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in the important tasks of check-worthiness estimation and automatic claim verification.
Improved statistical machine translation using monolingual paraphrases
Venue: arXiv cs.CL 2021
Year: 2021

We propose a novel monolingual sentence paraphrasing method for augmenting the training data for statistical machine translation systems "for free" -- by creating it from data that is already available rather than having to create more aligned data. Starting with a syntactic tree, we recursively generate new sentence variants where noun compounds are paraphrased using suitable prepositions, and vice-versa -- preposition-containing noun phrases are turned into noun compounds. The evaluation shows an improvement equivalent to 33%-50% of that of doubling the amount of training data.