OpenRaiser/Intern-Atlas huggingface dataset:
Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation
Venue: arXiv cs.NI 2021
Year: 2021

Colosseum is an open-access and publicly-available large-scale wireless testbed for experimental research via virtualized and softwarized waveforms and protocol stacks on a fully programmable, "white-box" platform. Through 256 state-of-the-art software-defined radios and a massive channel emulator core, Colosseum can model virtually any scenario, enabling the design, development and testing of solutions at scale in a variety of deployments and channel conditions. These Colosseum radio-frequency scenarios are reproduced through high-fidelity FPGA-based emulation with finite-impulse response filters. Filters model the taps of desired wireless channels and apply them to the signals generated by the radio nodes, faithfully mimicking the conditions of real-world wireless environments. In this paper, we introduce Colosseum as a testbed that is for the first time open to the research community. We describe the architecture of Colosseum and its experimentation and emulation capabilities. We then demonstrate the effectiveness of Colosseum for experimental research at scale through exemplary use cases including prevailing wireless technologies (e.g., cellular and Wi-Fi) in spectrum sharing and unmanned aerial vehicle scenarios. A roadmap for Colosseum future updates concludes the paper.
Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly
Venue: arXiv cs.NE 2021
Year: 2021

With the advent of Genome Sequencing, the field of Personalized Medicine has been revolutionized. From drug testing and studying diseases and mutations to clan genomics, studying the genome is required. However, genome sequence assembly is a very complex combinatorial optimization problem of computational biology. PSO is a popular meta-heuristic swarm intelligence optimization algorithm, used to solve combinatorial optimization problems. In this paper, we propose a new variant of PSO to address this permutation-optimization problem. PSO is integrated with the Chaos and Levy Flight (A random walk algorithm) to effectively balance the exploration and exploitation capability of the algorithm. Empirical experiments are conducted to evaluate the performance of the proposed method in comparison to the other variants of the PSO proposed in the literature. The analysis is conducted on four DNA coverage datasets. The conducted analysis demonstrates that the proposed model attain a better performance with better reliability and consistency in comparison to other competitive methods in all cases.
Using a market basket analysis in tourism studies
Venue: arXiv cs.SI 2021
Year: 2021

Understanding tourist visitation patterns is crucial for decision makers in order to create smart tourism industry. A growing body of tourism research uses geo-location data in order to better understand tourism demand. In this paper, we present a new approach based on a market basket analysis. This approach uses geo-location data shared by tourists on tourism platforms in order to bundle the range of available tourism services and understand which experiences are consumed together. The approach was tested on the case of Vienna, Austria. Based on our analyses we argue that the proposed approach has potential for use at the destination level and provides relevant information on tourism demand patterns important for smart tourism decision-making.
Auction Design through Multi-Agent Learning in Peer-to-Peer Energy Trading
Venue: arXiv cs.GT 2021
Year: 2021

Distributed energy resources (DERs), such as rooftop solar panels, are growing rapidly and are reshaping power systems. To promote DERs, feed-in-tariff (FIT) is usually adopted by utilities to pay DER owners certain fixed rates for supplying energy to the grid. An alternative to FIT is a market-based approach; that is, consumers and DER owners trade energy in an auction-based peer-to-peer (P2P) market, and the rates are determined based on supply and demand. However, the auction complexity and market participants' bounded rationality may invalidate many well-established theories on auction design and hinder market development. To address the challenges, we propose an automated bidding framework based on multi-agent, multi-armed bandit learning for repeated auctions, which aims to minimize each bidder's cumulative regret. Numerical results indicate convergence of such a multi-agent learning game to a steady-state. Being particularly interested in auction designs, we have applied the framework to four different implementations of repeated double-side auctions to compare their market outcomes. While it is difficult to pick a clear winner, $k$-double auction (a variant of uniform pricing auction) and McAfee auction (a variant of Vickrey double-auction) appear to perform well in general, with their respective strengths and weaknesses.
Self-Supervision and Spatial-Sequential Attention Based Loss for Multi-Person Pose Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Bottom-up based multi-person pose estimation approaches use heatmaps with auxiliary predictions to estimate joint positions and belonging at one time. Recently, various combinations between auxiliary predictions and heatmaps have been proposed for higher performance, these predictions are supervised by the corresponding L2 loss function directly. However, the lack of more explicit supervision results in low features utilization and contradictions between predictions in one model. To solve these problems, this paper proposes (i) a new loss organization method which uses self-supervised heatmaps to reduce prediction contradictions and spatial-sequential attention to enhance networks' features extraction; (ii) a new combination of predictions composed by heatmaps, Part Affinity Fields (PAFs) and our block-inside offsets to fix pixel-level joints positions and further demonstrates the effectiveness of proposed loss function. Experiments are conducted on the MS COCO keypoint dataset and adopting OpenPose as the baseline model. Our method outperforms the baseline overall. On the COCO verification dataset, the mAP of OpenPose trained with our proposals outperforms the OpenPose baseline by over 5.5%.
Scheduling of Graph Queries: Controlling Intra- and Inter-query Parallelism for a High System Throughput
Venue: arXiv cs.DB 2021
Year: 2021

The vast amounts of data used in social, business or traffic networks, biology and other natural sciences are often managed in graph-based data sets, consisting of a few thousand up to billions and trillions of vertices and edges, respectively. Typical applications utilizing such data either execute one or a few complex queries or many small queries at the same time interactively or as batch jobs. Furthermore, graph processing is inherently complex, as data sets can substantially differ (scale free vs. constant degree), and algorithms exhibit diverse behavior (computational intensity, local or global, push- or pull-based). This work is concerned with multi-query execution by automatically controlling the degree of parallelization, with overall objectives including high system utilization, low synchronization cost, and highly efficient concurrent execution. The underlying concept is three-fold: (1) sampling is used to determine graph statistics, (2) parallelization constraints are derived from algorithm and system properties, and (3) suitable work packages are generated based on the previous two aspects. We evaluate the proposed concept using different algorithms on synthetic and real world data sets, with up to 16 concurrent sessions (queries). The results demonstrate a robust performance in spite of these various configurations, and in particular that the performance is always close to or even slightly ahead of the performance of manually optimized implementations. Furthermore, the similar performance to manually optimized implementations under extreme configurations, which require either a full parallelization (few large queries) or complete sequential execution (many small queries), shows that the proposed concept exhibits a particularly low overhead.
HALP: Hardware-Aware Latency Pruning
Venue: arXiv cs.CV 2021
Year: 2021

Structural pruning can simplify network architecture and improve inference speed. We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global resource allocation optimization problem, aiming at maximizing the accuracy while constraining latency under a predefined budget. For filter importance ranking, HALP leverages latency lookup table to track latency reduction potential and global saliency score to gauge accuracy drop. Both metrics can be evaluated very efficiently during pruning, allowing us to reformulate global structural pruning under a reward maximization problem given target constraint. This makes the problem solvable via our augmented knapsack solver, enabling HALP to surpass prior work in pruning efficacy and accuracy-efficiency trade-off. We examine HALP on both classification and detection tasks, over varying networks, on ImageNet and VOC datasets. In particular, for ResNet-50/-101 pruning on ImageNet, HALP improves network throughput by $1.60\times$/$1.90\times$ with $+0.3\%$/$-0.2\%$ top-1 accuracy changes, respectively. For SSD pruning on VOC, HALP improves throughput by $1.94\times$ with only a $0.56$ mAP drop. HALP consistently outperforms prior art, sometimes by large margins.
AdamD: Improved bias-correction in Adam
Venue: arXiv cs.LG 2021
Year: 2021

Here I present a small update to the bias-correction term in the Adam optimizer that has the advantage of making smaller gradient updates in the first several steps of training. With the default bias-correction, Adam may actually make larger than requested gradient updates early in training. By only including the well-justified bias-correction of the second moment gradient estimate, $v_t$, and excluding the bias-correction on the first-order estimate, $m_t$, we attain these more desirable gradient update properties in the first series of steps. The default implementation of Adam may be as sensitive as it is to the hyperparameters $β_1, β_2$ partially due to the originally proposed bias correction procedure, and its behavior in early steps.
ReachBot: A Small Robot for Large Mobile Manipulation Tasks
Venue: arXiv cs.RO 2021
Year: 2021

Robots are widely deployed in space environments because of their versatility and robustness. However, adverse gravity conditions and challenging terrain geometry expose the limitations of traditional robot designs, which are often forced to sacrifice one of mobility or manipulation capabilities to attain the other. Prospective climbing operations in these environments reveals a need for small, compact robots capable of versatile mobility and manipulation. We propose a novel robotic concept called ReachBot that fills this need by combining two existing technologies: extendable booms and mobile manipulation. ReachBot leverages the reach and tensile strength of extendable booms to achieve an outsized reachable workspace and wrench capability. Through their lightweight, compactable structure, these booms also reduce mass and complexity compared to traditional rigid-link articulated-arm designs. Using these advantages, ReachBot excels in mobile manipulation missions in low gravity or that require climbing, particularly when anchor points are sparse. After introducing the ReachBot concept, we discuss modeling approaches and strategies for increasing stability and robustness. We then develop a 2D analytical model for ReachBot's dynamics inspired by grasp models for dexterous manipulators. Next, we introduce a waypoint-tracking controller for a planar ReachBot in microgravity. Our simulation results demonstrate the controller's robustness to disturbances and modeling error. Finally, we briefly discuss next steps that build on these initially promising results to realize the full potential of ReachBot.
SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning
Venue: arXiv cs.CV 2021
Year: 2021

For many years, the family of convolutional neural networks (CNNs) has been a workhorse in deep learning. Recently, many novel CNN structures have been designed to address increasingly challenging tasks. To make them work efficiently on edge devices, researchers have proposed various structured network pruning strategies to reduce their memory and computational cost. However, most of them only focus on reducing the number of filter channels per layer without considering the redundancy within individual filter channels. In this work, we explore pruning from another dimension, the kernel size. We develop a CNN pruning framework called SMOF, which Squeezes More Out of Filters by reducing both kernel size and the number of filter channels. Notably, SMOF is friendly to standard hardware devices without any customized low-level implementations, and the pruning effort by kernel size reduction does not suffer from the fixed-size width constraint in SIMD units of general-purpose processors. The pruned networks can be deployed effortlessly with significant running time reduction. We also support these claims via extensive experiments on various CNN structures and general-purpose processors for mobile devices.
Deep Image Matting with Flexible Guidance Input
Venue: arXiv cs.CV 2021
Year: 2021

Image matting is an important computer vision problem. Many existing matting methods require a hand-made trimap to provide auxiliary information, which is very expensive and limits the real world usage. Recently, some trimap-free methods have been proposed, which completely get rid of any user input. However, their performance lag far behind trimap-based methods due to the lack of guidance information. In this paper, we propose a matting method that use Flexible Guidance Input as user hint, which means our method can use trimap, scribblemap or clickmap as guidance information or even work without any guidance input. To achieve this, we propose Progressive Trimap Deformation(PTD) scheme that gradually shrink the area of the foreground and background of the trimap with the training step increases and finally become a scribblemap. To make our network robust to any user scribble and click, we randomly sample points on foreground and background and perform curve fitting. Moreover, we propose Semantic Fusion Module(SFM) which utilize the Feature Pyramid Enhancement Module(FPEM) and Joint Pyramid Upsampling(JPU) in matting task for the first time. The experiments show that our method can achieve state-of-the-art results comparing with existing trimap-based and trimap-free methods.
A Decentralized Framework for Serverless Edge Computing in the Internet of Things
Venue: arXiv cs.NI 2021
Year: 2021

Serverless computing is becoming widely adopted among cloud providers, thus making increasingly popular the Function-as-a-Service (FaaS) programming model, where the developers realize services by packaging sequences of stateless function calls. The current technologies are very well suited to data centers, but cannot provide equally good performance in decentralized environments, such as edge computing systems, which are expected to be typical for Internet of Things (IoT) applications. In this paper, we fill this gap by proposing a framework for efficient dispatching of stateless tasks to in-network executors so as to minimize the response times while exhibiting short- and long-term fairness, also leveraging information from a virtualized network infrastructure when available. Our solution is shown to be simple enough to be installed on devices with limited computational capabilities, such as IoT gateways, especially when using a hierarchical forwarding extension. We evaluate the proposed platform by means of extensive emulation experiments with a prototype implementation in realistic conditions. The results show that it is able to smoothly adapt to the mobility of clients and to the variations of their service request patterns, while coping promptly with network congestion.
Pixel-Level Face Image Quality Assessment for Explainable Face Recognition
Venue: arXiv cs.CV 2021
Year: 2021

An essential factor to achieve high performance in face recognition systems is the quality of its samples. Since these systems are involved in daily life there is a strong need of making face recognition processes understandable for humans. In this work, we introduce the concept of pixel-level face image quality that determines the utility of pixels in a face image for recognition. We propose a training-free approach to assess the pixel-level qualities of a face image given an arbitrary face recognition network. To achieve this, a model-specific quality value of the input image is estimated and used to build a sample-specific quality regression model. Based on this model, quality-based gradients are back-propagated and converted into pixel-level quality estimates. In the experiments, we qualitatively and quantitatively investigated the meaningfulness of our proposed pixel-level qualities based on real and artificial disturbances and by comparing the explanation maps on faces incompliant with the ICAO standards. In all scenarios, the results demonstrate that the proposed solution produces meaningful pixel-level qualities enhancing the interpretability of the complete face image quality. The code is publicly available
Spatial Location Constraint Prototype Loss for Open Set Recognition
Venue: arXiv cs.CV 2021
Year: 2021

One of the challenges in pattern recognition is open set recognition. Compared with closed set recognition, open set recognition needs to reduce not only the empirical risk, but also the open space risk, and the reduction of these two risks corresponds to classifying the known classes and identifying the unknown classes respectively. How to reduce the open space risk is the key of open set recognition. This paper explores the origin of the open space risk by analyzing the distribution of known and unknown classes features. On this basis, the spatial location constraint prototype loss function is proposed to reduce the two risks simultaneously. Extensive experiments on multiple benchmark datasets and many visualization results indicate that our methods is superior to most existing approaches.
Certifying C program correctness with respect to CompCert with VeriFast
Venue: arXiv cs.LO 2021
Year: 2021

VeriFast is a powerful tool for verification of various correctness properties of C programs using symbolic execution. However, VeriFast itself has not been verified. We present a proof-of-concept extension which generates a correctness certificate for each successful verification run individually. This certificate takes the form of a Coq script containing two proofs which, when successfully checked by Coq, together remove the need for trusting in the correctness of VeriFast itself. The first proves a lemma expressing the correctness of the program with respect to a big step operational semantics developed by ourselves, intended to reflect VeriFast's interpretation of C. We have formalized this semantics in Coq as cbsem. This lemma is proven by symbolic execution in Coq, which in turn is implemented by transforming the exported AST of the program into a Coq proposition representing the symbolic execution performed by VeriFast itself. The second proves the correctness of the same C program with respect to CompCert's Clight big step semantics. This proof simply applies our proof of the soundness of cbsem with respect to CompCert Clight to the first proof.
Transfer beyond the Field of View: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2021
Year: 2021

Autonomous vehicles clearly benefit from the expanded Field of View (FoV) of 360-degree sensors, but modern semantic segmentation approaches rely heavily on annotated training data which is rarely available for panoramic images. We look at this problem from the perspective of domain adaptation and bring panoramic semantic segmentation to a setting, where labelled training data originates from a different distribution of conventional pinhole camera images. To achieve this, we formalize the task of unsupervised domain adaptation for panoramic semantic segmentation and collect DensePASS - a novel densely annotated dataset for panoramic segmentation under cross-domain conditions, specifically built to study the Pinhole-to-Panoramic domain shift and accompanied with pinhole camera training examples obtained from Cityscapes. DensePASS covers both, labelled- and unlabelled 360-degree images, with the labelled data comprising 19 classes which explicitly fit the categories available in the source (i.e. pinhole) domain. Since data-driven models are especially susceptible to changes in data distribution, we introduce P2PDA - a generic framework for Pinhole-to-Panoramic semantic segmentation which addresses the challenge of domain divergence with different variants of attention-augmented domain adaptation modules, enabling the transfer in output-, feature-, and feature confidence spaces. P2PDA intertwines uncertainty-aware adaptation using confidence values regulated on-the-fly through attention heads with discrepant predictions. Our framework facilitates context exchange when learning domain correspondences and dramatically improves the adaptation performance of accuracy- and efficiency-focused models. Comprehensive experiments verify that our framework clearly surpasses unsupervised domain adaptation- and specialized panoramic segmentation approaches.
Improving Non-autoregressive Generation with Mixup Training
Venue: arXiv cs.CL 2021
Year: 2021

While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation tasks remains a challenge. To solve this problem, we present a non-autoregressive generation model based on pre-trained transformer models. To bridge the gap between autoregressive and non-autoregressive models, we propose a simple and effective iterative training method called MIx Source and pseudo Target (MIST). Unlike other iterative decoding methods, which sacrifice the inference speed to achieve better performance based on multiple decoding iterations, MIST works in the training stage and has no effect on inference time. Our experiments on three generation benchmarks including question generation, summarization and paraphrase generation, show that the proposed framework achieves the new state-of-the-art results for fully non-autoregressive models. We also demonstrate that our method can be used to a variety of pre-trained models. For instance, MIST based on the small pre-trained model also obtains comparable performance with seq2seq models.
Joint Power Control, Channel Assignment and Cell Association in Heterogeneous Cellular Networks
Venue: arXiv cs.IT 2021
Year: 2021

Heterogeneous network (HetNet) is a promising concept for increasing capacity and alleviating spectrum scarcity. In HetNets, however, channel assignment and transmit power control affect the distribution of users among base stations. We present a novel scheme to maximize the uplink sum rate in two-tier HetNets with one macrocell and several femtocells, where the transmit power of each user is bounded and at least one channel is assigned to each user. We divide the problem into two sub-problems: one for channel assignment and one for transmit power control and solve them by iteratively alternating between the two. Our scheme is convergent and yields a transmission rate above 6 bps/Hz for almost 50% of users as compared to the same for 10% of users in SINR-based schemes. When users are in cell boundaries, the average transmission rate for fractional frequency reuse is up to 20% more compared to the conventional full frequency reuse. This is due to reduced transmit power resulting in less interference.