OpenRaiser/Intern-Atlas huggingface dataset:
Joint Active and Passive Beamforming Design for IRS-Aided Radar-Communication
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we study an intelligent reflecting surface (IRS)-aided radar-communication (Radcom) system, where the IRS is leveraged to help Radcom base station (BS) transmit the joint of communication signals and radar signals for serving communication users and tracking targets simultaneously. The objective of this paper is to minimize the total transmit power at the Radcom BS by jointly optimizing the active beamformers, including communication beamformers and radar beamformers, at the Radcom BS and the phase shifts at the IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR) required by communication users, the minimum SINR required by the radar, and the cross-correlation pattern design. In particular, we consider two cases, namely, case I and case II, based on the presence or absence of the radar cross-correlation design and the interference introduced by the IRS on the Radcom BS. For case I where the cross correlation design and the interference are not considered, we prove that the dedicated radar signals are not needed, which significantly reduces implementation complexity and simplifies algorithm design. Then, a penalty-based algorithm is proposed to solve the resulting non-convex optimization problem. Whereas for case II considering the cross-correlation design and the interference, we unveil that the dedicated radar signals are needed in general to enhance the system performance. Since the resulting optimization problem is more challenging to solve as compared with the case I, the semidefinite relaxation (SDR) based alternating optimization (AO) algorithm is proposed. Simulation results demonstrate the effectiveness of proposed algorithms and also show the superiority of the proposed scheme over various benchmark schemes.
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)
Venue: arXiv cs.LG 2022
Year: 2022

The unlabeled data are generally assumed to be normal data in detecting abnormal data via semisupervised learning. This assumption, however, causes inevitable detection error when distribution of unlabeled data is different from distribution of labeled normal dataset. To deal the problem caused by distribution gap between labeled and unlabeled data, we propose a semi-supervised anomaly detection algorithm using KL divergence (SAD-KL). The proposed SAD-KL is composed of two steps: (1) estimating KL divergence of probability density functions (PDFs) of the local outlier factors (LOFs) of the labeled normal data and the unlabeled data (2) estimating detection probability and threshold for detecting normal data in unlabeled data by using the KL divergence. We show that the PDFs of the LOFs follow Burr distribution and use them for detection. Once the threshold is computed, the SAD-KL runs iteratively until the labeling change rate is lower than the predefined threshold. Experiments results show that the SAD-KL shows superior detection probability over the existing algorithms even though it takes less learning time.
Bribes to Miners: Evidence from Ethereum
Venue: arXiv cs.CR 2022
Year: 2022

In blockchain, bribery is an inevitable problem since users with various goals can bribe miners by transferring cryptoassets. To alleviate the negative effects of such collusion, Ethereum blockchain implemented new transaction fee mechanism in the London Fork, which was deployed on August 5th, 2021. In this paper, we first filter potential bribery by scanning Ethereum transactions, and the potential bribers and bribees are centralized in a small group. Then we construct bribing proxies to measure the active level of bribery and then investigate the effects of bribery. Consequently, bribery can influence both Ethereum and other mainstream blockchains, in aspects of underlying cryptocurrency, transaction statistics, and network adoption. Moreover, the London Fork shows complicated effects on relationship between bribery and blockchain factors. Besides, bribery in Ethereum relates to stock markets, e.g., S&P 500 and Nasdaq, implying implicit interlinks between blockchain and traditional finance.
A quantitative comparison of plantar soft tissue strainability distribution and homogeneity between ulcerated and non-ulcerated patients using strain elastography
Venue: arXiv cs.CV 2022
Year: 2022

The primary objective of this study was to develop a method that allows accurate quantification of plantar soft tissue stiffness distribution and homogeneity. The secondary aim of this study is to investigate if the differences in soft tissue stiffness distribution and homogeneity can be detected between ulcerated and non-ulcerated foot. Novel measures of individual pixel stiffness, named as quantitative strainability (QS) and relative strainability (RS) were developed. SE data obtained from 39 (9 with active diabetic foot ulcers) patients with diabetic neuropathy. The patients with active diabetic foot ulcer had wound in parts of the foot other than the first metatarsal head and the heel where the elastography measures were conducted. RS was used to measure changes and gradients in the stiffness distribution of plantar soft tissues in participants with and without active diabetic foot ulcer. The plantar soft tissue homogeneity in superior-inferior direction in the left forefoot was significantly (p<0.05) higher in ulcerated group compared to non-ulcerated group. The assessment of homogeneity showed potentials to further explain the nature of the change in tissue that can increase internal stress . This can have implications in assessing the vulnerability to soft tissue damage and ulceration in diabetes.
Injection testing backed refactoring
Venue: arXiv cs.SE 2022
Year: 2022

Injection-based testing while refactoring is a pattern that minimizes the need for manual editing when altering the behaviour of a code base. Neither does it rely on a compilation or a linking process nor does it make assumptions on the structure of the code. Thus, it can be particularly useful for refactoring code that has been written in scripting languages, and specifically targets the research and engineering context. We describe the pattern and propose a set of functions for its application. The applicability of code injection for refactoring is highlighted via specific examples for deriving unit and integration tests. Finally, we comment on the customizing of the pattern and give practical advice for its implementation.
MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

This paper studies the 3D instance segmentation problem, which has a variety of real-world applications such as robotics and augmented reality. Since the surroundings of 3D objects are of high complexity, the separating of different objects is very difficult. To address this challenging problem, we propose a novel framework to group and refine the 3D instances. In practice, we first learn an offset vector for each point and shift it to its predicted instance center. To better group these points, we propose a Hierarchical Point Grouping algorithm to merge the centrally aggregated points progressively. All points are grouped into small clusters, which further gradually undergo another clustering procedure to merge into larger groups. These multi-scale groups are exploited for instance prediction, which is beneficial for predicting instances with different scales. In addition, a novel MaskScoreNet is developed to produce binary point masks of these groups for further refining the segmentation results. Extensive experiments conducted on the ScanNetV2 and S3DIS benchmarks demonstrate the effectiveness of the proposed method. For instance, our approach achieves a 66.4\% mAP with the 0.5 IoU threshold on the ScanNetV2 test set, which is 1.9\% higher than the state-of-the-art method.
Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan Region
Venue: arXiv cs.CV 2022
Year: 2022

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-available satellite imagery, can be used to produce cost-effective and high spatial-resolution crop maps. However, accessing ground truth data for supervised learning is especially challenging in developing countries due to factors such as smallholding and fragmented geography, which often results in a lack of crop type maps or even reliable cropland maps. Our area of interest for this study lies in Himachal Pradesh, India, where we aim at producing an open-access binary cropland map at 10-meter resolution for the Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline that relies on Sentinel-2 satellite images time series. We investigated two pixel-based supervised classifiers, support vector machines (SVM) and random forest (RF), which are used to classify per-pixel time series for binary cropland mapping. The ground truth data used for training, validation and testing was manually annotated from a combination of field survey reference points and visual interpretation of very high resolution (VHR) imagery. We trained and validated the models via spatial cross-validation to account for local spatial autocorrelation and selected the RF model due to overall robustness and lower computational cost. We tested the generalization capability of the chosen model at the pixel level by computing the accuracy, recall, precision, and F1-score on hold-out test sets of each district, achieving an average accuracy for the RF (our best model) of 87%. We used this model to generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps.
REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework
Venue: arXiv cs.SE 2022
Year: 2022

In this work a general framework is proposed to support the development of software systems that are able to adapt their behaviour according to the operating environment changes. The proposed approach, named REPTILE, works in a complete proactive manner and relies on Deep Reinforcement Learning-based agents to react to events, referred as novelties, that can affect the expected behaviour of the system. In our framework, two types of novelties are taken into account: those related to the context/environment and those related to the physical architecture itself. The framework, predicting those novelties before their occurrence, extracts time-changing models of the environment and uses a suitable Markov Decision Process to deal with the real-time setting. Moreover, the architecture of our RL agent evolves based on the possible actions that can be taken.
Neural Estimation and Optimization of Directed Information over Continuous Spaces
Venue: arXiv cs.IT 2022
Year: 2022

This work develops a new method for estimating and optimizing the directed information rate between two jointly stationary and ergodic stochastic processes. Building upon recent advances in machine learning, we propose a recurrent neural network (RNN)-based estimator which is optimized via gradient ascent over the RNN parameters. The estimator does not require prior knowledge of the underlying joint and marginal distributions. The estimator is also readily optimized over continuous input processes realized by a deep generative model. We prove consistency of the proposed estimation and optimization methods and combine them to obtain end-to-end performance guarantees. Applications for channel capacity estimation of continuous channels with memory are explored, and empirical results demonstrating the scalability and accuracy of our method are provided. When the channel is memoryless, we investigate the mapping learned by the optimized input generator.
5G Routing Interfered Environment
Venue: arXiv cs.NI 2022
Year: 2022

5G is the next-generation cellular network technology, with the goal of meeting the critical demand for bandwidth required to accommodate a high density of users. It employs flexible architectures to accommodate the high density. 5G is enabled by mmWave communication, which operates at frequencies ranging from 30 to 300 GHz. This paper describes the design of the 5G Routing Interfered Environment (5GRIE), a python-based environment based on Gym's methods. The environment can run different algorithms to route packets with source and destination pairs using a formulated interference model. Deep Reinforcement Learning algorithms that use Stable-Baselines 3, as well as heuristic-based algorithms like random or greedy, can be run on it. Profitable is an algorithm that is provided.
An attention mechanism based convolutional network for satellite precipitation downscaling over China
Venue: arXiv cs.CV 2022
Year: 2022

Precipitation is a key part of hydrological circulation and is a sensitive indicator of climate change. The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) datasets are widely used for global and regional precipitation investigations. However, their local application is limited by the relatively coarse spatial resolution. Therefore, in this paper, an attention mechanism based convolutional network (AMCN) is proposed to downscale GPM IMERG monthly precipitation data. The proposed method is an end-to-end network, which consists of a global cross-attention module, a multi-factor cross-attention module, and a residual convolutional module, comprehensively considering the potential relationships between precipitation and complicated surface characteristics. In addition, a degradation loss function based on low-resolution precipitation is designed to physically constrain the network training, to improve the robustness of the proposed network under different time and scale variations. The experiments demonstrate that the proposed network significantly outperforms three baseline methods. Finally, a geographic difference analysis method is introduced to further improve the downscaled results by incorporating in-situ measurements for high-quality and fine-scale precipitation estimation.
Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model
Venue: arXiv cs.LG 2022
Year: 2022

The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Red noise is essential to many operational parameterization schemes, helping model temporal correlations. We show how to build on the successes of red noise by combining the known benefits of stochasticity with machine learning. This is done using a physically-informed recurrent neural network within a probabilistic framework. Our model is competitive and often superior to both a bespoke baseline and an existing probabilistic machine learning approach (GAN) when applied to the Lorenz 96 atmospheric simulation. This is due to its superior ability to model temporal patterns compared to standard first-order autoregressive schemes. It also generalises to unseen scenarios. We evaluate across a number of metrics from the literature, and also discuss the benefits of using the probabilistic metric of hold-out likelihood.
LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications
Venue: arXiv cs.DC 2022
Year: 2022

Spark SQL has been widely deployed in industry but it is challenging to tune its performance. Recent studies try to employ machine learning (ML) to solve this problem, but suffer from two drawbacks. First, it takes a long time (high overhead) to collect training samples. Second, the optimal configuration for one input data size of the same application might not be optimal for others. To address these issues, we propose a novel Bayesian Optimization (BO) based approach named LOCAT to automatically tune the configurations of Spark SQL applications online. LOCAT innovates three techniques. The first technique, named QCSA, eliminates the configuration-insensitive queries by Query Configuration Sensitivity Analysis (QCSA) when collecting training samples. The second technique, dubbed DAGP, is a Datasize-Aware Gaussian Process (DAGP) which models the performance of an application as a distribution of functions of configuration parameters as well as input data size. The third technique, called IICP, Identifies Important Configuration Parameters (IICP) with respect to performance and only tunes the important ones. As such, LOCAT can tune the configurations of a Spark SQL application with low overhead and adapt to different input data sizes. We employ Spark SQL applications from benchmark suites TPC-DS, TPC-H, and HiBench running on two significantly different clusters, a four-node ARM cluster and an eight-node x86 cluster, to evaluate LOCAT. The experimental results on the ARM cluster show that LOCAT accelerates the optimization procedures of the state-of-the-art approaches by at least 4.1x and up to 9.7x; moreover, LOCAT improves the application performance by at least 1.9x and up to 2.4x. On the x86 cluster, LOCAT shows similar results to those on the ARM cluster.
Moving Obstacle Avoidance: a Data-Driven Risk-Aware Approach
Venue: arXiv cs.RO 2022
Year: 2022

This paper proposes a new structured method for a moving agent to predict the paths of dynamically moving obstacles and avoid them using a risk-aware model predictive control (MPC) scheme. Given noisy measurements of the a priori unknown obstacle trajectory, a bootstrapping technique predicts a set of obstacle trajectories. The bootstrapped predictions are incorporated in the MPC optimization using a risk-aware methodology so as to provide probabilistic guarantees on obstacle avoidance. We validate our methods using simulations of a 3-dimensional multi-rotor drone that avoids various moving obstacles, such as a thrown ball and a frisbee with air drag.
A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps
Venue: arXiv cs.LG 2022
Year: 2022

Climate control of buildings makes up a significant portion of global energy consumption, with groundwater heat pumps providing a suitable alternative. To prevent possibly negative interactions between heat pumps throughout a city, city planners have to optimize their layouts in the future. We develop a novel data-driven approach for building small-scale surrogates for modelling the thermal plumes generated by groundwater heat pumps in the surrounding subsurface water. Building on a data set generated from 2D numerical simulations, we train a convolutional neural network for predicting steady-state subsurface temperature fields from a given subsurface velocity field. We show that compared to existing models ours can capture more complex dynamics while still being quick to compute. The resulting surrogate is thus well-suited for interactive design tools by city planners.
Are Deepfakes Concerning? Analyzing Conversations of Deepfakes on Reddit and Exploring Societal Implications
Venue: arXiv cs.HC 2022
Year: 2022

Deepfakes are synthetic content generated using advanced deep learning and AI technologies. The advancement of technology has created opportunities for anyone to create and share deepfakes much easier. This may lead to societal concerns based on how communities engage with it. However, there is limited research available to understand how communities perceive deepfakes. We examined deepfake conversations on Reddit from 2018 to 2021 -- including major topics and their temporal changes as well as implications of these conversations. Using a mixed-method approach -- topic modeling and qualitative coding, we found 6,638 posts and 86,425 comments discussing concerns of the believable nature of deepfakes and how platforms moderate them. We also found Reddit conversations to be pro-deepfake and building a community that supports creating and sharing deepfake artifacts and building a marketplace regardless of the consequences. Possible implications derived from qualitative codes indicate that deepfake conversations raise societal concerns. We propose that there are implications for Human Computer Interaction (HCI) to mitigate the harm created from deepfakes.
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation
Venue: arXiv cs.LG 2022
Year: 2022

Federated learning is a framework for distributed optimization that places emphasis on communication efficiency. In particular, it follows a client-server broadcast model and is particularly appealing because of its ability to accommodate heterogeneity in client compute and storage resources, non-i.i.d. data assumptions, and data privacy. Our contribution is to offer a new federated learning algorithm, FedADMM, for solving non-convex composite optimization problems with non-smooth regularizers. We prove converges of FedADMM for the case when not all clients are able to participate in a given communication round under a very general sampling model.
RoBoa: Construction and Evaluation of a Steerable Vine Robot for Search and Rescue Applications
Venue: arXiv cs.RO 2022
Year: 2022

RoBoa is a vine-like search and rescue robot that can explore narrow and cluttered environments such as destroyed buildings. The robot assists rescue teams in finding and communicating with trapped people. It employs the principle of vine robots for locomotion, everting the tip of its tube to move forward. Inside the tube, pneumatic actuators enable lateral movement. The head carries sensors and is mounted outside at the tip of the tube. At the back, a supply box contains the rolled up tube and provides pressurized air, power, computation, as well as an interface for the user to interact with the system. A decentralized control scheme was implemented that reduces the required number of cables and takes care of the low-level control of the pneumatic actuators. The design, characterization, and experimental evaluation of the system and its crucial components is shown. The complete prototype is fully functional and was evaluated in a realistic environment of a collapsed building where the remote-controlled robot was able to repeatedly locate a trapped person after a travel distance of about 10 m.
Model-Based Control of Planar Piezoelectric Inchworm Soft Robot for Crawling in Constrained Environments
Venue: arXiv cs.RO 2022
Year: 2022

Soft robots have drawn significant attention recently for their ability to achieve rich shapes when interacting with complex environments. However, their elasticity and flexibility compared to rigid robots also pose significant challenges for precise and robust shape control in real-time. Motivated by their potential to operate in highly-constrained environments, as in search-and-rescue operations, this work addresses these challenges of soft robots by developing a model-based full-shape controller, validated and demonstrated by experiments. A five-actuator planar soft robot was constructed with planar piezoelectric layers bonded to a steel foil substrate, enabling inchworm-like motion. The controller uses a soft-body continuous model for shape planning and control, given target shapes and/or environmental constraints, such as crawling under overhead barriers or "roof" safety lines. An approach to background model calibrations is developed to address deviations of actual robot shape due to material parameter variations and drift. Full experimental shape control and optimal movement under a roof safety line are demonstrated, where the robot maximizes its speed within the overhead constraint. The mean-squared error between the measured and target shapes improves from ~0.05 cm$^{2}$ without calibration to ~0.01 cm$^{2}$ with calibration. Simulation-based validation is also performed with various different roof shapes.