OpenRaiser/Intern-Atlas huggingface dataset:
Multi-fidelity reinforcement learning framework for shape optimization
Venue: arXiv cs.LG 2022
Year: 2022

Deep reinforcement learning (DRL) is a promising outer-loop intelligence paradigm which can deploy problem solving strategies for complex tasks. Consequently, DRL has been utilized for several scientific applications, specifically in cases where classical optimization or control methods are limited. One key limitation of conventional DRL methods is their episode-hungry nature which proves to be a bottleneck for tasks which involve costly evaluations of a numerical forward model. In this article, we address this limitation of DRL by introducing a controlled transfer learning framework that leverages a multi-fidelity simulation setting. Our strategy is deployed for an airfoil shape optimization problem at high Reynolds numbers, where our framework can learn an optimal policy for generating efficient airfoil shapes by gathering knowledge from multi-fidelity environments and reduces computational costs by over 30\%. Furthermore, our formulation promotes policy exploration and generalization to new environments, thereby preventing over-fitting to data from solely one fidelity. Our results demonstrate this framework's applicability to other scientific DRL scenarios where multi-fidelity environments can be used for policy learning.
Selecting cells in a raster database for maximal impact intervention in the presence of spatial interaction: Computational complexity of a Multiple vs. a Single Flow Direction Method
Venue: arXiv cs.CE 2022
Year: 2022

To minimize the sediment flowing to the outlet of a river catchment with minimal effort or cost, it is important to select the best areas to perform a certain intervention, e.g., afforestation. CAMF (Cellular Automata based heuristic for Minimizing Flow) is a method that performs this selection process iteratively in a raster geodatabase environment. To simulate the flow paths, the original CAMF uses a Single Flow Direction (SFD) algorithm. However, SFD fails to reflect the real nature of flow transport, especially in areas with low relief. This paper describes and analyzes the integration of a Multiple Flow Direction (MFD) algorithm in CAMF, in order to provide a more realistic flow simulation. We compare the computational complexity of CAMF-SFD and CAMF-MFD and we discuss the scalability w.r.t. the number of cells involved. We evaluate the behavior of both variants for sediment yield minimization by afforestation in two catchments with different properties.
Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion for Autonomous Driving
Venue: arXiv cs.CV 2022
Year: 2022

Autonomous driving demands accurate perception and safe decision-making. To achieve this, automated vehicles are now equipped with multiple sensors (e.g., camera, Lidar, etc.), enabling them to exploit complementary environmental context by fusing data from different sensing modalities. With the success of Deep Convolutional Neural Network(DCNN), the fusion between DCNNs has been proved as a promising strategy to achieve satisfactory perception accuracy. However, mainstream existing DCNN fusion schemes conduct fusion by directly element-wisely adding feature maps extracted from different modalities together at various stages, failing to consider whether the features being fused are matched or not. Therefore, we first propose a feature disparity metric to quantitatively measure the degree of feature disparity between the feature maps being fused. We then propose Fusion-filter as a feature-matching techniques to tackle the feature-mismatching issue. We also propose a Layer-sharing technique in the deep layer that can achieve better accuracy with less computational overhead. Together with the help of the feature disparity to be an additional loss, our proposed technologies enable DCNN to learn corresponding feature maps with similar characteristics and complementary visual context from different modalities to achieve better accuracy. Experimental results demonstrate that our proposed fusion technique can achieve better accuracy on KITTI dataset with less computational resources demand.
MLProxy: SLA-Aware Reverse Proxy for Machine Learning Inference Serving on Serverless Computing Platforms
Venue: arXiv cs.DC 2022
Year: 2022

Serving machine learning inference workloads on the cloud is still a challenging task on the production level. Optimal configuration of the inference workload to meet SLA requirements while optimizing the infrastructure costs is highly complicated due to the complex interaction between batch configuration, resource configurations, and variable arrival process. Serverless computing has emerged in recent years to automate most infrastructure management tasks. Workload batching has revealed the potential to improve the response time and cost-effectiveness of machine learning serving workloads. However, it has not yet been supported out of the box by serverless computing platforms. Our experiments have shown that for various machine learning workloads, batching can hugely improve the system's efficiency by reducing the processing overhead per request. In this work, we present MLProxy, an adaptive reverse proxy to support efficient machine learning serving workloads on serverless computing systems. MLProxy supports adaptive batching to ensure SLA compliance while optimizing serverless costs. We performed rigorous experiments on Knative to demonstrate the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of serverless deployment by up to 92% while reducing SLA violations by up to 99% that can be generalized across state-of-the-art model serving frameworks.
Tight Dynamic Problem Lower Bounds from Generalized BMM and OMv
Venue: arXiv cs.CC 2022
Year: 2022

The main theme of this paper is using $k$-dimensional generalizations of the combinatorial Boolean Matrix Multiplication (BMM) hypothesis and the closely-related Online Matrix Vector Multiplication (OMv) hypothesis to prove new tight conditional lower bounds for dynamic problems. The combinatorial $k$-Clique hypothesis, which is a standard hypothesis in the literature, naturally generalizes the combinatorial BMM hypothesis. In this paper, we prove tight lower bounds for several dynamic problems under the combinatorial $k$-Clique hypothesis. For instance, we show that: * The Dynamic Range Mode problem has no combinatorial algorithms with $\mathrm{poly}(n)$ pre-processing time, $O(n^{2/3-ε})$ update time and $O(n^{2/3-ε})$ query time for any $ε> 0$, matching the known upper bounds for this problem. Previous lower bounds only ruled out algorithms with $O(n^{1/2-ε})$ update and query time under the OMv hypothesis. Other examples include tight combinatorial lower bounds for Dynamic Subgraph Connectivity, Dynamic 2D Orthogonal Range Color Counting, Dynamic 2-Pattern Document Retrieval, and Dynamic Range Mode in higher dimensions. Furthermore, we propose the OuMv$_k$ hypothesis as a natural generalization of the OMv hypothesis. Under this hypothesis, we prove tight lower bounds for various dynamic problems. For instance, we show that: * The Dynamic Skyline Points Counting problem in $(2k-1)$-dimensional space has no algorithm with $\mathrm{poly}(n)$ pre-processing time and $O(n^{1-1/k-ε})$ update and query time for $ε> 0$, even if the updates are semi-online. Other examples include tight conditional lower bounds for (semi-online) Dynamic Klee's measure for unit cubes, and high-dimensional generalizations of Erickson's problem and Langerman's problem.
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devices
Venue: arXiv cs.LG 2022
Year: 2022

Along with the progress of AI democratization, neural networks are being deployed more frequently in edge devices for a wide range of applications. Fairness concerns gradually emerge in many applications, such as face recognition and mobile medical. One fundamental question arises: what will be the fairest neural architecture for edge devices? By examining the existing neural networks, we observe that larger networks typically are fairer. But, edge devices call for smaller neural architectures to meet hardware specifications. To address this challenge, this work proposes a novel Fairness- and Hardware-aware Neural architecture search framework, namely FaHaNa. Coupled with a model freezing approach, FaHaNa can efficiently search for neural networks with balanced fairness and accuracy, while guaranteed to meet hardware specifications. Results show that FaHaNa can identify a series of neural networks with higher fairness and accuracy on a dermatology dataset. Target edge devices, FaHaNa finds a neural architecture with slightly higher accuracy, 5.28x smaller size, 15.14% higher fairness score, compared with MobileNetV2; meanwhile, on Raspberry PI and Odroid XU-4, it achieves 5.75x and 5.79x speedup.
Scalable Query Answering under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach
Venue: arXiv cs.AI 2022
Year: 2022

Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there is also available ontological knowledge encoding the current state of the art regarding its different areas, activation patterns, key words associated with studies, etc. Furthermore, there is an inherent uncertainty associated with brain scans arising from the mapping between voxels -- 3D pixels -- and actual points in different individual brains. Unfortunately, there is currently no unifying framework for accessing such collections of rich heterogeneous data under uncertainty, making it necessary for researchers to rely on ad hoc tools. In particular, one major weakness of current tools that attempt to address this kind of task is that only very limited propositional query languages have been developed. In this paper, we present NeuroLang, an ontology language with existential rules, probabilistic uncertainty, and built-in mechanisms to guarantee tractable query answering over very large datasets. After presenting the language and its general query answering architecture, we discuss real-world use cases showing how NeuroLang can be applied to practical scenarios for which current tools are inadequate.
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
Venue: arXiv cs.IR 2022
Year: 2022

Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item ratings while omitting the underlying causes behind the ratings. For example, the widely-used Yahoo!R3 dataset contains little information on the causes of the user-movie ratings. A solution could be to conduct surveys and require the users to provide such information. In practice, the user surveys can hardly avoid compliance issues and sparse user responses, which greatly hinders the exploration of causality-based recommendation. To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios. To illustrate the use of our framework, we construct a semi-synthetic dataset with Causal Tags And Ratings (CTAR), based on the movies as well as their descriptive tags and rating information collected from a famous movie rating website. Using the collected data and the causal graph, the user-item-ratings and their corresponding user-item-tags are automatically generated, which provides the reasons (selected tags) why the user rates the items. Descriptive statistics and baseline results regarding the CTAR dataset are also reported. The proposed data generation framework is not limited to recommendation, and the released APIs can be used to generate customized datasets for other research tasks.
Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting
Venue: arXiv cs.LG 2022
Year: 2022

Transformer-based methods have shown great potential in long-term time series forecasting. However, most of these methods adopt the standard point-wise self-attention mechanism, which not only becomes intractable for long-term forecasting since its complexity increases quadratically with the length of time series, but also cannot explicitly capture the predictive dependencies from contexts since the corresponding key and value are transformed from the same point. This paper proposes a predictive Transformer-based model called {\em Preformer}. Preformer introduces a novel efficient {\em Multi-Scale Segment-Correlation} mechanism that divides time series into segments and utilizes segment-wise correlation-based attention for encoding time series. A multi-scale structure is developed to aggregate dependencies at different temporal scales and facilitate the selection of segment length. Preformer further designs a predictive paradigm for decoding, where the key and value come from two successive segments rather than the same segment. In this way, if a key segment has a high correlation score with the query segment, its successive segment contributes more to the prediction of the query segment. Extensive experiments demonstrate that our Preformer outperforms other Transformer-based methods.
Compositional Model Checking of Consensus Protocols Specified in TLA+ via Interaction-Preserving Abstraction
Venue: arXiv cs.DC 2022
Year: 2022

Consensus protocols are widely used in building reliable distributed software systems and its correctness is of vital importance. TLA+ is a lightweight formal specification language which enables precise specification of system design and exhaustive checking of the design without any human effort. The features of TLA+ make it widely used in the specification and model checking of consensus protocols, both in academia and industry. However, the application of TLA+ is limited by the state explosion problem in model checking. Though compositional model checking is essential to tame the state explosion problem, existing compositional checking techniques do not sufficiently consider the characteristics of TLA+. In this work, we propose the Interaction-Preserving Abstraction (IPA) framework, which leverages the features of TLA+ and enables practical and efficient compositional model checking of consensus protocols specified in TLA+. In the IPA framework, system specification is partitioned into multiple modules, and each module is divided to the internal part and the interaction part. The basic idea of the interaction-preserving abstraction is to omit the internal part of each module, such that another module cannot distinguish whether it is interacting with the original module or the coarsened abstract one. We use the IPA framework to the compositional checking of the TLA+ specification of two consensus protocols Raft and ParallelRaft. Raft is a consensus protocol which is originally developed in the academia and then widely used in industry. ParallelRaft is the replication protocol in PolarFS, the distributed file system for the commercial database Alibaba PoloarDB. We demonstrate that the IPA framework is easy to use in realistic scenarios and at the same time significantly reduces the model checking cost.
A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing
Venue: arXiv cs.CV 2022
Year: 2022

Due to the availability of multi-modal remote sensing (RS) image archives, one of the most important research topics is the development of cross-modal RS image retrieval (CM-RSIR) methods that search semantically similar images across different modalities. Existing CM-RSIR methods require the availability of a high quality and quantity of annotated training images. The collection of a sufficient number of reliable labeled images is time consuming, complex and costly in operational scenarios, and can significantly affect the final accuracy of CM-RSIR. In this paper, we introduce a novel self-supervised CM-RSIR method that aims to: i) model mutual-information between different modalities in a self-supervised manner; ii) retain the distributions of modal-specific feature spaces similar to each other; and iii) define the most similar images within each modality without requiring any annotated training image. To this end, we propose a novel objective including three loss functions that simultaneously: i) maximize mutual information of different modalities for inter-modal similarity preservation; ii) minimize the angular distance of multi-modal image tuples for the elimination of inter-modal discrepancies; and iii) increase cosine similarity of the most similar images within each modality for the characterization of intra-modal similarities. Experimental results show the effectiveness of the proposed method compared to state-of-the-art methods. The code of the proposed method is publicly available at https://git.tu-berlin.de/rsim/SS-CM-RSIR.
Data-Driven Decision Making in COVID-19 Response: A Survey
Venue: arXiv cs.SI 2022
Year: 2022

COVID-19 has spread all over the world, having an enormous effect on our daily life and work. In response to the epidemic, a lot of important decisions need to be taken to save communities and economies worldwide. Data clearly plays a vital role in effective decision making. Data-driven decision making uses data related evidence and insights to guide the decision making process and to verify the plan of action before it is committed. To better handle the epidemic, governments and policy making institutes have investigated abundant data originating from COVID-19. These data include those related to medicine, knowledge, media, etc. Based on these data, many prevention and control policies are made. In this survey paper, we summarize the progress of data-driven decision making in the response to COVID-19, including COVID-19 prevention and control, psychological counselling, financial aid, work resumption, and school re-opening. We also propose some current challenges and open issues in data-driven decision making, including data collection and quality, complex data analysis, and fairness in decision making. This survey paper sheds light on current policy making driven by data, which also provides a feasible direction for further scientific research.
Thermal hand image segmentation for biometric recognition
Venue: arXiv cs.CV 2022
Year: 2022

In this paper we present a method to identify people by means of thermal (TH) and visible (VIS) hand images acquired simultaneously with a TESTO 882-3 camera. In addition, we also present a new database specially acquired for this work. The real challenge when dealing with TH images is the cold finger areas, which can be confused with the acquisition surface. This problem is solved by taking advantage of the VIS information. We have performed different tests to show how TH and VIS images work in identification problems. Experimental results reveal that TH hand image is as suitable for biometric recognition systems as VIS hand images, and better results are obtained when combining this information. A Biometric Dispersion Matcher has been used as a feature vector dimensionality reduction technique as well as a classification task. Its selection criteria helps to reduce the length of the vectors used to perform identification up to a hundred measurements. Identification rates reach a maximum value of 98.3% under these conditions, when using a database of 104 people.
Reconstruction Task Finds Universal Winning Tickets
Venue: arXiv cs.CV 2022
Year: 2022

Pruning well-trained neural networks is effective to achieve a promising accuracy-efficiency trade-off in computer vision regimes. However, most of existing pruning algorithms only focus on the classification task defined on the source domain. Different from the strong transferability of the original model, a pruned network is hard to transfer to complicated downstream tasks such as object detection arXiv:arch-ive/2012.04643. In this paper, we show that the image-level pretrain task is not capable of pruning models for diverse downstream tasks. To mitigate this problem, we introduce image reconstruction, a pixel-level task, into the traditional pruning framework. Concretely, an autoencoder is trained based on the original model, and then the pruning process is optimized with both autoencoder and classification losses. The empirical study on benchmark downstream tasks shows that the proposed method can outperform state-of-the-art results explicitly.
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search
Venue: arXiv cs.LG 2022
Year: 2022

Neural networks often encounter various stringent resource constraints while deploying on edge devices. To tackle these problems with less human efforts, automated machine learning becomes popular in finding various neural architectures that fit diverse Artificial Intelligence of Things (AIoT) scenarios. Recently, to prevent the leakage of private information while enable automated machine intelligence, there is an emerging trend to integrate federated learning and neural architecture search (NAS). Although promising as it may seem, the coupling of difficulties from both tenets makes the algorithm development quite challenging. In particular, how to efficiently search the optimal neural architecture directly from massive non-independent and identically distributed (non-IID) data among AIoT devices in a federated manner is a hard nut to crack. In this paper, to tackle this challenge, by leveraging the advances in ProxylessNAS, we propose a Federated Direct Neural Architecture Search (FDNAS) framework that allows for hardware-friendly NAS from non- IID data across devices. To further adapt to both various data distributions and different types of devices with heterogeneous embedded hardware platforms, inspired by meta-learning, a Cluster Federated Direct Neural Architecture Search (CFDNAS) framework is proposed to achieve device-aware NAS, in the sense that each device can learn a tailored deep learning model for its particular data distribution and hardware constraint. Extensive experiments on non-IID datasets have shown the state-of-the-art accuracy-efficiency trade-offs achieved by the proposed solution in the presence of both data and device heterogeneity.
A Survey on Technological Trends to Enhance Spectrum Efficiency in 6G Communications
Venue: arXiv cs.NI 2022
Year: 2022

The research community has already identified that, by 2030, 5G networks will reach the capacity limits, and hence, will be inadequate to support next generation bandwidth-hungry, ubiquitous, intelligent services, and applications. Therefore, in view of sustaining the competitive edge of wireless technology and stratifying the next decade's communication requirements both, industry and research community have already begun conceptualizing the 6G technology. This article presents a detailed survey on the recent technological trends which address the capacity issues and enhance the spectrum-efficiency in 6G Communications. We present these trends in detail and then identify the challenges that need solutions before the practical deployment to realize 6G communications. Our survey article attempts to significantly contribute to initiating future research directions in the area of spectrum-efficiency in 6G communications.