OpenRaiser/Intern-Atlas huggingface dataset:
Space-Time Codes from Sum-Rank Codes
Venue: arXiv cs.IT 2021
Year: 2021

Just as rank-metric or Gabidulin codes may be used to construct rate-diversity tradeoff optimal space-time codes, a recently introduced generalization for the sum-rank metric -- linearized Reed-Solomon codes -- accomplishes the same in the case of multiple fading blocks. In this paper, we provide the first explicit construction of minimal delay rate-diversity optimal multiblock space-time codes as an application of linearized Reed-Solomon codes. We also provide sequential decoders for these codes and, more generally, space-time codes constructed from finite field codes. Simulation results show that the proposed codes can outperform full diversity codes based on cyclic division algebras at low SNRs as well as utilize significantly smaller constellations.
Finding the Sweet Spot for Frame Aggregation in 802.11 WLANs
Venue: arXiv cs.NI 2021
Year: 2021

This letter proposes an algorithm for the dynamic tuning of the maximum size of aggregated frames in 802.11 WLANs. Traffic flows with opposed requirements may coexist in these networks: traditional services as web browsing or file download that need high throughput, and services with realtime requirements that need low latency. The proposed algorithm allows the network manager to find an optimal balance (i.e. the "sweet spot" between throughput and latency: a "delay budget" can be assigned to real-time flows, with the objective of keeping the latency as close as possible to that budget, while penalizing the throughput of traditional services as little as possible.
Fast and Effective Biomedical Entity Linking Using a Dual Encoder
Venue: arXiv cs.CL 2021
Year: 2021

Biomedical entity linking is the task of identifying mentions of biomedical concepts in text documents and mapping them to canonical entities in a target thesaurus. Recent advancements in entity linking using BERT-based models follow a retrieve and rerank paradigm, where the candidate entities are first selected using a retriever model, and then the retrieved candidates are ranked by a reranker model. While this paradigm produces state-of-the-art results, they are slow both at training and test time as they can process only one mention at a time. To mitigate these issues, we propose a BERT-based dual encoder model that resolves multiple mentions in a document in one shot. We show that our proposed model is multiple times faster than existing BERT-based models while being competitive in accuracy for biomedical entity linking. Additionally, we modify our dual encoder model for end-to-end biomedical entity linking that performs both mention span detection and entity disambiguation and out-performs two recently proposed models.
Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data
Venue: arXiv cs.SI 2021
Year: 2021

Higher-order network analysis uses the ideas of hypergraphs, simplicial complexes, multilinear and tensor algebra, and more, to study complex systems. These are by now well established mathematical abstractions. What's new is that the ideas can be tested and refined on the type of large-scale data arising in today's digital world. This research area therefore is making an impact across many applications. Here, we provide a brief history, guide, and survey.
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
Venue: arXiv cs.LG 2021
Year: 2021

We study a family of algorithms, which we refer to as local update methods, generalizing many federated and meta-learning algorithms. We prove that for quadratic models, local update methods are equivalent to first-order optimization on a surrogate loss we exactly characterize. Moreover, fundamental algorithmic choices (such as learning rates) explicitly govern a trade-off between the condition number of the surrogate loss and its alignment with the true loss. We derive novel convergence rates showcasing these trade-offs and highlight their importance in communication-limited settings. Using these insights, we are able to compare local update methods based on their convergence/accuracy trade-off, not just their convergence to critical points of the empirical loss. Our results shed new light on a broad range of phenomena, including the efficacy of server momentum in federated learning and the impact of proximal client updates.
Text Simplification by Tagging
Venue: arXiv cs.CL 2021
Year: 2021

Edit-based approaches have recently shown promising results on multiple monolingual sequence transduction tasks. In contrast to conventional sequence-to-sequence (Seq2Seq) models, which learn to generate text from scratch as they are trained on parallel corpora, these methods have proven to be much more effective since they are able to learn to make fast and accurate transformations while leveraging powerful pre-trained language models. Inspired by these ideas, we present TST, a simple and efficient Text Simplification system based on sequence Tagging, leveraging pre-trained Transformer-based encoders. Our system makes simplistic data augmentations and tweaks in training and inference on a pre-existing system, which makes it less reliant on large amounts of parallel training data, provides more control over the outputs and enables faster inference speeds. Our best model achieves near state-of-the-art performance on benchmark test datasets for the task. Since it is fully non-autoregressive, it achieves faster inference speeds by over 11 times than the current state-of-the-art text simplification system.
Learning Hierarchical Integration of Foveal and Peripheral Vision for Vergence Control by Active Efficient Coding
Venue: arXiv cs.CV 2021
Year: 2021

The active efficient coding (AEC) framework parsimoniously explains the joint development of visual processing and eye movements, e.g., the emergence of binocular disparity selective neurons and fusional vergence, the disjunctive eye movements that align left and right eye images. Vergence can be driven by information in both the fovea and periphery, which play complementary roles. The high resolution fovea can drive precise short range movements. The lower resolution periphery supports coarser long range movements. The fovea and periphery may also contain conflicting information, e.g. due to objects at different depths. While past AEC models did integrate peripheral and foveal information, they did not explicitly take into account these characteristics. We propose here a two-level hierarchical approach that does. The bottom level generates different vergence actions from foveal and peripheral regions. The top level selects one. We demonstrate that the hierarchical approach performs better than prior approaches in realistic environments, exhibiting better alignment and less oscillation.
New Methods for Detecting Concentric Objects With High Accuracy
Venue: arXiv cs.CV 2021
Year: 2021

Fitting concentric geometric objects to digitized data is an important problem in many areas such as iris detection, autonomous navigation, and industrial robotics operations. There are two common approaches to fitting geometric shapes to data: the geometric (iterative) approach and algebraic (non-iterative) approach. The geometric approach is a nonlinear iterative method that minimizes the sum of the squares of Euclidean distances of the observed points to the ellipses and regarded as the most accurate method, but it needs a good initial guess to improve the convergence rate. The algebraic approach is based on minimizing the algebraic distances with some constraints imposed on parametric space. Each algebraic method depends on the imposed constraint, and it can be solved with the aid of the generalized eigenvalue problem. Only a few methods in literature were developed to solve the problem of concentric ellipses. Here we study the statistical properties of existing methods by firstly establishing a general mathematical and statistical framework for this problem. Using rigorous perturbation analysis, we derive the variances and biasedness of each method under the small-sigma model. We also develop new estimators, which can be used as reliable initial guesses for other iterative methods. Then we compare the performance of each method according to their theoretical accuracy. Not only do our methods described here outperform other existing non-iterative methods, they are also quite robust against large noise. These methods and their practical performances are assessed by a series of numerical experiments on both synthetic and real data.
Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Data
Venue: arXiv cs.CV 2021
Year: 2021

Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response. Here, the challenge lies in the multimodal nature of urban big data. We propose a compact neural ensemble model to alleviate overfitting in fusing multimodal features and develop some new features such as fractal measure of road complexity in satellite images, taxi flows, POIs, and road width and connectivity in OpenStreetMap. The solution is more promising in performance than the baseline methods and the single-modality data based solutions. After visualization from a micro view, the visual patterns of the scenes related to high and low risk are revealed, providing lessons for future road design. From city point of view, the predicted risk map is close to the ground truth, and can act as the base in optimizing spatial configuration of resources for emergency response, and alarming signs. To the best of our knowledge, it is the first work to fuse visual and spatio-temporal features in traffic accident prediction while advances to bridge the gap between data mining based urban computing and computer vision based urban perception.
Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis
Venue: arXiv cs.CV 2021
Year: 2021

Early detection of skin cancers like melanoma is crucial to ensure high chances of survival for patients. Clinical application of Deep Learning (DL)-based Decision Support Systems (DSS) for skin cancer screening has the potential to improve the quality of patient care. The majority of work in the medical AI community focuses on a diagnosis setting that is mainly relevant for autonomous operation. Practical decision support should, however, go beyond plain diagnosis and provide explanations. This paper provides an overview of works towards explainable, DL-based decision support in medical applications with the example of skin cancer diagnosis from clinical, dermoscopic and histopathologic images. Analysis reveals that comparably little attention is payed to the explanation of histopathologic skin images and that current work is dominated by visual relevance maps as well as dermoscopic feature identification. We conclude that future work should focus on meeting the stakeholder's cognitive concepts, providing exhaustive explanations that combine global and local approaches and leverage diverse modalities. Moreover, the possibility to intervene and guide models in case of misbehaviour is identified as a major step towards successful deployment of AI as DL-based DSS and beyond.
Model Complexity of Deep Learning: A Survey
Venue: arXiv cs.LG 2021
Year: 2021

Model complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complexity of deep learning can be categorized into expressive capacity and effective model complexity. We review the existing studies on those two categories along four important factors, including model framework, model size, optimization process and data complexity. We also discuss the applications of deep learning model complexity including understanding model generalization, model optimization, and model selection and design. We conclude by proposing several interesting future directions.
A Topological Approach to Compare Document Semantics Based on a New Variant of Syntactic N-grams
Venue: arXiv cs.CL 2021
Year: 2021

This paper delivers a new perspective of thinking and utilizing syntactic n-grams (sn-grams). Sn-grams are a type of non-linear n-grams which have been playing a critical role in many NLP tasks. Introducing sn-grams to comparing document semantics thus is an appealing application, and few studies have reported progress at this. However, when proceeding on this application, we found three major issues of sn-grams: lack of significance, being sensitive to word orders and failing on capture indirect syntactic relations. To address these issues, we propose a new variant of sn-grams named generalized phrases (GPs). Then based on GPs we propose a topological approach, named DSCoH, to compute document semantic similarities. DSCoH has been extensively tested on the document semantics comparison and the document clustering tasks. The experimental results show that DSCoH can outperform state-of-the-art embedding-based methods.
Explanations in Autonomous Driving: A Survey
Venue: arXiv cs.HC 2021
Year: 2021

The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environments and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs' behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have 'seen', done, and might do in environments in which they operate. In this paper, we provide a comprehensive survey of the existing body of work around explainable autonomous driving. First, we open with a motivation for explanations by highlighting and emphasising the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their explanation requirements for AV. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, control, and system management). Finally, we identify pertinent challenges and provide recommendations, such as a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explainability in AVs.
A Cascaded Multi-IRSs Beamforming Method in mmWave Communication Systems
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we study how to jointly design the phase shift of cascaded multi-IRSs and the precoding vector of the BS to improve the coverage in dense urban areas. We aim to maximize the signal-to-noise ratio (SNR) of the user equipment (UE) received signal by employing this method. However, it is a constrained non-convex optimization problem and is NP-hard. In order to solve this problem, we simplify it by utilizing the characteristic of the mmWave wireless system to decompose the optimization problem into multiple sub-optimization problems. By employing the asymptotic orthogonality of wireless channel in mmWave system to solve the sub-optimization problems, we finally yield a closed-form sub-optimal solution. The simulation results verify that our solution can effectively improve the coverage of deep dense urban areas.
Uncertainty-aware Incremental Learning for Multi-organ Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Most existing approaches to train a unified multi-organ segmentation model from several single-organ datasets require simultaneously access multiple datasets during training. In the real scenarios, due to privacy and ethics concerns, the training data of the organs of interest may not be publicly available. To this end, we investigate a data-free incremental organ segmentation scenario and propose a novel incremental training framework to solve it. We use the pretrained model instead of its own training data for privacy protection. Specifically, given a pretrained $K$ organ segmentation model and a new single-organ dataset, we train a unified $K+1$ organ segmentation model without accessing any data belonging to the previous training stages. Our approach consists of two parts: the background label alignment strategy and the uncertainty-aware guidance strategy. The first part is used for knowledge transfer from the pretained model to the training model. The second part is used to extract the uncertainty information from the pretrained model to guide the whole knowledge transfer process. By combing these two strategies, more reliable information is extracted from the pretrained model without original training data. Experiments on multiple publicly available pretrained models and a multi-organ dataset MOBA have demonstrated the effectiveness of our framework.
Fast and Efficient Bit-Level Precision Tuning
Venue: arXiv cs.PL 2021
Year: 2021

In this article, we introduce a new technique for precision tuning. This problem consists of finding the least data types for numerical values such that the result of the computation satisfies some accuracy requirement. State of the art techniques for precision tuning use a try and fail approach. They change the data types of some variables of the program and evaluate the accuracy of the result. Depending on what is obtained, they change more or less data types and repeat the process. Our technique is radically different. Based on semantic equations, we generate an Integer Linear Problem (ILP) from the program source code. Basically, this is done by reasoning on the most significant bit and the number of significant bits of the values which are integer quantities. The integer solution to this problem, computed in polynomial time by a (real) linear programming solver, gives the optimal data types at the bit level. A finer set of semantic equations is also proposed which does not reduce directly to an ILP problem. So we use policy iteration to find the solution. Both techniques have been implemented and we show that our results encompass the results of state of the art tools.
A deep learning based known plaintext attack method for chaotic cryptosystem
Venue: arXiv cs.CR 2021
Year: 2021

In this paper, we propose a known-plaintext attack (KPA) method based on deep learning for traditional chaotic encryption scheme. We employ the convolutional neural network to learn the operation mechanism of chaotic cryptosystem, and accept the trained network as the final decryption system. To evaluate the attack performance of different networks on different chaotic cryptosystem, we adopt two neural networks to perform known-plaintext attacks on two distinct chaotic encryption schemes. The experimental results demonstrate the potential of deep learning-based method for known-plaintext attack against chaotic cryptosystem. Different from the previous known-plaintext attack methods, which were usually limited to a specific chaotic cryptosystem, a neural network can be applied to the cryptanalysis of various chaotic cryptosystems with deep learning-based approach, while several different networks can be designed for the cryptanalysis of chaotic cryptosystems. This paper provides a new idea for the cryptanalysis of chaotic image encryption algorithm.
Will it Crease or Cease? A study of Debulking of Air Pockets in Automated Prepreg Composite Layup
Venue: arXiv cs.CE 2021
Year: 2021

Automatic draping of carbon-fiber prepreg plies for the aerospace industry is a promising technique for lowering the manufacturing costs and to this end, a thorough in-process quality control is crucial. In this paper, out-of-plane defects in the layup are investigated. After draping, air pockets are occasionally encountered. The question is, if such apparent defects can be mitigated sufficiently during vacuum debulking. The 3D topology is measured by means of a structured-light 3D scanner and air pockets are segmented. An approximate mass-spring ply model is used to study the behavior of the air pockets during application of vacuum pressure. The model is computationally fast and will indicate online whether the air pocket will be removed or manual intervention is required. Upon comparing the model predictions with experimental data, it is shown that the system is capable of correctly predicting 13 out of 14 air pockets in a test layup.
DISC: A Dynamic Shape Compiler for Machine Learning Workloads
Venue: arXiv cs.DC 2021
Year: 2021

Many recent machine learning models show dynamic shape characteristics. However, existing AI compiler optimization systems suffer a lot from problems brought by dynamic shape models, including compilation overhead, memory usage, optimization pipeline and deployment complexity. This paper provides a compiler system to natively support optimization for dynamic shape workloads, named DISC. DISC enriches a set of IR to form a fully dynamic shape representation. It generates the runtime flow at compile time to support processing dynamic shape based logic, which avoids the interpretation overhead at runtime and enlarges the opportunity of host-device co-optimization. It addresses the kernel fusion problem of dynamic shapes with shape propagation and constraints collecting methods. This is the first work to demonstrate how to build an end-to-end dynamic shape compiler based on MLIR infrastructure. Experiments show that DISC achieves up to 3.3x speedup than TensorFlow/PyTorch, and 1.8x than Nimble.
BERTese: Learning to Speak to BERT
Venue: arXiv cs.CL 2021
Year: 2021

Large pre-trained language models have been shown to encode large amounts of world and commonsense knowledge in their parameters, leading to substantial interest in methods for extracting that knowledge. In past work, knowledge was extracted by taking manually-authored queries and gathering paraphrases for them using a separate pipeline. In this work, we propose a method for automatically rewriting queries into "BERTese", a paraphrase query that is directly optimized towards better knowledge extraction. To encourage meaningful rewrites, we add auxiliary loss functions that encourage the query to correspond to actual language tokens. We empirically show our approach outperforms competing baselines, obviating the need for complex pipelines. Moreover, BERTese provides some insight into the type of language that helps language models perform knowledge extraction.