OpenRaiser/Intern-Atlas huggingface dataset:
AMDet: A Tool for Mitotic Cell Detection in Histopathology Slides
Venue: arXiv cs.CV 2021
Year: 2021

Breast Cancer is the most prevalent cancer in the world. The World Health Organization reports that the disease still affects a significant portion of the developing world citing increased mortality rates in the majority of low to middle income countries. The most popular protocol pathologists use for diagnosing breast cancer is the Nottingham grading system which grades the proliferation of tumors based on 3 major criteria, the most important of them being mitotic cell count. The way in which pathologists evaluate mitotic cell count is to subjectively and qualitatively analyze cells present in stained slides of tissue and make a decision on its mitotic state i.e. is it mitotic or not? This process is extremely inefficient and tiring for pathologists and so an efficient, accurate, and fully automated tool to aid with the diagnosis is extremely desirable. Fortunately, creating such a tool is made significantly easier with the AutoML tool available from Microsoft Azure, however to the best of our knowledge the AutoML tool has never been formally evaluated for use in mitotic cell detection in histopathology images. This paper serves as an evaluation of the AutoML tool for this purpose and will provide a first look on how the tool handles this challenging problem. All code is available athttps://github.com/WaltAFWilliams/AMDet
Visual Analysis of Hyperproperties for Understanding Model Checking Results
Venue: arXiv cs.HC 2021
Year: 2021

Model checkers provide algorithms for proving that a mathematical model of a system satisfies a given specification. In case of a violation, a counterexample that shows the erroneous behavior is returned. Understanding these counterexamples is challenging, especially for hyperproperty specifications, i.e., specifications that relate multiple executions of a system to each other. We aim to facilitate the visual analysis of such counterexamples through our HyperVis tool, which provides interactive visualizations of the given model, specification, and counterexample. Within an iterative and interdisciplinary design process, we developed visualization solutions that can effectively communicate the core aspects of the model checking result. Specifically, we introduce graphical representations of binary values for improving pattern recognition, color encoding for better indicating related aspects, visually enhanced textual descriptions, as well as extensive cross-view highlighting mechanisms. Further, through an underlying causal analysis of the counterexample, we are also able to identify values that contributed to the violation and use this knowledge for both improved encoding and highlighting. Finally, the analyst can modify both the specification of the hyperproperty and the system directly within HyperVis and initiate the model checking of the new version. In combination, these features notably support the analyst in understanding the error leading to the counterexample as well as iterating the provided system and specification. We ran multiple case studies with HyperVis and tested it with domain experts in qualitative feedback sessions. The participants' positive feedback confirms the considerable improvement over the manual, text-based status quo and the value of the tool for explaining hyperproperties.
On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization
Venue: arXiv cs.LG 2021
Year: 2021

Combinatorial optimization problems (COPs) on the graph with real-life applications are canonical challenges in Computer Science. The difficulty of finding quality labels for problem instances holds back leveraging supervised learning across combinatorial problems. Reinforcement learning (RL) algorithms have recently been adopted to solve this challenge automatically. The underlying principle of this approach is to deploy a graph neural network (GNN) for encoding both the local information of the nodes and the graph-structured data in order to capture the current state of the environment. Then, it is followed by the actor to learn the problem-specific heuristics on its own and make an informed decision at each state for finally reaching a good solution. Recent studies on this subject mainly focus on a family of combinatorial problems on the graph, such as the travel salesman problem, where the proposed model aims to find an ordering of vertices that optimizes a given objective function. We use the security-aware phone clone allocation in the cloud as a classical quadratic assignment problem (QAP) to investigate whether or not deep RL-based model is generally applicable to solve other classes of such hard problems. Extensive empirical evaluation shows that existing RL-based model may not generalize to QAP.
Performance Evaluation of MU-MIMO Under the Impact of Open Loop Traffic Dynamics
Venue: arXiv cs.NI 2021
Year: 2021

Multi-user MIMO enhances throughput by simultaneously transmitting/receiving parallel data streams to/from a group of users. However, the throughput analysis does not ac-count for variable data traffic. The research objective of this project is to analyze the effect of variable traffic as it will change the system behavior. The two characteristic features of variable traffic viz. packet size variation and traffic burstiness have been considered for the analysis in this project. Their effects have been studied individually to provide an insight into the problem. Through simulations, we show that in certain scenarios the system performance of MU-MIMO deteriorates significantly due to both packet size variations and traffic burstiness individually even under ideal conditions with respect to channel variations, channel correlation, mobility, etc. Furthermore, we show that under bursty traffic with higher offered load the aggregate throughput first re-mains steady until a certain peak to average rate ratio and then deteriorates linearly instead of exponentially. Also, un-der high amount of traffic burstiness, the throughputs are in-dependent of the aggregation rates. A thorough analysis is provided to explain these two phenomena. This is the first research work that considers the impact of variable traffic on the performance of MU-MIMO. Therefore, the implications for MAC protocol design are significant.
Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching
Venue: arXiv cs.CV 2021
Year: 2021

Learning to generate 3D point clouds without 3D supervision is an important but challenging problem. Current solutions leverage various differentiable renderers to project the generated 3D point clouds onto a 2D image plane, and train deep neural networks using the per-pixel difference with 2D ground truth images. However, these solutions are still struggling to fully recover fine structures of 3D shapes, such as thin tubes or planes. To resolve this issue, we propose an unsupervised approach for 3D point cloud generation with fine structures. Specifically, we cast 3D point cloud learning as a 2D projection matching problem. Rather than using entire 2D silhouette images as a regular pixel supervision, we introduce structure adaptive sampling to randomly sample 2D points within the silhouettes as an irregular point supervision, which alleviates the consistency issue of sampling from different view angles. Our method pushes the neural network to generate a 3D point cloud whose 2D projections match the irregular point supervision from different view angles. Our 2D projection matching approach enables the neural network to learn more accurate structure information than using the per-pixel difference, especially for fine and thin 3D structures. Our method can recover fine 3D structures from 2D silhouette images at different resolutions, and is robust to different sampling methods and point number in irregular point supervision. Our method outperforms others under widely used benchmarks. Our code, data and models are available at https://github.com/chenchao15/2D\_projection\_matching.
Velocity-adaptive Access Scheme for MEC-assisted Platooning Networks: Access Fairness Via Data Freshness
Venue: arXiv cs.NI 2021
Year: 2021

Platooning strategy is an important part of autonomous driving technology. Due to the limited resource of autonomous vehicles in platoons, mobile edge computing (MEC) is usually used to assist vehicles in platoons to obtain useful information, increasing its safety. Specifically, vehicles usually adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit large amount of data to the base station (BS) through vehicle-to-infrastructure (V2I) communications, where the useful information can be extracted by the edge server connected to the BS and then sent back to the vehicles to make correct decisions in time. However, vehicles may be moving on different lanes with different velocities, which incurs the unfair access due to the characteristics of platoons, i.e., vehicles on different lanes transmit different amount of data to the BS when they pass through the coverage of the BS, which also results in the different amount of useful information received by various vehicles. Moreover, age of information (AoI) is an important performance metric to measure the freshness of the data. Large average age of data implies not receiving the useful information in time. It is necessary to design an access scheme to jointly optimize the fairness and data freshness. In this paper, we formulate a joint optimization problem in the MEC-assisted V2I networks and present a multi-objective optimization scheme to solve the problem through adjusting the minimum contention window under the IEEE 802.11 DCF mode according to the velocities of vehicles. The effectiveness of the scheme has been demonstrated by simulation.
SPECT Angle Interpolation Based on Deep Learning Methodologies
Venue: arXiv cs.AI 2021
Year: 2021

A novel method for SPECT angle interpolation based on deep learning methodologies is presented. Projection data from software phantoms were used to train the proposed model. For evaluation of the efficacy of the method, phantoms based on Shepp Logan, with various noise levels added were used, and the resulting interpolated sinograms are reconstructed using Ordered Subset Expectation Maximization (OSEM) and compared to the reconstructions of the original sinograms. The proposed method can quadruple the projections, and denoise the original sinogram, in the same process. As the results show, the proposed model significantly improves the reconstruction accuracy. Finally, to demonstrate the efficacy and capability of the proposed method results from real-world DAT-SPECT sinograms are presented.
"What makes my queries slow?": Subgroup Discovery for SQL Workload Analysis
Venue: arXiv cs.AI 2021
Year: 2021

Among daily tasks of database administrators (DBAs), the analysis of query workloads to identify schema issues and improving performances is crucial. Although DBAs can easily pinpoint queries repeatedly causing performance issues, it remains challenging to automatically identify subsets of queries that share some properties only (a pattern) and simultaneously foster some target measures, such as execution time. Patterns are defined on combinations of query clauses, environment variables, database alerts and metrics and help answer questions like what makes SQL queries slow? What makes I/O communications high? Automatically discovering these patterns in a huge search space and providing them as hypotheses for helping to localize issues and root-causes is important in the context of explainable AI. To tackle it, we introduce an original approach rooted on Subgroup Discovery. We show how to instantiate and develop this generic data-mining framework to identify potential causes of SQL workloads issues. We believe that such data-mining technique is not trivial to apply for DBAs. As such, we also provide a visualization tool for interactive knowledge discovery. We analyse a one week workload from hundreds of databases from our company, make both the dataset and source code available, and experimentally show that insightful hypotheses can be discovered.
A Neural Approach for Detecting Morphological Analogies
Venue: arXiv cs.CL 2021
Year: 2021

Analogical proportions are statements of the form "A is to B as C is to D" that are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). For instance, there are analogy based approaches to semantics as well as to morphology. In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e.g., the axiomatic approach as well as that based on Kolmogorov complexity. In this paper, we propose a deep learning approach to detect morphological analogies, for instance, with reinflexion or conjugation. We present empirical results that show that our framework is competitive with the above-mentioned state of the art symbolic approaches. We also explore empirically its transferability capacity across languages, which highlights interesting similarities between them.
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces
Venue: arXiv cs.LG 2021
Year: 2021

Multi-agent control problems constitute an interesting area of application for deep reinforcement learning models with continuous action spaces. Such real-world applications, however, typically come with critical safety constraints that must not be violated. In order to ensure safety, we enhance the well-known multi-agent deep deterministic policy gradient (MADDPG) framework by adding a safety layer to the deep policy network. In particular, we extend the idea of linearizing the single-step transition dynamics, as was done for single-agent systems in Safe DDPG (Dalal et al., 2018), to multi-agent settings. We additionally propose to circumvent infeasibility problems in the action correction step using soft constraints (Kerrigan & Maciejowski, 2000). Results from the theory of exact penalty functions can be used to guarantee constraint satisfaction of the soft constraints under mild assumptions. We empirically find that the soft formulation achieves a dramatic decrease in constraint violations, making safety available even during the learning procedure.
A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
Venue: arXiv cs.LG 2021
Year: 2021

Heterogeneous Information Network (HIN) embedding refers to the low-dimensional projections of the HIN nodes that preserve the HIN structure and semantics. HIN embedding has emerged as a promising research field for network analysis as it enables downstream tasks such as clustering and node classification. In this work, we propose \ours for joint learning of cluster embeddings as well as cluster-aware HIN embedding. We assume that the connected nodes are highly likely to fall in the same cluster, and adopt a variational approach to preserve the information in the pairwise relations in a cluster-aware manner. In addition, we deploy contrastive modules to simultaneously utilize the information in multiple meta-paths, thereby alleviating the meta-path selection problem - a challenge faced by many of the famous HIN embedding approaches. The HIN embedding, thus learned, not only improves the clustering performance but also preserves pairwise proximity as well as the high-order HIN structure. We show the effectiveness of our approach by comparing it with many competitive baselines on three real-world datasets on clustering and downstream node classification.
Not quite there yet: Combining analogical patterns and encoder-decoder networks for cognitively plausible inflection
Venue: arXiv cs.CL 2021
Year: 2021

The paper presents four models submitted to Part 2 of the SIGMORPHON 2021 Shared Task 0, which aims at replicating human judgements on the inflection of nonce lexemes. Our goal is to explore the usefulness of combining pre-compiled analogical patterns with an encoder-decoder architecture. Two models are designed using such patterns either in the input or the output of the network. Two extra models controlled for the role of raw similarity of nonce inflected forms to existing inflected forms in the same paradigm cell, and the role of the type frequency of analogical patterns. Our strategy is entirely endogenous in the sense that the models appealing solely to the data provided by the SIGMORPHON organisers, without using external resources. Our model 2 ranks second among all submitted systems, suggesting that the inclusion of analogical patterns in the network architecture is useful in mimicking speakers' predictions.
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset
Venue: arXiv cs.CL 2021
Year: 2021

An important part when constructing multiple-choice questions (MCQs) for reading comprehension assessment are the distractors, the incorrect but preferably plausible answer options. In this paper, we present a new BERT-based method for automatically generating distractors using only a small-scale dataset. We also release a new such dataset of Swedish MCQs (used for training the model), and propose a methodology for assessing the generated distractors. Evaluation shows that from a student's perspective, our method generated one or more plausible distractors for more than 50% of the MCQs in our test set. From a teacher's perspective, about 50% of the generated distractors were deemed appropriate. We also do a thorough analysis of the results.
Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning
Venue: arXiv cs.AI 2021
Year: 2021

Nowadays, artificial neural networks are widely used for users' online travel planning. Personalized travel planning has many real applications and is affected by various factors, such as transportation type, intention destination estimation, budget limit and crowdness prediction. Among those factors, users' intention destination prediction is an essential task in online travel platforms. The reason is that, the user may be interested in the travel plan only when the plan matches his real intention destination. Therefore, in this paper, we focus on predicting users' intention destinations in online travel platforms. In detail, we act as online travel platforms (such as Fliggy and Airbnb) to recommend travel plans for users, and the plan consists of various vacation items including hotel package, scenic packages and so on. Predicting the actual intention destination in travel planning is challenging. Firstly, users' intention destination is highly related to their travel status (e.g., planning for a trip or finishing a trip). Secondly, users' actions (e.g. clicking, searching) over different product types (e.g. train tickets, visa application) have different indications in destination prediction. Thirdly, users may mostly visit the travel platforms just before public holidays, and thus user behaviors in online travel platforms are more sparse, low-frequency and long-period. Therefore, we propose a Deep Multi-Sequences fused neural Networks (DMSN) to predict intention destinations from fused multi-behavior sequences. Real datasets are used to evaluate the performance of our proposed DMSN models. Experimental results indicate that the proposed DMSN models can achieve high intention destination prediction accuracy.
FOLASP: FO(.) as Input Language for Answer Ser Solvers
Venue: arXiv cs.AI 2021
Year: 2021

Over the past decades, Answer Set Programming (ASP) has emerged as an important paradigm for declarative problem solving. Technological progress in this area has been stimulated by the use of common standards, such as the ASP-Core-2 language. While ASP has its roots in non-monotonic reasoning, efforts have also been made to reconcile ASP with classical first-order logic (FO). This has resulted in the development of FO(.), an expressive extension of FO, which allows ASP-like problem solving in a purely classical setting. This language may be more accessible to domain experts already familiar with FO, and may be easier to combine with other formalisms that are based on classical logic. It is supported by the IDP inference system, which has successfully competed in a number of ASP competitions. Here, however, technological progress has been hampered by the limited number of systems that are available for FO(.). In this paper, we aim to address this gap by means of a translation tool that transforms an FO(.) specification into ASP-Core-2, thereby allowing ASP-Core-2 solvers to be used as solvers for FO(.) as well. We present experimental results to show that the resulting combination of our translation with an off-the-shelf ASP solver is competitive with the IDP system as a way of solving problems formulated in FO(.). Under consideration for acceptance in TPLP.
Understanding Tool Synthesis Behavior and Safe Finite State Machine Design
Venue: arXiv cs.AR 2021
Year: 2021

High-reliability design requires understanding synthesis tool behavior and best practices. Detection and protection against illegal states and transitions is important for critical Finite State Machines (FSMs) within high reliability applications. Single Event Upsets (SEUs) probability is increasing with decreasing circuit dimensions and voltage [1]. SEU handling must be analyzed post optimization to ensure designed protections are still functional. In this work the default behavior of three synthesis tools interacting with high reliability FSMs is discussed. Post-synthesis netlists of test FSMs are analyzed for optimization induced changes that affect reliability during a SEU. Best practices are proposed to curtail aggressive optimizers.
Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models
Venue: arXiv cs.CL 2021
Year: 2021

Open-domain extractive question answering works well on textual data by first retrieving candidate texts and then extracting the answer from those candidates. However, some questions cannot be answered by text alone but require information stored in tables. In this paper, we present an approach for retrieving both texts and tables relevant to a question by jointly encoding texts, tables and questions into a single vector space. To this end, we create a new multi-modal dataset based on text and table datasets from related work and compare the retrieval performance of different encoding schemata. We find that dense vector embeddings of transformer models outperform sparse embeddings on four out of six evaluation datasets. Comparing different dense embedding models, tri-encoders with one encoder for each question, text and table, increase retrieval performance compared to bi-encoders with one encoder for the question and one for both text and tables. We release the newly created multi-modal dataset to the community so that it can be used for training and evaluation.