OpenRaiser/Intern-Atlas huggingface dataset:
Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles
Venue: arXiv cs.AI 2021
Year: 2021

Sensing and Perception (S&P) is a crucial component of an autonomous system (such as a robot), especially when deployed in highly dynamic environments where it is required to react to unexpected situations. This is particularly true in case of Autonomous Vehicles (AVs) driving on public roads. However, the current evaluation metrics for perception algorithms are typically designed to measure their accuracy per se and do not account for their impact on the decision making subsystem(s). This limitation does not help developers and third party evaluators to answer a critical question: is the performance of a perception subsystem sufficient for the decision making subsystem to make robust, safe decisions? In this paper, we propose a simulation-based methodology towards answering this question. At the same time, we show how to analyze the impact of different kinds of sensing and perception errors on the behavior of the autonomous system.
Generalized Visual Information Analysis via Tensorial Algebra
Venue: arXiv cs.CV 2021
Year: 2021

Higher order data is modeled using matrices whose entries are numerical arrays of a fixed size. These arrays, called t-scalars, form a commutative ring under the convolution product. Matrices with elements in the ring of t-scalars are referred to as t-matrices. The t-matrices can be scaled, added and multiplied in the usual way. There are t-matrix generalizations of positive matrices, orthogonal matrices and Hermitian symmetric matrices. With the t-matrix model, it is possible to generalize many well-known matrix algorithms. In particular, the t-matrices are used to generalize the SVD (Singular Value Decomposition), HOSVD (High Order SVD), PCA (Principal Component Analysis), 2DPCA (Two Dimensional PCA) and GCA (Grassmannian Component Analysis). The generalized t-matrix algorithms, namely TSVD, THOSVD,TPCA, T2DPCA and TGCA, are applied to low-rank approximation, reconstruction,and supervised classification of images. Experiments show that the t-matrix algorithms compare favorably with standard matrix algorithms.
AutoFCL: Automatically Tuning Fully Connected Layers for Handling Small Dataset
Venue: arXiv cs.CV 2021
Year: 2021

Deep Convolutional Neural Networks (CNN) have evolved as popular machine learning models for image classification during the past few years, due to their ability to learn the problem-specific features directly from the input images. The success of deep learning models solicits architecture engineering rather than hand-engineering the features. However, designing state-of-the-art CNN for a given task remains a non-trivial and challenging task, especially when training data size is less. To address this phenomena, transfer learning has been used as a popularly adopted technique. While transferring the learned knowledge from one task to another, fine-tuning with the target-dependent Fully Connected (FC) layers generally produces better results over the target task. In this paper, the proposed AutoFCL model attempts to learn the structure of FC layers of a CNN automatically using Bayesian optimization. To evaluate the performance of the proposed AutoFCL, we utilize five pre-trained CNN models such as VGG-16, ResNet, DenseNet, MobileNet, and NASNetMobile. The experiments are conducted on three benchmark datasets, namely CalTech-101, Oxford-102 Flowers, and UC Merced Land Use datasets. Fine-tuning the newly learned (target-dependent) FC layers leads to state-of-the-art performance, according to the experiments carried out in this research. The proposed AutoFCL method outperforms the existing methods over CalTech-101 and Oxford-102 Flowers datasets by achieving the accuracy of 94.38% and 98.89%, respectively. However, our method achieves comparable performance on the UC Merced Land Use dataset with 96.83% accuracy. The source codes of this research are available at https://github.com/shabbeersh/AutoFCL.
Dialogue-Based Simulation For Cultural Awareness Training
Venue: arXiv cs.CY 2021
Year: 2021

Existing simulations designed for cultural and interpersonal skill training rely on pre-defined responses with a menu option selection interface. Using a multiple-choice interface and restricting trainees' responses may limit the trainees' ability to apply the lessons in real life situations. This systems also uses a simplistic evaluation model, where trainees' selected options are marked as either correct or incorrect. This model may not capture sufficient information that could drive an adaptive feedback mechanism to improve trainees' cultural awareness. This paper describes the design of a dialogue-based simulation for cultural awareness training. The simulation, built around a disaster management scenario involving a joint coalition between the US and the Chinese armies. Trainees were able to engage in realistic dialogue with the Chinese agent. Their responses, at different points, get evaluated by different multi-label classification models. Based on training on our dataset, the models score the trainees' responses for cultural awareness in the Chinese culture. Trainees also get feedback that informs the cultural appropriateness of their responses. The result of this work showed the following; i) A feature-based evaluation model improves the design, modeling and computation of dialogue-based training simulation systems; ii) Output from current automatic speech recognition (ASR) systems gave comparable end results compared with the output from manual transcription; iii) A multi-label classification model trained as a cultural expert gave results which were comparable with scores assigned by human annotators.
Designing GANs: A Likelihood Ratio Approach
Venue: arXiv cs.LG 2021
Year: 2021

We are interested in the design of generative networks. The training of these mathematical structures is mostly performed with the help of adversarial (min-max) optimization problems. We propose a simple methodology for constructing such problems assuring, at the same time, consistency of the corresponding solution. We give characteristic examples developed by our method, some of which can be recognized from other applications, and some are introduced here for the first time. We present a new metric, the likelihood ratio, that can be employed online to examine the convergence and stability during the training of different Generative Adversarial Networks (GANs). Finally, we compare various possibilities by applying them to well-known datasets using neural networks of different configurations and sizes.
Plague Dot Text: Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)
Venue: arXiv cs.CL 2021
Year: 2021

The design of models that govern diseases in population is commonly built on information and data gathered from past outbreaks. However, epidemic outbreaks are never captured in statistical data alone but are communicated by narratives, supported by empirical observations. Outbreak reports discuss correlations between populations, locations and the disease to infer insights into causes, vectors and potential interventions. The problem with these narratives is usually the lack of consistent structure or strong conventions, which prohibit their formal analysis in larger corpora. Our interdisciplinary research investigates more than 100 reports from the third plague pandemic (1894-1952) evaluating ways of building a corpus to extract and structure this narrative information through text mining and manual annotation. In this paper we discuss the progress of our ongoing exploratory project, how we enhance optical character recognition (OCR) methods to improve text capture, our approach to structure the narratives and identify relevant entities in the reports. The structured corpus is made available via Solr enabling search and analysis across the whole collection for future research dedicated, for example, to the identification of concepts. We show preliminary visualisations of the characteristics of causation and differences with respect to gender as a result of syntactic-category-dependent corpus statistics. Our goal is to develop structured accounts of some of the most significant concepts that were used to understand the epidemiology of the third plague pandemic around the globe. The corpus enables researchers to analyse the reports collectively allowing for deep insights into the global epidemiological consideration of plague in the early twentieth century.
Embedding linear codes into self-orthogonal codes and their optimal minimum distances
Venue: arXiv cs.IT 2021
Year: 2021

We obtain a characterization on self-orthogonality for a given binary linear code in terms of the number of column vectors in its generator matrix, which extends the result of Bouyukliev et al. (2006). As an application, we give an algorithmic method to embed a given binary $k$-dimensional linear code $\mathcal{C}$ ($k = 2,3,4$) into a self-orthogonal code of the shortest length which has the same dimension $k$ and minimum distance $d' \ge d(\mathcal{C})$. For $k > 4$, we suggest a recursive method to embed a $k$-dimensional linear code to a self-orthogonal code. We also give new explicit formulas for the minimum distances of optimal self-orthogonal codes for any length $n$ with dimension 4 and any length $n \not\equiv 6,13,14,21,22,28,29 \pmod{31}$ with dimension 5. We determine the exact optimal minimum distances of $[n,4]$ self-orthogonal codes which were left open by Li-Xu-Zhao (2008) when $n \equiv 0,3,4,5,10,11,12 \pmod{15}$. Then, using MAGMA, we observe that our embedding sends an optimal linear code to an optimal self-orthogonal code.
Self-recognition in conversational agents
Venue: arXiv cs.AI 2021
Year: 2021

In a standard Turing test, a machine has to prove its humanness to the judges. By successfully imitating a thinking entity such as a human, this machine then proves that it can also think. Some objections claim that Turing test is not a tool to demonstrate the existence of general intelligence or thinking activity. A compelling alternative is the Lovelace test, in which the agent must originate a product that the agent's creator cannot explain. Therefore, the agent must be the owner of an original product. However, for this to happen the agent must exhibit the idea of self and distinguish oneself from others. Sustaining the idea of self within the Turing test is still possible if the judge decides to act as a textual mirror. Self-recognition tests applied on animals through mirrors appear to be viable tools to demonstrate the existence of a type of general intelligence. Methodology here constructs a textual version of the mirror test by placing the agent as the one and only judge to figure out whether the contacted one is an other, a mimicker, or oneself in an unsupervised manner. This textual version of the mirror test is objective, self-contained, and devoid of humanness. Any agent passing this textual mirror test should have or can acquire a thought mechanism that can be referred to as the inner-voice, answering the original and long lasting question of Turing "Can machines think?" in a constructive manner still within the bounds of the Turing test. Moreover, it is possible that a successful self-recognition might pave way to stronger notions of self-awareness in artificial beings.
A Survey on String Constraint Solving
Venue: arXiv cs.AI 2021
Year: 2021

String constraint solving refers to solving combinatorial problems involving constraints over string variables. String solving approaches have become popular over the last years given the massive use of strings in different application domains like formal analysis, automated testing, database query processing, and cybersecurity. This paper reports a comprehensive survey on string constraint solving by exploring the large number of approaches that have been proposed over the last decades to solve string constraints.
Discovering Mathematical Objects of Interest -- A Study of Mathematical Notations
Venue: arXiv cs.DL 2021
Year: 2021

Mathematical notation, i.e., the writing system used to communicate concepts in mathematics, encodes valuable information for a variety of information search and retrieval systems. Yet, mathematical notations remain mostly unutilized by today's systems. In this paper, we present the first in-depth study on the distributions of mathematical notation in two large scientific corpora: the open access arXiv (2.5B mathematical objects) and the mathematical reviewing service for pure and applied mathematics zbMATH (61M mathematical objects). Our study lays a foundation for future research projects on mathematical information retrieval for large scientific corpora. Further, we demonstrate the relevance of our results to a variety of use-cases. For example, to assist semantic extraction systems, to improve scientific search engines, and to facilitate specialized math recommendation systems. The contributions of our presented research are as follows: (1) we present the first distributional analysis of mathematical formulae on arXiv and zbMATH; (2) we retrieve relevant mathematical objects for given textual search queries (e.g., linking $P_{n}^{(α, β)}\!\left(x\right)$ with `Jacobi polynomial'); (3) we extend zbMATH's search engine by providing relevant mathematical formulae; and (4) we exemplify the applicability of the results by presenting auto-completion for math inputs as the first contribution to math recommendation systems. To expedite future research projects, we have made available our source code and data.
PLLay: Efficient Topological Layer based on Persistence Landscapes
Venue: arXiv cs.LG 2021
Year: 2021

We propose PLLay, a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological features of the input data structure. In this work, we show differentiability with respect to layer inputs, for a general persistent homology with arbitrary filtration. Thus, our proposed layer can be placed anywhere in the network and feed critical information on the topological features of input data into subsequent layers to improve the learnability of the networks toward a given task. A task-optimal structure of PLLay is learned during training via backpropagation, without requiring any input featurization or data preprocessing. We provide a novel adaptation for the DTM function-based filtration, and show that the proposed layer is robust against noise and outliers through a stability analysis. We demonstrate the effectiveness of our approach by classification experiments on various datasets.
On the Estimation of Information Measures of Continuous Distributions
Venue: arXiv cs.IT 2021
Year: 2021

The estimation of information measures of continuous distributions based on samples is a fundamental problem in statistics and machine learning. In this paper, we analyze estimates of differential entropy in $K$-dimensional Euclidean space, computed from a finite number of samples, when the probability density function belongs to a predetermined convex family $\mathcal{P}$. First, estimating differential entropy to any accuracy is shown to be infeasible if the differential entropy of densities in $\mathcal{P}$ is unbounded, clearly showing the necessity of additional assumptions. Subsequently, we investigate sufficient conditions that enable confidence bounds for the estimation of differential entropy. In particular, we provide confidence bounds for simple histogram based estimation of differential entropy from a fixed number of samples, assuming that the probability density function is Lipschitz continuous with known Lipschitz constant and known, bounded support. Our focus is on differential entropy, but we provide examples that show that similar results hold for mutual information and relative entropy as well.
Safe Wasserstein Constrained Deep Q-Learning
Venue: arXiv cs.LG 2021
Year: 2021

This paper presents a distributionally robust Q-Learning algorithm (DrQ) which leverages Wasserstein ambiguity sets to provide idealistic probabilistic out-of-sample safety guarantees during online learning. First, we follow past work by separating the constraint functions from the principal objective to create a hierarchy of machines which estimate the feasible state-action space within the constrained Markov decision process (CMDP). DrQ works within this framework by augmenting constraint costs with tightening offset variables obtained through Wasserstein distributionally robust optimization (DRO). These offset variables correspond to worst-case distributions of modeling error characterized by the TD-errors of the constraint Q-functions. This procedure allows us to safely approach the nominal constraint boundaries. Using a case study of lithium-ion battery fast charging, we explore how idealistic safety guarantees translate to generally improved safety relative to conventional methods.
RDFFrames: Knowledge Graph Access for Machine Learning Tools
Venue: arXiv cs.DB 2021
Year: 2021

Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.
E-APR: Mapping the Effectiveness of Automated Program Repair
Venue: arXiv cs.SE 2021
Year: 2021

Automated Program Repair (APR) is a fast growing area with numerous new techniques being developed to tackle one of the most challenging software engineering problems. APR techniques have shown promising results, giving us hope that one day it will be possible for software to repair itself. In this paper, we focus on the problem of objective performance evaluation of APR techniques. We introduce a new approach, Explaining Automated Program Repair (E-APR), which identifies features of buggy programs that explain why a particular instance is difficult for an APR technique. E-APR is used to examine the diversity and quality of the buggy programs used by most researchers, and analyse the strengths and weaknesses of existing APR techniques. E-APR visualises an instance space of buggy programs, with each buggy program represented as a point in the space. The instance space is constructed to reveal areas of hard and easy buggy programs, and enables the strengths and weaknesses of APR techniques to be identified.
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis
Venue: arXiv cs.LG 2021
Year: 2021

Multi-agent reinforcement learning (MARL), despite its popularity and empirical success, suffers from the curse of dimensionality. This paper builds the mathematical framework to approximate cooperative MARL by a mean-field control (MFC) approach, and shows that the approximation error is of $\mathcal{O}(\frac{1}{\sqrt{N}})$. By establishing an appropriate form of the dynamic programming principle for both the value function and the Q function, it proposes a model-free kernel-based Q-learning algorithm (MFC-K-Q), which is shown to have a linear convergence rate for the MFC problem, the first of its kind in the MARL literature. It further establishes that the convergence rate and the sample complexity of MFC-K-Q are independent of the number of agents $N$, which provides an $\mathcal{O}(\frac{1}{\sqrt{N}})$ approximation to the MARL problem with $N$ agents in the learning environment. Empirical studies for the network traffic congestion problem demonstrate that MFC-K-Q outperforms existing MARL algorithms when $N$ is large, for instance when $N>50$.
Efficient active learning of sparse halfspaces with arbitrary bounded noise
Venue: arXiv cs.LG 2021
Year: 2021

We study active learning of homogeneous $s$-sparse halfspaces in $\mathbb{R}^d$ under the setting where the unlabeled data distribution is isotropic log-concave and each label is flipped with probability at most $η$ for a parameter $η\in \big[0, \frac12\big)$, known as the bounded noise. Even in the presence of mild label noise, i.e. $η$ is a small constant, this is a challenging problem and only recently have label complexity bounds of the form $\tilde{O}\big(s \cdot \mathrm{polylog}(d, \frac{1}ε)\big)$ been established in [Zhang, 2018] for computationally efficient algorithms. In contrast, under high levels of label noise, the label complexity bounds achieved by computationally efficient algorithms are much worse: the best known result of [Awasthi et al., 2016] provides a computationally efficient algorithm with label complexity $\tilde{O}\big((\frac{s \ln d}ε)^{2^{\mathrm{poly}(1/(1-2η))}} \big)$, which is label-efficient only when the noise rate $η$ is a fixed constant. In this work, we substantially improve on it by designing a polynomial time algorithm for active learning of $s$-sparse halfspaces, with a label complexity of $\tilde{O}\big(\frac{s}{(1-2η)^4} \mathrm{polylog} (d, \frac 1 ε) \big)$. This is the first efficient algorithm with label complexity polynomial in $\frac{1}{1-2η}$ in this setting, which is label-efficient even for $η$ arbitrarily close to $\frac12$. Our active learning algorithm and its theoretical guarantees also immediately translate to new state-of-the-art label and sample complexity results for full-dimensional active and passive halfspace learning under arbitrary bounded noise. The key insight of our algorithm and analysis is a new interpretation of online learning regret inequalities, which may be of independent interest.
Uniform Linked Lists Contraction
Venue: arXiv cs.DS 2021
Year: 2021

We present a parallel algorithm (EREW PRAM algorithm) for linked lists contraction. We show that when we contract a linked list from size $n$ to size $n/c$ for a suitable constant $c$ we can pack the linked list into an array of size $n/d$ for a constant $1 < d\leq c$ in the time of 3 coloring the list. Thus for a set of linked lists with a total of $n$ elements and the longest list has $l$ elements our algorithm contracts them in $O(n\log i/p+(\log^{(i)}n+\log i )\log \log l+ \log l)$ time, for an arbitrary constructible integer $i$, with $p$ processors on the EREW PRAM, where $\log^{(1)} n =\log n$ and $\log^{(t)}n=\log \log^{(t-1)} n$ and $\log^*n=\min \{ i|\log^{(i)} n < 10\}$. When $i$ is a constant we get time $O(n/p+\log^{(i)}n\log \log l+\log l)$. Thus when $l=Ω(\log^{(c)}n)$ for any constant $c$ we achieve $O(n/p+\log l)$ time. The previous best deterministic EREW PRAM algorithm has time $O(n/p+\log n)$ and best CRCW PRAM algorithm has time $O(n/p+\log n/\log \log n+\log l)$. Keywords: Parallel algorithms, linked list, linked list contraction, uniform linked list contraction, EREW PRAM.