OpenRaiser/Intern-Atlas huggingface dataset:
Constrained Routing Between Non-Visible Vertices
Venue: arXiv cs.CG 2021
Year: 2021

In this paper we study local routing strategies on geometric graphs. Such strategies use geometric properties of the graph like the coordinates of the current and target nodes to route. Specifically, we study routing strategies in the presence of constraints which are obstacles that edges of the graph are not allowed to cross. Let $P$ be a set of $n$ points in the plane and let $S$ be a set of line segments whose endpoints are in $P$, with no two line segments intersecting properly. We present the first deterministic 1-local $O(1)$-memory routing algorithm that is guaranteed to find a path between two vertices in the visibility graph of $P$ with respect to a set of constraints $S$. The strategy never looks beyond the direct neighbors of the current node and does not store more than $O(1)$-information to reach the target. We then turn our attention to finding competitive routing strategies. We show that when routing on any triangulation $T$ of $P$ such that $S\subseteq T$, no $o(n)$-competitive routing algorithm exists when the routing strategy restricts its attention to the triangles intersected by the line segment from the source to the target (a technique commonly used in the unconstrained setting). Finally, we provide an $O(n)$-competitive deterministic 1-local $O(1)$-memory routing algorithm on any such $T$, which is optimal in the worst case, given the lower bound.
Extensional and Intensional Semantics of Bounded and Unbounded Nondeterminism
Venue: arXiv cs.LO 2021
Year: 2021

We give extensional and intensional characterizations of functional programs with nondeterminism: as structure preserving functions between biorders, and as nondeterministic sequential algorithms on ordered concrete data structures which compute them. A fundamental result establishes that these extensional and intensional representations are equivalent, by showing how to construct the unique sequential algorithm which computes a given monotone and stable function, and describing the conditions on sequential algorithms which correspond to continuity with respect to each order. We illustrate by defining may-testing and must-testing denotational semantics for sequential functional languages with bounded and unbounded choice operators. We prove that these are computationally adequate, despite the non-continuity of the must-testing semantics of unbounded nondeterminism. In the bounded case, we prove that our continuous models are fully abstract with respect to may-testing and must-testing by identifying a simple universal type, which may also form the basis for models of the untyped λ-calculus. In the unbounded case we observe that our model contains computable functions which are not denoted by terms, by identifying a further "weak continuity" property of the definable elements, and use this to establish that it is not fully abstract.
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
Venue: arXiv cs.LG 2021
Year: 2021

This paper introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This framework, termed physics-guided neural networks (PGNN), leverages the output of physics-based model simulations along with observational features in a hybrid modeling setup to generate predictions using a neural network architecture. Further, this framework uses physics-based loss functions in the learning objective of neural networks to ensure that the model predictions not only show lower errors on the training set but are also scientifically consistent with the known physics on the unlabeled set. We illustrate the effectiveness of PGNN for the problem of lake temperature modeling, where physical relationships between the temperature, density, and depth of water are used to design a physics-based loss function. By using scientific knowledge to guide the construction and learning of neural networks, we are able to show that the proposed framework ensures better generalizability as well as scientific consistency of results. All the code and datasets used in this study have been made available on this link \url{https://github.com/arkadaw9/PGNN}.
Locally-Iterative Distributed (Delta + 1)-Coloring below Szegedy-Vishwanathan Barrier, and Applications to Self-Stabilization and to Restricted-Bandwidth Models
Venue: arXiv cs.DC 2021
Year: 2021

We consider graph coloring and related problems in the distributed message-passing model. {Locally-iterative algorithms} are especially important in this setting. These are algorithms in which each vertex decides about its next color only as a function of the current colors in its 1-hop neighborhood. In STOC'93 Szegedy and Vishwanathan showed that any locally-iterative (Delta+1)-coloring algorithm requires Omega(Delta log Delta + log^* n) rounds, unless there is "a very special type of coloring that can be very efficiently reduced" \cite{SV93}. In this paper we obtain this special type of coloring. Specifically, we devise a locally-iterative (Delta+1)-coloring algorithm with running time O(Delta + log^* n), i.e., {below} Szegedy-Vishwanathan barrier. This demonstrates that this barrier is not an inherent limitation for locally-iterative algorithms. As a result, we also achieve significant improvements for dynamic, self-stabilizing and bandwidth-restricted settings: - We obtain self-stabilizing distributed algorithms for (Delta+1)-vertex-coloring, (2Delta-1)-edge-coloring, maximal independent set and maximal matching with O(Delta+log^* n) time. This significantly improves previously-known results that have O(n) or larger running times \cite{GK10}. - We devise a (2Delta-1)-edge-coloring algorithm in the CONGEST model with O(Delta + log^* n) time and in the Bit-Round model with O(Delta + log n) time. Previously-known algorithms had superlinear dependency on Delta for (2Delta-1)-edge-coloring in these models. - We obtain an arbdefective coloring algorithm with running time O(\sqrt Delta + log^* n). We employ it in order to compute proper colorings that improve the recent state-of-the-art bounds of Barenboim from PODC'15 \cite{B15} and Fraigniaud et al. from FOCS'16 \cite{FHK16} by polylogarithmic factors. - Our algorithms are applicable to the SET-LOCAL model of \cite{HKMS15}.
Atomic Norm Based Localization of Far-Field and Near-Field Signals with Generalized Symmetric Arrays
Venue: arXiv cs.IT 2021
Year: 2021

Most localization methods for mixed far-field (FF) and near-field (NF) sources are based on uniform linear array (ULA) rather than sparse linear array (SLA). In this paper, we propose a localization method for mixed FF and NF sources based on the generalized symmetric linear arrays, which include ULAs, Cantor array, Fractal array and many other SLAs. Our method consists of two steps. In the first step, the high-order statistics of the array output is exploited to increase the degree of freedom. Then the direction-of-arrivals (DOAs) of the FF and NF sources are jointly estimated by using the recently proposed atomic norm minimization (ANM), which belongs to the gridless super-resolution method since the discretization of the parameter space is not required. In the second step, the ranges are given by MUSIC-like one-dimensional searching. Simulations results are provided to demonstrate the advantages of our method.
Completeness for the Complexity Class $\forall \exists \mathbb{R}$ and Area-Universality
Venue: arXiv cs.CG 2021
Year: 2021

Exhibiting a deep connection between purely geometric problems and real algebra, the complexity class $\exists \mathbb{R}$ plays a crucial role in the study of geometric problems. Sometimes $\exists \mathbb{R}$ is referred to as the 'real analog' of NP. While NP is a class of computational problems that deals with existentially quantified boolean variables, $\exists \mathbb{R}$ deals with existentially quantified real variables. In analogy to $Π_2^p$ and $Σ_2^p$ in the famous polynomial hierarchy, we study the complexity classes $\forall \exists \mathbb{R}$ and $\exists \forall \mathbb{R}$ with real variables. Our main interest is the area-universality problem, where we are given a plane graph $G$, and ask if for each assignment of areas to the inner faces of $G$, there exists a straight-line drawing of $G$ realizing the assigned areas. We conjecture that area-universality is $\forall \exists \mathbb{R}$-complete and support this conjecture by proving $\exists \mathbb{R}$- and $\forall \exists \mathbb{R}$-completeness of two variants of area-universality. To this end, we introduce tools to prove $\forall \exists \mathbb{R}$-hardness and membership. Finally, we present geometric problems as candidates for $\forall \exists \mathbb{R}$-complete problems. These problems have connections to the concepts of imprecision, robustness, and extendability.
Build and Execution Environment (BEE): an Encapsulated Environment Enabling HPC Applications Running Everywhere
Venue: arXiv cs.DC 2021
Year: 2021

Variations in High Performance Computing (HPC) system software configurations mean that applications are typically configured and built for specific HPC environments. Building applications can require a significant investment of time and effort for application users and requires application users to have additional technical knowledge. Linux container technologies such as Docker and Charliecloud bring great benefits to the application development, build and deployment processes. While cloud platforms already widely support containers, HPC systems still have non-uniform support of container technologies. In this work, we propose a unified runtime framework -- Build and Execution Environment (BEE) across both HPC and cloud platforms that allows users to run their containerized HPC applications across all supported platforms without modification. We design four BEE backends for four different classes of HPC or cloud platform so that together they cover the majority of mainstream computing platforms for HPC users. Evaluations show that BEE provides an easy-to-use unified user interface, execution environment, and comparable performance.
RAQ: Relationship-Aware Graph Querying in Large Networks
Venue: arXiv cs.DB 2021
Year: 2021

The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a given query graph in a (large) target graph. However, almost all existing techniques have tacitly ignored the presence of relationships in graphs, which are usually encoded through interactions between node and edge labels. In this paper, we propose RAQ -- Relationship-Aware Graph Querying, to mitigate this gap. Given a query graph, RAQ identifies the $k$ best matching subgraphs of the target graph that encode similar relationships as in the query graph. To assess the utility of RAQ as a graph querying paradigm for knowledge discovery and exploration tasks, we perform a user survey on the Internet Movie Database (IMDb), where an overwhelming 86% of the 170 surveyed users preferred the relationship-aware match over traditional graph querying. The need to perform subgraph isomorphism renders RAQ NP-hard. The querying is made practical through beam stack search. Extensive experiments on multiple real-world graph datasets demonstrate RAQ to be effective, efficient, and scalable.
SentiPers: A Sentiment Analysis Corpus for Persian
Venue: arXiv cs.CL 2021
Year: 2021

Sentiment Analysis (SA) is a major field of study in natural language processing, computational linguistics and information retrieval. Interest in SA has been constantly growing in both academia and industry over the recent years. Moreover, there is an increasing need for generating appropriate resources and datasets in particular for low resource languages including Persian. These datasets play an important role in designing and developing appropriate opinion mining platforms using supervised, semi-supervised or unsupervised methods. In this paper, we outline the entire process of developing a manually annotated sentiment corpus, SentiPers, which covers formal and informal written contemporary Persian. To the best of our knowledge, SentiPers is a unique sentiment corpus with such a rich annotation in three different levels including document-level, sentence-level, and entity/aspect-level for Persian. The corpus contains more than 26000 sentences of users opinions from digital product domain and benefits from special characteristics such as quantifying the positiveness or negativity of an opinion through assigning a number within a specific range to any given sentence. Furthermore, we present statistics on various components of our corpus as well as studying the inter-annotator agreement among the annotators. Finally, some of the challenges that we faced during the annotation process will be discussed as well.
Random Access Channel Coding in the Finite Blocklength Regime
Venue: arXiv cs.IT 2021
Year: 2021

Consider a random access communication scenario over a channel whose operation is defined for any number of possible transmitters. As in the model recently introduced by Polyanskiy for the Multiple Access Channel (MAC) with a fixed, known number of transmitters, the channel is assumed to be invariant to permutations on its inputs, and all active transmitters employ identical encoders. Unlike the Polyanskiy model, in the proposed scenario, neither the transmitters nor the receiver knows which transmitters are active. We refer to this agnostic communication setup as the Random Access Channel (RAC). Scheduled feedback of a finite number of bits is used to synchronize the transmitters. The decoder is tasked with determining from the channel output the number of active transmitters, $k$, and their messages but not which transmitter sent which message. The decoding procedure occurs at a time $n_t$ depending on the decoder's estimate, $t$, of the number of active transmitters, $k$, thereby achieving a rate that varies with the number of active transmitters. Single-bit feedback at each time $n_i, i \leq t$, enables all transmitters to determine the end of one coding epoch and the start of the next. The central result of this work demonstrates the achievability on a RAC of performance that is first-order optimal for the MAC in operation during each coding epoch. While prior multiple access schemes for a fixed number of transmitters require $2^k - 1$ simultaneous threshold rules, the proposed scheme uses a single threshold rule and achieves the same dispersion.
Delay-Dependent Distributed Kalman Fusion Estimation with Dimensionality Reduction in Cyber-Physical Systems
Venue: arXiv cs.SY 2021
Year: 2021

This paper studies the distributed dimensionality reduction fusion estimation problem with communication delays for a class of cyber-physical systems (CPSs). The raw measurements are preprocessed in each sink node to obtain the local optimal estimate (LOE) of a CPS, and the compressed LOE under dimensionality reduction encounters with communication delays during the transmission. Under this case, a mathematical model with compensation strategy is proposed to characterize the dimensionality reduction and communication delays. This model also has the property to reduce the information loss caused by the dimensionality reduction and delays. Based on this model, a recursive distributed Kalman fusion estimator (DKFE) is derived by optimal weighted fusion criterion in the linear minimum variance sense. A stability condition for the DKFE, which can be easily verified by the exiting software, is derived. In addition, this condition can guarantee that estimation error covariance matrix of the DKFE converges to the unique steady-state matrix for any initial values, and thus the steady-state DKFE (SDKFE) is given. Notice that the computational complexity of the SDKFE is much lower than that of the DKFE. Moreover, a probability selection criterion for determining the dimensionality reduction strategy is also presented to guarantee the stability of the DKFE. Two illustrative examples are given to show the advantage and effectiveness of the proposed methods.
Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions
Venue: arXiv cs.CL 2021
Year: 2021

The past few years have witnessed renewed interest in NLP tasks at the interface between vision and language. One intensively-studied problem is that of automatically generating text from images. In this paper, we extend this problem to the more specific domain of face description. Unlike scene descriptions, face descriptions are more fine-grained and rely on attributes extracted from the image, rather than objects and relations. Given that no data exists for this task, we present an ongoing crowdsourcing study to collect a corpus of descriptions of face images taken `in the wild'. To gain a better understanding of the variation we find in face description and the possible issues that this may raise, we also conducted an annotation study on a subset of the corpus. Primarily, we found descriptions to refer to a mixture of attributes, not only physical, but also emotional and inferential, which is bound to create further challenges for current image-to-text methods.
Inverse Optimal Control from Incomplete Trajectory Observations
Venue: arXiv cs.RO 2021
Year: 2021

This article develops a methodology that enables learning an objective function of an optimal control system from incomplete trajectory observations. The objective function is assumed to be a weighted sum of features (or basis functions) with unknown weights, and the observed data is a segment of a trajectory of system states and inputs. The proposed technique introduces the concept of the recovery matrix to establish the relationship between any available segment of the trajectory and the weights of given candidate features. The rank of the recovery matrix indicates whether a subset of relevant features can be found among the candidate features and the corresponding weights can be learned from the segment data. The recovery matrix can be obtained iteratively and its rank non-decreasing property shows that additional observations may contribute to the objective learning. Based on the recovery matrix, a method for using incomplete trajectory observations to learn the weights of selected features is established, and an incremental inverse optimal control algorithm is developed by automatically finding the minimal required observation. The effectiveness of the proposed method is demonstrated on a linear quadratic regulator system and a simulated robot manipulator.
Datasheets for Datasets
Venue: arXiv cs.DB 2021
Year: 2021

The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose datasheets for datasets. In the electronics industry, every component, no matter how simple or complex, is accompanied with a datasheet that describes its operating characteristics, test results, recommended uses, and other information. By analogy, we propose that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses, and so on. Datasheets for datasets will facilitate better communication between dataset creators and dataset consumers, and encourage the machine learning community to prioritize transparency and accountability.
On Existence and Uniqueness of Formal Power Series Solutions of Algebraic Ordinary Differential Equations
Venue: arXiv cs.SC 2021
Year: 2021

Given an algebraic ordinary differential equation (AODE), we propose a computational method to determine when a truncated power series can be extended to a formal power series solution. If a certain regularity condition on the given AODE or on the initial values is fulfilled, we compute all of the solutions. Moreover, when the existence is confirmed, we present the algebraic structure of the set of all formal power series solutions.
Lolisa: Formal syntax and semantics for a subset of the solidity programming language in Mathematical Tool Coq
Venue: arXiv cs.PL 2021
Year: 2021

This article presents the formal syntax and semantics for a large subset of the Solidity programming language developed for the Etheruem blockchain platform based on our resent work about developing a general, extensible, and reusable formal memory (GERM) framework and an extension of Curry-Howard isomorphism, denoted as execution-verification isomorphism (EVI). This subset is denoted as Lolisa, which, to our knowledge, is the first mechanized and validated formal syntax and semantics developed for Solidity. The formal syntax of Lolisa adopts a stronger static type system than Solidity for enhanced type safety. In addition, Lolisa not only includes nearly all the syntax components of Solidity, such as mapping, modifier, contract, and address types, but it also contains general-purpose programming language features, such as multiple return values, pointer arithmetic, struct, and field access. Therefore, the inherent compatibility of Lolisa allows Solidity programs to be directly translated into Lolisa with a line-by-line correspondence without rebuilding or abstracting, and, in addition, the inherent generality of Lolisa allows it to be extended to express other programming languages as well. To this end, we also present a preliminary scheme for extending Lolisa to other languages systematically.
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Venue: arXiv cs.CR 2021
Year: 2021

As machine learning becomes widely used for automated decisions, attackers have strong incentives to manipulate the results and models generated by machine learning algorithms. In this paper, we perform the first systematic study of poisoning attacks and their countermeasures for linear regression models. In poisoning attacks, attackers deliberately influence the training data to manipulate the results of a predictive model. We propose a theoretically-grounded optimization framework specifically designed for linear regression and demonstrate its effectiveness on a range of datasets and models. We also introduce a fast statistical attack that requires limited knowledge of the training process. Finally, we design a new principled defense method that is highly resilient against all poisoning attacks. We provide formal guarantees about its convergence and an upper bound on the effect of poisoning attacks when the defense is deployed. We evaluate extensively our attacks and defenses on three realistic datasets from health care, loan assessment, and real estate domains.
Small-d MSR Codes with Optimal Access, Optimal Sub-Packetization and Linear Field Size
Venue: arXiv cs.IT 2021
Year: 2021

This paper presents an explicit construction of a class of optimal-access, minimum storage regenerating (MSR) codes, for small values of the number $d$ of helper nodes. The construction is valid for any parameter set $(n,k,d)$ with $d \in \{k+1, k+2, k+3\}$ and employs a finite field $\mathbb{F}_q$ of size $q=O(n)$. We will refer to the constructed codes as Small-d MSR codes. The sub-packetization level $α$ is given by $α= s^{{\lceil\frac{n}{s}\rceil}}$, where $s=d-k+1$. By an earlier result on the sub-packetization level for optimal-access MSR codes, this is the smallest value possible.