IA Scholar Query: NP-Complete Decision Problems for Quadratic Polynomials
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgSat, 15 Oct 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Multi-Objective Flexible Job Shop Scheduling Using Genetic Algorithms
https://scholar.archive.org/work/rwgne7ub4rgxdl3czkf4hmrm44
Flexible Job Shop Scheduling is an important problem in the fields of combinatorial optimization and production management. This research addresses multiobjective flexible job shop scheduling problem with the objective of simultaneous minimization of: (1) makespan, (2) workload of the most loaded machine, and (3) total workload. A general-purpose, domain independent genetic algorithm implemented in a spreadsheet environment is proposed for the flexible job shop. Spreadsheet functions are used to develop the shop model. Performance of the proposed algorithm is compared with heuristic algorithms already reported in the literature. Simulation experiments demonstrated that the proposed methodology can achieve solutions that are comparable to previous approaches in terms of solution quality and computational time. Flexible job shop models presented herein are easily customizable to cater for different objective functions without changing the basic genetic algorithm routine or the spreadsheet model. Experimental analysis demonstrates the robustness, simplicity, and general-purpose nature of the proposed approach.work_rwgne7ub4rgxdl3czkf4hmrm44Sat, 15 Oct 2022 00:00:00 GMTOn Finding Rank Regret Representatives
https://scholar.archive.org/work/r4npneviqrf5nludmsukxruju4
Selecting the best items in a dataset is a common task in data exploration. However, the concept of "best" lies in the eyes of the beholder: Different users may consider different attributes more important and, hence, arrive at different rankings. Nevertheless, one can remove "dominated" items and create a "representative" subset of the data, comprising the "best items" in it. A Pareto-optimal representative is guaranteed to contain the best item of each possible ranking, but it can be a large portion of data. A much smaller representative can be found if we relax the requirement of including the best item for each user and instead just limit the users' "regret." Existing work defines regret as the loss in score by limiting consideration to the representative instead of the full dataset, for any chosen ranking function. However, the score is often not a meaningful number, and users may not understand its absolute value. Sometimes small ranges in score can include large fractions of the dataset. In contrast, users do understand the notion of rank ordering. Therefore, we consider items' positions in the ranked list in defining the regret and propose the rank-regret representative as the minimal subset of the data containing at least one of the top- k of any possible ranking function. This problem is polynomial time solvable in two-dimensional space but is NP-hard on three or more dimensions. We design a suite of algorithms to fulfill different purposes, such as whether relaxation is permitted on k , the result size, or both, whether a distribution is known, whether theoretical guarantees or practical efficiency is important, and so on. Experiments on real datasets demonstrate that we can efficiently find small subsets with small rank-regrets.Abolfazl Asudeh, Gautam Das, H. V. Jagadish, Shangqi Lu, Azade Nazi, Yufei Tao, Nan Zhang, Jianwen Zhaowork_r4npneviqrf5nludmsukxruju4Fri, 30 Sep 2022 00:00:00 GMTA Tutorial Introduction to Lattice-based Cryptography and Homomorphic Encryption
https://scholar.archive.org/work/vlqa6rnsa5d3vnpa3qeaizot6a
Why study Lattice-based Cryptography? There are a few ways to answer this question. 1. It is useful to have cryptosystems that are based on a variety of hard computational problems so the different cryptosystems are not all vulnerable in the same way. 2. The computational aspects of lattice-based cryptosystem are usually simple to understand and fairly easy to implement in practice. 3. Lattice-based cryptosystems have lower encryption/decryption computational complexities compared to popular cryptosystems that are based on the integer factorisation or the discrete logarithm problems. 4. Lattice-based cryptosystems enjoy strong worst-case hardness security proofs based on approximate versions of known NP-hard lattice problems. 5. Lattice-based cryptosystems are believed to be good candidates for post-quantum cryptography, since there are currently no known quantum algorithms for solving lattice problems that perform significantly better than the best-known classical (non-quantum) algorithms, unlike for integer factorisation and (elliptic curve) discrete logarithm problems. 6. Last but not least, interesting structures in lattice problems have led to significant advances in Homomorphic Encryption, a new research area with wide-ranging applications.Yang Li, Kee Siong Ng, Michael Purcellwork_vlqa6rnsa5d3vnpa3qeaizot6aWed, 28 Sep 2022 00:00:00 GMTStudies of quantum chromodynamics with jets at the CMS experiment at the LHC
https://scholar.archive.org/work/tl6cqxvdijhwbnd4wxd3l6sbii
Several people played a decisive role in accomplishing this thesis and helped me in dierent aspects. In Hamburg, I would like to extend my deepest gratitude to Patrick L.S. Connor for his invaluable contribution to this work and for training me to consider scientic research as a "share, help, learn, cross-check, enjoy" cycle. Besides developing the overall analysis framework, he was always reachable for help and support, making the work with him a continuous upskilling process. I am also extremely grateful to Paolo Gunnellini for his contributions to the analysis, but mainly for his crucial guidance during my rst steps in high energy physics and his availability to help whenever I needed to. At DESY, I am deeply indebted to Hannes Jung for all his hospitality and support. Apart from that, he also gave me the opportunity to work with his wonderful team, to whom I am also grateful. In particular, many thanks toParaskevas Gianneios, University Of Ioanninawork_tl6cqxvdijhwbnd4wxd3l6sbiiWed, 28 Sep 2022 00:00:00 GMTA Survey on Physical Adversarial Attack in Computer Vision
https://scholar.archive.org/work/7hvhyotxjbfu3gciygbxxdilgq
In the past decade, deep learning has dramatically changed the traditional hand-craft feature manner with strong feature learning capability, resulting in tremendous improvement of conventional tasks. However, deep neural networks have recently been demonstrated vulnerable to adversarial examples, a kind of malicious samples crafted by small elaborately designed noise, which mislead the DNNs to make the wrong decisions while remaining imperceptible to humans. Adversarial examples can be divided into digital adversarial attacks and physical adversarial attacks. The digital adversarial attack is mostly performed in lab environments, focusing on improving the performance of adversarial attack algorithms. In contrast, the physical adversarial attack focus on attacking the physical world deployed DNN systems, which is a more challenging task due to the complex physical environment (i.e., brightness, occlusion, and so on). Although the discrepancy between digital adversarial and physical adversarial examples is small, the physical adversarial examples have a specific design to overcome the effect of the complex physical environment. In this paper, we review the development of physical adversarial attacks in DNN-based computer vision tasks, including image recognition tasks, object detection tasks, and semantic segmentation. For the sake of completeness of the algorithm evolution, we will briefly introduce the works that do not involve the physical adversarial attack. We first present a categorization scheme to summarize the current physical adversarial attacks. Then discuss the advantages and disadvantages of the existing physical adversarial attacks and focus on the technique used to maintain the adversarial when applied into physical environment. Finally, we point out the issues of the current physical adversarial attacks to be solved and provide promising research directions.Donghua Wang, Wen Yao, Tingsong Jiang, Guijiang Tang, Xiaoqian Chenwork_7hvhyotxjbfu3gciygbxxdilgqWed, 28 Sep 2022 00:00:00 GMTFlavour-universal search for heavy neutral leptons with a deep neural network-based displaced jet tagger with the CMS experiment
https://scholar.archive.org/work/gb63ulsuuzhixe7bhaqbgb7a5q
This thesis describes a search for long-lived heavy neutral leptons using a dataset of 137/fb collected during the 2016-2018 proton-proton runs with the CMS detector. The search uses a final state containing two leptons and at least one hadronic jet. This is the first analysis at the Large Hadron Collider which considers universal mixing between the Standard Model and heavy neutral lepton species. The search makes heavy use of a deep neural network-based displaced jet tagging algorithm, originally developed to target heavy long-lived gluino decays. The tagger was trained on both simulation and proton-proton collision data using the domain adaptation technique, which significantly improved the modelling of its output in simulation. The tagger has excellent performance for a range of long-lived particle lifetimes and generalises well to various flavours of displaced jets. In this analysis, the backgrounds are estimated in an entirely data-driven manner. No evidence for heavy neutral leptons is observed, and upper limits are set for a wide range of heavy neutral lepton mass, lifetime, and mixing scenarios. This is the most sensitive search for heavy neutral leptons in the 1–12 GeV mass range to date.Vilius Cepaitis, Alexander Tapper, Science And Technology Facilities Councilwork_gb63ulsuuzhixe7bhaqbgb7a5qWed, 28 Sep 2022 00:00:00 GMTComputational complexity of problems for deterministic presentations of sofic shifts
https://scholar.archive.org/work/hc7nasbv6ngy5iyqhnbxgvc52q
Sofic shifts are symbolic dynamical systems defined by the set of bi-infinite sequences on an edge-labeled directed graph, called a presentation. We study the computational complexity of an array of natural decision problems about presentations of sofic shifts, such as whether a given graph presents a shift of finite type, or an irreducible shift; whether one graph presents a subshift of another; and whether a given presentation is minimal, or has a synchronizing word. Leveraging connections to automata theory, we first observe that these problems are all decidable in polynomial time when the given presentation is irreducible (strongly connected), via algorithms both known and novel to this work. For the general (reducible) case, however, we show they are all PSPACE-complete. All but one of these problems (subshift) remain polynomial-time solvable when restricting to synchronizing deterministic presentations. We also study the size of synchronizing words and synchronizing deterministic presentations.Justin Cai, Rafael Frongillowork_hc7nasbv6ngy5iyqhnbxgvc52qWed, 28 Sep 2022 00:00:00 GMTTTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning
https://scholar.archive.org/work/75gga5pnivhonlbsrygj43s4se
We present a novel procedure for optimization based on the combination of efficient quantized tensor train representation and a generalized maximum matrix volume principle. We demonstrate the applicability of the new Tensor Train Optimizer (TTOpt) method for various tasks, ranging from minimization of multidimensional functions to reinforcement learning. Our algorithm compares favorably to popular evolutionary-based methods and outperforms them by the number of function evaluations or execution time, often by a significant margin.Konstantin Sozykin, Andrei Chertkov, Roman Schutski, Anh-Huy Phan, Andrzej Cichocki, Ivan Oseledetswork_75gga5pnivhonlbsrygj43s4seWed, 28 Sep 2022 00:00:00 GMTOnline Search-based Collision-inclusive Motion Planning and Control for Impact-resilient Mobile Robots
https://scholar.archive.org/work/qw5mjoywhfepjiadyuiyxp2q4e
This paper focuses on the emerging paradigm shift of collision-inclusive motion planning and control for impact-resilient mobile robots, and develops a unified hierarchical framework for navigation in unknown and partially-observable cluttered spaces. At the lower-level, we develop a deformation recovery control and trajectory replanning strategy that handles collisions that may occur at run-time, locally. The low-level system actively detects collisions (via embedded Hall effect sensors on a mobile robot built in-house), enables the robot to recover from them, and locally adjusts the post-impact trajectory. Then, at the higher-level, we propose a search-based planning algorithm to determine how to best utilize potential collisions to improve certain metrics, such as control energy and computational time. Our method builds upon A* with jump points. We generate a novel heuristic function, and a collision checking and adjustment technique, thus making the A* algorithm converge faster to reach the goal by exploiting and utilizing possible collisions. The overall hierarchical framework generated by combining the global A* algorithm and the local deformation recovery and replanning strategy, as well as individual components of this framework, are tested extensively both in simulation and experimentally. An ablation study draws links to related state-of-the-art search-based collision-avoidance planners (for the overall framework), as well as search-based collision-avoidance and sampling-based collision-inclusive global planners (for the higher level). Results demonstrate our method's efficacy for collision-inclusive motion planning and control in unknown environments with isolated obstacles for a class of impact-resilient robots operating in 2D.Zhouyu Lu, Zhichao Liu, Merrick Campbell, Konstantinos Karydiswork_qw5mjoywhfepjiadyuiyxp2q4eTue, 27 Sep 2022 00:00:00 GMTSolving homogeneous linear equations over polynomial semirings
https://scholar.archive.org/work/vsscsis2snhrll7lfys24hg2ry
For a subset B of ℝ, denote by U(B) be the semiring of (univariate) polynomials in ℝ[X] that are strictly positive on B. Let ℕ[X] be the semiring of (univariate) polynomials with non-negative integer coefficients. We study solutions of homogeneous linear equations over the polynomial semirings U(B) and ℕ[X]. In particular, we prove local-global principles for solving single homogeneous linear equations over these semirings. We then show PTIME decidability of determining the existence of non-zero solutions over ℕ[X] of single homogeneous linear equations. Our study of these polynomial semirings is largely motivated by several semigroup algorithmic problems in the wreath product ℤ≀ℤ. As an application of our results, we show that the Identity Problem (whether a given semigroup contains the neutral element?) and the Group Problem (whether a given semigroup is a group?) for finitely generated sub-semigroups of the wreath product ℤ≀ℤ is decidable when elements of the semigroup generator have the form (y, ± 1).Ruiwen Dongwork_vsscsis2snhrll7lfys24hg2ryTue, 27 Sep 2022 00:00:00 GMTA characterization of functions over the integers computable in polynomial time using discrete differential equations
https://scholar.archive.org/work/z2bfjpkqvvadlp4ve2yfr5mp6e
This paper studies the expressive and computational power of discrete Ordinary Differential Equations (ODEs), a.k.a. (Ordinary) Difference Equations. It presents a new framework using these equations as a central tool for computation and algorithm design. We present the general theory of discrete ODEs for computation theory, we illustrate this with various examples of algorithms, and we provide several implicit characterizations of complexity and computability classes. The proposed framework presents an original point of view on complexity and computation classes. It unifies several constructions that have been proposed for characterizing these classes including classical approaches in implicit complexity using restricted recursion schemes, as well as recent characterizations of computability and complexity by classes of continuous ordinary differential equations. It also helps understanding the relationships between analog computations and classical discrete models of computation theory. At a more technical point of view, this paper points out the fundamental role of linear (discrete) ODEs and classical ODE tools such as changes of variables to capture computability and complexity measures, or as a tool for programming many algorithms.Olivier Bournez, Arnaud Durandwork_z2bfjpkqvvadlp4ve2yfr5mp6eSun, 25 Sep 2022 00:00:00 GMTRealizable Learning is All You Need
https://scholar.archive.org/work/kickchwacjbnrondd6dmujd2ga
The equivalence of realizable and agnostic learnability is a fundamental phenomenon in learning theory. With variants ranging from classical settings like PAC learning and regression to recent trends such as adversarially robust and private learning, it's surprising that we still lack a unified theory; traditional proofs of the equivalence tend to be disparate, and rely on strong model-specific assumptions like uniform convergence and sample compression. In this work, we give the first model-independent framework explaining the equivalence of realizable and agnostic learnability: a three-line blackbox reduction that simplifies, unifies, and extends our understanding across a wide variety of settings. This includes models with no known characterization of learnability such as learning with arbitrary distributional assumptions or general loss, as well as a host of other popular settings such as robust learning, partial learning, fair learning, and the statistical query model. More generally, we argue that the equivalence of realizable and agnostic learning is actually a special case of a broader phenomenon we call property generalization: any desirable property of a learning algorithm (e.g.\ noise tolerance, privacy, stability) that can be satisfied over finite hypothesis classes extends (possibly in some variation) to any learnable hypothesis class.Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajanwork_kickchwacjbnrondd6dmujd2gaSun, 25 Sep 2022 00:00:00 GMTDeep Neural Networks for Visual Reasoning
https://scholar.archive.org/work/viks3f7ou5fkvegimdooztmaxi
Visual perception and language understanding are - fundamental components of human intelligence, enabling them to understand and reason about objects and their interactions. It is crucial for machines to have this capacity to reason using these two modalities to invent new robot-human collaborative systems. Recent advances in deep learning have built separate sophisticated representations of both visual scenes and languages. However, understanding the associations between the two modalities in a shared context for multimodal reasoning remains a challenge. Focusing on language and vision modalities, this thesis advances the understanding of how to exploit and use pivotal aspects of vision-and-language tasks with neural networks to support reasoning. We derive these understandings from a series of works, making a two-fold contribution: (i) effective mechanisms for content selection and construction of temporal relations from dynamic visual scenes in response to a linguistic query and preparing adequate knowledge for the reasoning process (ii) new frameworks to perform reasoning with neural networks by exploiting visual-linguistic associations, deduced either directly from data or guided by external priors.Thao Minh Lework_viks3f7ou5fkvegimdooztmaxiSat, 24 Sep 2022 00:00:00 GMTCombinatorial optimization and reasoning with graph neural networks
https://scholar.archive.org/work/dszclpgdgfgzrnd562tfbceni4
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by enhancing exact solvers. The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers.Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, Petar Veličkovićwork_dszclpgdgfgzrnd562tfbceni4Fri, 23 Sep 2022 00:00:00 GMTGAGA: Deciphering Age-path of Generalized Self-paced Regularizer
https://scholar.archive.org/work/kamcia3bmbgqnnzkuwxxqcehz4
Nowadays self-paced learning (SPL) is an important machine learning paradigm that mimics the cognitive process of humans and animals. The SPL regime involves a self-paced regularizer and a gradually increasing age parameter, which plays a key role in SPL but where to optimally terminate this process is still non-trivial to determine. A natural idea is to compute the solution path w.r.t. age parameter (i.e., age-path). However, current age-path algorithms are either limited to the simplest regularizer, or lack solid theoretical understanding as well as computational efficiency. To address this challenge, we propose a novel Generalized Age-path Algorithm (GAGA) for SPL with various self-paced regularizers based on ordinary differential equations (ODEs) and sets control, which can learn the entire solution spectrum w.r.t. a range of age parameters. To the best of our knowledge, GAGA is the first exact path-following algorithm tackling the age-path for general self-paced regularizer. Finally the algorithmic steps of classic SVM and Lasso are described in detail. We demonstrate the performance of GAGA on real-world datasets, and find considerable speedup between our algorithm and competing baselines.Xingyu Qu, Diyang Li, Xiaohan Zhao, Bin Guwork_kamcia3bmbgqnnzkuwxxqcehz4Fri, 23 Sep 2022 00:00:00 GMTSubsequences in Bounded Ranges: Matching and Analysis Problems
https://scholar.archive.org/work/kazqifkclbhplc4c5wne73iatu
In this paper, we consider a variant of the classical algorithmic problem of checking whether a given word v is a subsequence of another word w. More precisely, we consider the problem of deciding, given a number p (defining a range-bound) and two words v and w, whether there exists a factor w[i:i+p-1] (or, in other words, a range of length p) of w having v as subsequence (i. e., v occurs as a subsequence in the bounded range w[i:i+p-1]). We give matching upper and lower quadratic bounds for the time complexity of this problem. Further, we consider a series of algorithmic problems in this setting, in which, for given integers k, p and a word w, we analyse the set p-Subseq_k(w) of all words of length k which occur as subsequence of some factor of length p of w. Among these, we consider the k-universality problem, the k-equivalence problem, as well as problems related to absent subsequences. Surprisingly, unlike the case of the classical model of subsequences in words where such problems have efficient solutions in general, we show that most of these problems become intractable in the new setting when subsequences in bounded ranges are considered. Finally, we provide an example of how some of our results can be applied to subsequence matching problems for circular words.Maria Kosche, Tore Koß, Florin Manea, Viktoriya Pakwork_kazqifkclbhplc4c5wne73iatuThu, 22 Sep 2022 00:00:00 GMTReversible Gromov-Monge Sampler for Simulation-Based Inference
https://scholar.archive.org/work/v6ibinwwvbc6lbhcbmejmndi7e
This paper introduces a new simulation-based inference procedure to model and sample from multi-dimensional probability distributions given access to i.i.d.samples, circumventing the usual approaches of explicitly modeling the density function or designing Markov chain Monte Carlo. Motivated by the seminal work on distance and isomorphism between metric measure spaces, we propose a new notion called the Reversible Gromov-Monge (RGM) distance and study how RGM can be used to design new transform samplers to perform simulation-based inference. Our RGM sampler can also estimate optimal alignments between two heterogeneous metric measure spaces (, μ, c_) and (, ν, c_) from empirical data sets, with estimated maps that approximately push forward one measure μ to the other ν, and vice versa. We study the analytic properties of the RGM distance and derive that under mild conditions, RGM equals the classic Gromov-Wasserstein distance. Curiously, drawing a connection to Brenier's polar factorization, we show that the RGM sampler induces bias towards strong isomorphism with proper choices of c_ and c_. Statistical rate of convergence, representation, and optimization questions regarding the induced sampler are studied. Synthetic and real-world examples showcasing the effectiveness of the RGM sampler are also demonstrated.YoonHaeng Hur, Wenxuan Guo, Tengyuan Liangwork_v6ibinwwvbc6lbhcbmejmndi7eThu, 22 Sep 2022 00:00:00 GMTComplexity through Translations for Modal Logic with Recursion
https://scholar.archive.org/work/uhfbgbvgjbhg7dyw4qnkf3rxl4
This paper studies the complexity of classical modal logics and of their extension with fixed-point operators, using translations to transfer results across logics. In particular, we show several complexity results for multi-agent logics via translations to and from the mu-calculus and modal logic, which allow us to transfer known upper and lower bounds. We also use these translations to introduce a terminating tableau system for the logics we study, based on Kozen's tableau for the mu-calculus, and the one of Fitting and Massacci for modal logic.Luca Acetowork_uhfbgbvgjbhg7dyw4qnkf3rxl4Wed, 21 Sep 2022 00:00:00 GMTOn the convex formulations of robust Markov decision processes
https://scholar.archive.org/work/plob7n5zuzdmppkt7rfy3o2o5i
Robust Markov decision processes (MDPs) are used for applications of dynamic optimization in uncertain environments and have been studied extensively. Many of the main properties and algorithms of MDPs, such as value iteration and policy iteration, extend directly to RMDPs. Surprisingly, there is no known analog of the MDP convex optimization formulation for solving RMDPs. This work describes the first convex optimization formulation of RMDPs under the classical sa-rectangularity and s-rectangularity assumptions. We derive a convex formulation with a linear number of variables and constraints but large coefficients in the constraints by using entropic regularization and exponential change of variables. Our formulation can be combined with efficient methods from convex optimization to obtain new algorithms for solving RMDPs with uncertain probabilities. We further simplify the formulation for RMDPs with polyhedral uncertainty sets. Our work opens a new research direction for RMDPs and can serve as a first step toward obtaining a tractable convex formulation of RMDPs.Julien Grand-Clément, Marek Petrikwork_plob7n5zuzdmppkt7rfy3o2o5iWed, 21 Sep 2022 00:00:00 GMTIncremental Updates of Generalized Hypertree Decompositions
https://scholar.archive.org/work/q7o5osfh3rgutbuxu3w4tmjsvi
Structural decomposition methods, such as generalized hypertree decompositions, have been successfully used for solving constraint satisfaction problems (CSPs). As decompositions can be reused to solve CSPs with the same constraint scopes, investing resources in computing good decompositions is beneficial, even though the computation itself is hard. Unfortunately, current methods need to compute a completely new decomposition even if the scopes change only slightly. In this paper, we make the first steps toward solving the problem of updating the decomposition of a CSP P so that it becomes a valid decomposition of a new CSP P' produced by some modification of P. Even though the problem is hard in theory, we propose and implement a framework for effectively updating GHDs. The experimental evaluation of our algorithm strongly suggests practical applicability.Georg Gottlob, Matthias Lanzinger, Davide Mario Longo, Cem Okulmuswork_q7o5osfh3rgutbuxu3w4tmjsviWed, 21 Sep 2022 00:00:00 GMT