IA Scholar Query: Formalizing non-termination of recursive programs.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgSat, 31 Dec 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Revisiting Iso-Recursive Subtyping
https://scholar.archive.org/work/vfmzcu77zvdkbhgmeocsrubsde
The Amber rules are well-known and widely used for subtyping iso-recursive types. They were first briefly and informally introduced in 1985 by Cardelli in a manuscript describing the Amber language. Despite their use over many years, important aspects of the metatheory of the iso-recursive style Amber rules have not been studied in depth or turn out to be quite challenging to formalize. This article aims to revisit the problem of subtyping iso-recursive types. We start by introducing a novel declarative specification for Amber-style iso-recursive subtyping. Informally, the specification states that two recursive types are subtypes if all their finite unfoldings are subtypes . The Amber rules are shown to have equivalent expressive power to this declarative specification. We then show two variants of sound , complete and decidable algorithmic formulations of subtyping with respect to the declarative specification, which employ the idea of double unfoldings . Compared to the Amber rules, the double unfolding rules have the advantage of: (1) being modular; (2) not requiring reflexivity to be built in; (3) leading to an easy proof of transitivity of subtyping; and (4) being easily applicable to subtyping relations that are not antisymmetric (such as subtyping relations with record types). This work sheds new insights on the theory of subtyping iso-recursive types, and the new rules based on double unfoldings have important advantages over the original Amber rules involving recursive types. All results are mechanically formalized in the Coq theorem prover.Yaoda Zhou, Jinxu Zhao, Bruno C. D. S. Oliveirawork_vfmzcu77zvdkbhgmeocsrubsdeSat, 31 Dec 2022 00:00:00 GMTConjunctive queries for logic-based information extraction
https://scholar.archive.org/work/wd2pb3qomzeb7lqcc3av3fepqq
This thesis offers two logic-based approaches to conjunctive queries in the context of information extraction. The first and main approach is the introduction of conjunctive query fragments of the logics FC and FC[REG], denoted as FC-CQ and FC[REG]-CQ respectively. FC is a first-order logic based on word equations, where the semantics are defined by limiting the universe to the factors of some finite input word. FC[REG] is FC extended with regular constraints. Our first results consider the comparative expressive power of FC[REG]-CQ in relation to document spanners (a formal framework for the query language AQL), and various fragments of FC[REG]-CQ – some of which coincide with well-known language generators, such as patterns and regular expressions. Then, we look at decision problems. We show that many decision problems for FC-CQ and FC[REG]-CQ (such as equivalence and regularity) are undecidable. The model checking problem for FC-CQ and FC[REG]-CQ is NP-complete even if the FC-CQ is acyclic – under the definition of acyclicity where each word equation in an FC-CQ is an atom. This leads us to look at the "decomposition" of an FC word equation into a conjunction of binary word equations (i.e., of the form x =˙ y · z). If a query consists of only binary word equations and the query is acyclic, then model checking is tractable and we can enumerate results efficiently. We give an algorithm that decomposes an FC-CQ into an acyclic FC-CQ consisting of binary word equations in polynomial time, or determines that this is not possible. The second approach is to consider the dynamic complexity of FC. This uses the common way of encoding words in a relational structure using a universe with a linear order along with symbol predicates. Then, each element of the universe can carry a symbol if the predicate for said symbol holds for that element. Instead of the "usual way" (looking at first-order logic over these structures), we study the dynamic complexity, where symbols can be modified. As each of these modifications only c [...]Sam M Thompsonwork_wd2pb3qomzeb7lqcc3av3fepqqWed, 30 Nov 2022 00:00:00 GMTComputation of Parameter Dependent Robust Invariant Sets for LPV Models with Guaranteed Performance
https://scholar.archive.org/work/tvq6bk6vcvdetoeikxvz2kl2q4
This paper presents an iterative algorithm to compute a Robust Control Invariant (RCI) set, along with an invariance-inducing control law, for Linear Parameter-Varying (LPV) systems. As the real-time measurements of the scheduling parameters are typically available, in the presented formulation, we allow the RCI set description along with the invariance-inducing controller to be scheduling parameter dependent. The considered formulation thus leads to parameter-dependent conditions for the set invariance, which are replaced by sufficient Linear Matrix Inequality (LMI) conditions via Polya's relaxation. These LMI conditions are then combined with a novel volume maximization approach in a Semidefinite Programming (SDP) problem, which aims at computing the desirably large RCI set. In addition to ensuring invariance, it is also possible to guarantee performance within the RCI set by imposing a chosen quadratic performance level as an additional constraint in the SDP problem. The reported numerical example shows that the presented iterative algorithm can generate invariant sets which are larger than the maximal RCI sets computed without exploiting scheduling parameter information.Ankit Gupta, Manas Mejari, Paolo Falcone, Dario Pigawork_tvq6bk6vcvdetoeikxvz2kl2q4Wed, 30 Nov 2022 00:00:00 GMTReinforcement Learning with Dynamic Convex Risk Measures
https://scholar.archive.org/work/k7vmgnmfuvgflattu4provllqi
We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time-consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules that aid in obtaining optimal policies. We further develop an actor-critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to three optimization problems: statistical arbitrage trading strategies, financial hedging, and obstacle avoidance robot control.Anthony Coache, Sebastian Jaimungalwork_k7vmgnmfuvgflattu4provllqiWed, 30 Nov 2022 00:00:00 GMTBalancing covariates in randomized experiments with the Gram-Schmidt Walk design
https://scholar.archive.org/work/tpsxer6pnbdrxhhbj5fljsmtfy
The design of experiments involves an inescapable compromise between covariate balance and robustness. This paper provides a formalization of this trade-off and introduces an experimental design that allows experimenters to navigate it. The design is specified by a robustness parameter that bounds the worst-case mean squared error of an estimator of the average treatment effect. Subject to the experimenter's desired level of robustness, the design aims to simultaneously balance all linear functions of potentially many covariates. The achieved level of balance is better than previously known possible and considerably better than what a fully random assignment would produce. We show that the mean squared error of the estimator is bounded by the minimum of the loss function of an implicit ridge regression of the potential outcomes on the covariates. The estimator does not itself conduct covariate adjustment, so one can interpret the approach as regression adjustment by design. Finally, we provide both a central limit theorem and non-asymptotic tail bounds for the estimator, which facilitate the construction of confidence intervals.Christopher Harshaw and Fredrik Sävje and Daniel Spielman and Peng Zhangwork_tpsxer6pnbdrxhhbj5fljsmtfyWed, 30 Nov 2022 00:00:00 GMTReady or Not? Understanding the factors that impact on offender treatment readiness
https://scholar.archive.org/work/6h6uetvkkvdofk6hp76nfr5gbq
Poor engagement in treatment amongst forensic populations is associated with negative consequences for clients, practitioners, the criminal justice system, and general population. Therefore, understanding the factors which impact offending behaviour treatment engagement is a crucial issue in offender rehabilitation. This thesis aims to develop a more in-depth understanding of readiness to engage with treatment amongst forensic populations. A qualitative approach is applied to explore the views and opinions of both the individuals participating within offending behaviour programmes, and the staff whom are directly involved in the selection and facilitation of these programmes within a UK prison. Consensus regarding reasons for treatment completion/non-completion was found, which appeared to support the factors outlined in the Multifactor Offender Readiness Model (MORM), a model of treatment readiness. Furthermore, additional factors to those outlined within the MORM were also identified. The data and discoveries detailed throughout this thesis are rooted within original research, and provide a contribution to the understanding of offender treatment readiness. Furthermore, this thesis is the first to investigate the applicability of the MORM within the prison setting, making the project unique and novel. Based on the results found, implications for practice are discussed and areas for future research highlighted in order to further existing research. Recommendations to enhance readiness to engage with treatment are provided for both prisons and programme users. In addition, owing to the acknowledged need for individualised assessment and case formulation prior to treatment being offered to reduce the rates of inappropriate referral to treatment and utilise resources more efficiently, a treatment readiness formulation protocol has been developed by the author in response to the research findings.Karla Churchill-Bettswork_6h6uetvkkvdofk6hp76nfr5gbqWed, 30 Nov 2022 00:00:00 GMTBridging Causal Reversibility and Time Reversibility: A Stochastic Process Algebraic Approach
https://scholar.archive.org/work/yw4hozjk4fbbhgzrj6hwbaeuuy
Causal reversibility blends causality and reversibility for concurrent systems. It indicates that an action can be undone provided that all of its consequences have been undone already, thus making it possible to bring the system back to a past consistent state. Time reversibility is instead considered in the field of stochastic processes, mostly for efficient analysis purposes. A performance model based on a continuous-time Markov chain is time reversible if its stochastic behavior remains the same when the direction of time is reversed. We bridge these two theories of reversibility by showing the conditions under which causal reversibility and time reversibility are both ensured by construction. This is done in the setting of a stochastic process calculus, which is then equipped with a variant of stochastic bisimilarity accounting for both forward and backward directions.Marco Bernardo, Claudio Antares Mezzinawork_yw4hozjk4fbbhgzrj6hwbaeuuyWed, 30 Nov 2022 00:00:00 GMTComplexity of Deciding Syntactic Equivalence up to Renaming for Term Rewriting Systems (Extended Version)
https://scholar.archive.org/work/3srcupmcy5aolacb2hif6ax5sa
Motivated by questions from program transformations, eight notions of isomorphisms between term rewriting systems are defined, analysed, and classified. The notions include global isomorphisms, where the renaming of variables and function symbols is the same for all term rewriting rules of the system, local ones, where a single renaming for every rule is used, and a combination, where one symbol set is renamed globally while the other set is renamed locally. Preservation of semantic properties like convertibility and termination is analysed for the different isomorphism notions. The notions of templates and maximal normal forms of term rewriting systems are introduced and algorithms to efficiently compute them are presented. Equipped with these techniques, the complexity of the underlying decision problems of the isomorphisms are analysed and either shown to be efficiently solvable or proved to be complete for the graph isomorphism complexity class.Michael Christian Fink Amores, David Sabelwork_3srcupmcy5aolacb2hif6ax5saWed, 30 Nov 2022 00:00:00 GMTSupervised Feature Compression based on Counterfactual Analysis
https://scholar.archive.org/work/vunze73nrbdvzldz7pkwcusujm
Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, a smaller, therefore more interpretable Decision Tree can be trained, which, in addition, enhances the stability and robustness of the baseline Decision Tree. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity compared to the baseline Decision Tree.Veronica Piccialli, Dolores Romero Morales, Cecilia Salvatorework_vunze73nrbdvzldz7pkwcusujmTue, 29 Nov 2022 00:00:00 GMTThe Complexity of Infinite-Horizon General-Sum Stochastic Games
https://scholar.archive.org/work/rzuknccxqfgzlntwc7xkouqibi
We study the complexity of computing stationary Nash equilibrium (NE) in n-player infinite-horizon general-sum stochastic games. We focus on the problem of computing NE in such stochastic games when each player is restricted to choosing a stationary policy and rewards are discounted. First, we prove that computing such NE is in PPAD (in addition to clearly being PPAD-hard). Second, we consider turn-based specializations of such games where at each state there is at most a single player that can take actions and show that these (seemingly-simpler) games remain PPAD-hard. Third, we show that under further structural assumptions on the rewards computing NE in such turn-based games is possible in polynomial time. Towards achieving these results we establish structural facts about stochastic games of broader utility, including monotonicity of utilities under single-state single-action changes and reductions to settings where each player controls a single state.Yujia Jin, Vidya Muthukumar, Aaron Sidfordwork_rzuknccxqfgzlntwc7xkouqibiTue, 29 Nov 2022 00:00:00 GMT2019
https://scholar.archive.org/work/wcy47hfvvvdwvfgnwx2cuak4ze
On completion of this course, students will have knowledge in: • CO1.Basics of electrochemistry. Classical & modern batteries and fuel cells. CO2. Causes & effects of corrosion of metals and control of corrosion. Modification of surface properties of metals to develop resistance to corrosion, wear, tear, impact etc. by electroplating and electroless plating. CO3. Production & consumption of energy for industrialization of country and living standards of people. Utilization of solar energy for different useful forms of energy. CO4. Understanding Phase rule and instrumental techniques and its applications. CO5.Over viewing of synthesis, properties and applications of nanomaterials.BTECH.CSwork_wcy47hfvvvdwvfgnwx2cuak4zeMon, 28 Nov 2022 00:00:00 GMTA numerical approach to the optimal control of thermally convective flows
https://scholar.archive.org/work/iaiubc3gf5fpfhwvyve4akbqiy
The optimal control of thermally convective flows is usually modeled by an optimization problem with constraints of Boussinesq equations that consist of the Navier-Stokes equation and an advection-diffusion equation. This optimal control problem is challenging from both theoretical analysis and algorithmic design perspectives. For example, the nonlinearity and coupling of fluid flows and energy transports prevent direct applications of gradient type algorithms in practice. In this paper, we propose an efficient numerical method to solve this problem based on the operator splitting and optimization techniques. In particular, we employ the Marchuk-Yanenko method leveraged by the L^2-projection for the time discretization of the Boussinesq equations so that the Boussinesq equations are decomposed into some easier linear equations without any difficulty in deriving the corresponding adjoint system. Consequently, at each iteration, four easy linear advection-diffusion equations and two degenerated Stokes equations at each time step are needed to be solved for computing a gradient. Then, we apply the Bercovier-Pironneau finite element method for space discretization, and design a BFGS type algorithm for solving the fully discretized optimal control problem. We look into the structure of the problem, and design a meticulous strategy to seek step sizes for the BFGS efficiently. Efficiency of the numerical approach is promisingly validated by the results of some preliminary numerical experiments.Yongcun Song, Xiaoming Yuan, Hangrui Yuework_iaiubc3gf5fpfhwvyve4akbqiyMon, 28 Nov 2022 00:00:00 GMTMultiple Query Satisfiability of Constrained Horn Clauses
https://scholar.archive.org/work/erb23iwnifgyflw6lgucuvaxui
We address the problem of checking the satisfiability of a set of constrained Horn clauses (CHCs) possibly including more than one query. We propose a transformation technique that takes as input a set of CHCs, including a set of queries, and returns as output a new set of CHCs, such that the transformed CHCs are satisfiable if and only if so are the original ones, and the transformed CHCs incorporate in each new query suitable information coming from the other ones so that the CHC satisfiability algorithm is able to exploit the relationships among all queries. We show that our proposed technique is effective on a non trivial benchmark of sets of CHCs that encode many verification problems for programs manipulating algebraic data types such as lists and trees.Emanuele De Angeliswork_erb23iwnifgyflw6lgucuvaxuiMon, 28 Nov 2022 00:00:00 GMTEmbedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification
https://scholar.archive.org/work/wj5xkagk6rfkjgecke6x7wxeei
A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17–43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after thae trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.Chutinun Prasitpuriprecha, Rapeepan Pitakaso, Sarayut Gonwirat, Prem Enkvetchakul, Thanawadee Preeprem, Sirima Suvarnakuta Jantama, Chutchai Kaewta, Nantawatana Weerayuth, Thanatkij Srichok, Surajet Khonjun, Natthapong Nanthasamroengwork_wj5xkagk6rfkjgecke6x7wxeeiMon, 28 Nov 2022 00:00:00 GMT2019
https://scholar.archive.org/work/g6qfzbclcfe6pobzwry66zimou
On completion of this course, students will have knowledge in: • CO1.Basics of electrochemistry. Classical & modern batteries and fuel cells. CO2. Causes & effects of corrosion of metals and control of corrosion. Modification of surface properties of metals to develop resistance to corrosion, wear, tear, impact etc. by electroplating and electroless plating. CO3. Production & consumption of energy for industrialization of country and living standards of people. Utilization of solar energy for different useful forms of energy. CO4. Understanding Phase rule and instrumental techniques and its applications. CO5.Over viewing of synthesis, properties and applications of nanomaterials.BTECH.MECHwork_g6qfzbclcfe6pobzwry66zimouMon, 28 Nov 2022 00:00:00 GMTSome Upper Bounds on the Running Time of Policy Iteration on Deterministic MDPs
https://scholar.archive.org/work/j2klrt7pfrdz5ms7j3dh6ndcvu
Policy Iteration (PI) is a widely used family of algorithms to compute optimal policies for Markov Decision Problems (MDPs). We derive upper bounds on the running time of PI on Deterministic MDPs (DMDPs): the class of MDPs in which every state-action pair has a unique next state. Our results include a non-trivial upper bound that applies to the entire family of PI algorithms, and affirmation that a conjecture regarding Howard's PI on MDPs is true for DMDPs. Our analysis is based on certain graph-theoretic results, which may be of independent interest.Ritesh Goenka, Eashan Gupta, Sushil Khyalia, Pratyush Agarwal, Mulinti Shaik Wajid, Shivaram Kalyanakrishnanwork_j2klrt7pfrdz5ms7j3dh6ndcvuMon, 28 Nov 2022 00:00:00 GMTPolynomial algorithms for p-dispersion problems in a planar Pareto Front
https://scholar.archive.org/work/drggnwucs5ehjoiosebifuu7gi
In this paper, p-dispersion problems are studied to select p⩾ 2 representative points from a large 2D Pareto Front (PF), solution of bi-objective optimization. Four standard p-dispersion variants are considered. A novel variant, Max-Sum-Neighbor p-dispersion, is introduced for the specific case of a 2D PF. Firstly, 2-dispersion and 3-dispersion problems are proven solvable in O(n) time in a 2D PF. Secondly, dynamic programming algorithms are designed for three p-dispersion variants, proving polynomial complexities in a 2D PF. Max-min p-dispersion is solvable in O(pnlog n) time and O(n) memory space. Max-Sum-Neighbor p-dispersion is proven solvable in O(pn^2) time andO(n) space. Max-Sum-min p-dispersion is solvable in O(pn^3) time and O(pn^2) space, this complexity holds also in 1D, proving for the first time that Max-Sum-min p-dispersion is polynomial in 1D. Furthermore, properties of these algorithms are discussed for an efficient implementation and for a practical application inside bi-objective meta-heuristics.Nicolas Dupinwork_drggnwucs5ehjoiosebifuu7giMon, 28 Nov 2022 00:00:00 GMTSimulation Intelligence: Towards a New Generation of Scientific Methods
https://scholar.archive.org/work/rfujm43y4ngcnml5emnvjksbjy
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.Alexander Lavin, David Krakauer, Hector Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfefferwork_rfujm43y4ngcnml5emnvjksbjySun, 27 Nov 2022 00:00:00 GMTCross-Lingual Transfer Learning for Statistical Type Inference
https://scholar.archive.org/work/nyrnl53qs5bfrkycng7fdnizce
Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data. Most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose a cross-lingual transfer learning framework, PLATO, for statistical type inference, which allows us to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others, e.g., Python to JavaScript, Java to JavaScript, etc. PLATO is powered by a novel kernelized attention mechanism to constrain the attention scope of the backbone Transformer model such that model is forced to base its prediction on commonly shared features among languages. In addition, we propose the syntax enhancement that augments the learning on the feature overlap among language domains. Furthermore, PLATO can also be used to improve the performance of the conventional supervised-based type inference by introducing cross-language augmentation, which enables the model to learn more general features across multiple languages. We evaluated PLATO under two settings: 1) under the cross-domain scenario that the target language data is not labeled or labeled partially, the results show that PLATO outperforms the state-of-the-art domain transfer techniques by a large margin, e.g., it improves the Python to TypeScript baseline by +14.6%@EM, +18.6%@weighted-F1, and 2) under the conventional monolingual supervised scenario, PLATO improves the Python baseline by +4.10%@EM, +1.90%@weighted-F1 with the introduction of the cross-lingual augmentation.Zhiming Li, Xiaofei Xie, Haoliang Li, Zhengzi Xu, Yi Li, Yang Liuwork_nyrnl53qs5bfrkycng7fdnizceSat, 26 Nov 2022 00:00:00 GMTThirty-three deformation classes of compact hyperkähler orbifolds
https://scholar.archive.org/work/j7tq42kbvberhjeeggsz7mtdui
As their smooth analogue the irreducible symplectic varieties appear as elementary bricks in the generalizations of the Bogomolov decomposition theorem (arXiv:math/0402243, arXiv:2012.00441). Let S be a K3 surface; generalizing the Fujiki construction, we investigate the irreducible symplectic varieties with simply connected smooth locus that can be obtained as terminalizations of quotients of the product S^n. In dimension 4, we compute the singularities for 29 orbifolds examples which appear to be independent under deformation. We also provide 4 additional orbifolds examples in dimension 6.Grégoire Menetwork_j7tq42kbvberhjeeggsz7mtduiSat, 26 Nov 2022 00:00:00 GMT