IA Scholar Query: Computing preimages of Boolean networks.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgTue, 02 Aug 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440A ZK-SNARK based Proof of Assets Protocol for Bitcoin Exchanges
https://scholar.archive.org/work/74x7bupmjnc2zpfv7oquecsdhi
This paper proposes a protocol for Proof of Assets of a bitcoin exchange using the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARK) without revealing either the bitcoin addresses of the exchange or balances associated with those addresses. The proof of assets is a mechanism to prove the total value of bitcoins the exchange has authority to spend using its private keys. We construct a privacy-preserving ZK-SNARK proof system to prove the knowledge of the private keys corresponding to the bitcoin assets of an exchange. The ZK-SNARK tool-chain helps to convert an NP-Statement for proving the knowledge of the private keys (known to the exchange) into a circuit satisfiability problem. In this protocol, the exchange creates a Pedersen commitment to the value of bitcoins associated with each address without revealing the balance. The simulation results show that the proof generation time, size, and verification time are efficient in practice.B Swaroopa Reddywork_74x7bupmjnc2zpfv7oquecsdhiTue, 02 Aug 2022 00:00:00 GMTData Fusion: Theory, Methods, and Applications
https://scholar.archive.org/work/ntcpnuxe4zd3do75kjdnhn6j6a
A proper fusion of complex data is of interest to many researchers in diverse fields, including computational statistics, computational geometry, bioinformatics, machine learning, pattern recognition, quality management, engineering, statistics, finance, economics, etc. It plays a crucial role in: synthetic description of data processes or whole domains, creation of rule bases for approximate reasoning tasks, reaching consensus and selection of the optimal strategy in decision support systems, imputation of missing values, data deduplication and consolidation, record linkage across heterogeneous databases, and clustering. This open-access research monograph integrates the spread-out results from different domains using the methodology of the well-established classical aggregation framework, introduces researchers and practitioners to Aggregation 2.0, as well as points out the challenges and interesting directions for further research.Marek Gagolewskiwork_ntcpnuxe4zd3do75kjdnhn6j6aTue, 02 Aug 2022 00:00:00 GMTNon-Malleable Code in the Split-State Model
https://scholar.archive.org/work/ztfqgsidivapnjtm5tnumvelee
Non-malleable codes are a natural relaxation of error correction and error detection codes applicable in scenarios where error-correction or error-detection is impossible. Over the last decade, non-malleable codes have been studied for a wide variety of tampering families. Among the most well studied of these is the split-state family of tampering channels, where the codeword is split into two or more parts and each part is tampered with independently. We survey various constructions and applications of non-malleable codes in the split-state model.Divesh Aggarwal, Marshall Ball, Maciej Obremskiwork_ztfqgsidivapnjtm5tnumveleeThu, 28 Jul 2022 00:00:00 GMTWhat Happens after SGD Reaches Zero Loss? –A Mathematical Framework
https://scholar.archive.org/work/sqxipqzznrdw3kznzuw53lbxu4
Understanding the implicit bias of Stochastic Gradient Descent (SGD) is one of the key challenges in deep learning, especially for overparametrized models, where the local minimizers of the loss function L can form a manifold. Intuitively, with a sufficiently small learning rate η, SGD tracks Gradient Descent (GD) until it gets close to such manifold, where the gradient noise prevents further convergence. In such a regime, Blanc et al. (2020) proved that SGD with label noise locally decreases a regularizer-like term, the sharpness of loss, tr[∇^2 L]. The current paper gives a general framework for such analysis by adapting ideas from Katzenberger (1991). It allows in principle a complete characterization for the regularization effect of SGD around such manifold – i.e., the "implicit bias" – using a stochastic differential equation (SDE) describing the limiting dynamics of the parameters, which is determined jointly by the loss function and the noise covariance. This yields some new results: (1) a global analysis of the implicit bias valid for η^-2 steps, in contrast to the local analysis of Blanc et al. (2020) that is only valid for η^-1.6 steps and (2) allowing arbitrary noise covariance. As an application, we show with arbitrary large initialization, label noise SGD can always escape the kernel regime and only requires O(κln d) samples for learning an κ-sparse overparametrized linear model in ℝ^d (Woodworth et al., 2020), while GD initialized in the kernel regime requires Ω(d) samples. This upper bound is minimax optimal and improves the previous Õ(κ^2) upper bound (HaoChen et al., 2020).Zhiyuan Li, Tianhao Wang, Sanjeev Arorawork_sqxipqzznrdw3kznzuw53lbxu4Thu, 28 Jul 2022 00:00:00 GMTEVOLUTION AND ANALYSIS OF SECURED HASH ALGORITHM (SHA) FAMILY
https://scholar.archive.org/work/beohn3mwzna3ldw2xoqlm57s5i
With the rapid advancement of technologies and proliferation of intelligent devices, connecting to the internet challenges have grown manifold, such as ensuring communication security and keeping user credentials secret. Data integrity and user privacy have become crucial concerns in any ecosystem of advanced and interconnected communications. Cryptographic hash functions have been extensively employed to ensure data integrity in insecure environments. Hash functions are also combined with digital signatures to offer identity verification mechanisms and non-repudiation services. The federal organization National Institute of Standards and Technology (NIST) established the SHA to provide security and optimal performance over some time. The most well-known hashing standards are SHA-1, SHA-2, and SHA-3. This paper discusses the background of hashing, followed by elaborating on the evolution of the SHA family. The main goal is to present a comparative analysis of these hashing standards and focus on their security strength, performance and limitations against common attacks. The complete assessment was carried out using statistical analysis, performance analysis and extensive fault analysis over a defined test environment. The study outcome showcases the issues of SHA-1 besides exploring the security benefits of all the dominant variants of SHA-2 and SHA-3. The study also concludes that SHA-3 is the best option to mitigate novice intruders while allowing better performance cost-effectively.Burhan Ul Islam Khan, Rashidah Funke Olanrewaju, Malik Arman Morshidi, Roohie Naaz Mir, Miss Laiha Binti Mat Kiah, Abdul Mobeen Khanwork_beohn3mwzna3ldw2xoqlm57s5iWed, 27 Jul 2022 00:00:00 GMTFlag Polymatroids
https://scholar.archive.org/work/jkvwnluwinhapnfgwdxookr7rq
We generalize the flag matroids of Gelfand et al. to polymatroids by studying the geometry of Edmonds' greedy algorithm. The greedy paths on polymatroid polytopes are precisely the monotone paths with respect to a certain canonical linear function. The geometry of all paths is captured by monotone path polytopes, that we prove to be polymatroid base polytopes. The combinatorics of flag polymatroids is determined by the underlying lattice of flats. We show that flag polymatroid polytopes are normally equivalent to certain nestohedra, which yields new insights into the combinatorics of flag matroids. We give various examples that illustrate the rich combinatorial structure of flag polymatroids. We also study general monotone paths on polymatroid base polytopes, that relate to the enumeration of certain Young tableaux.Alexander E. Black, Raman Sanyalwork_jkvwnluwinhapnfgwdxookr7rqMon, 25 Jul 2022 00:00:00 GMTOn the connection of probabilistic model checking, planning, and learning for system verification
https://scholar.archive.org/work/elsht7dndnf7jlue3wo3zte7l4
This thesis presents approaches using techniques from the model checking, planning, and learning community to make systems more reliable and perspicuous. First, two heuristic search and dynamic programming algorithms are adapted to be able to check extremal reachability probabilities, expected accumulated rewards, and their bounded versions, on general Markov decision processes (MDPs). Thereby, the problem space originally solvable by these algorithms is enlarged considerably. Correctness and optimality proofs for the adapted algorithms are given, and in a comprehensive case study on established benchmarks it is shown that the implementation, called Modysh, is competitive with state-of-the-art model checkers and even outperforms them on very large state spaces. Second, Deep Statistical Model Checking (DSMC) is introduced, usable for quality assessment and learning pipeline analysis of systems incorporating trained decision-making agents, like neural networks (NNs). The idea of DSMC is to use statistical model checking to assess NNs resolving nondeterminism in systems modeled as MDPs. The versatility of DSMC is exemplified in a number of case studies on Racetrack, an MDP benchmark designed for this purpose, flexibly modeling the autonomous driving challenge. In a comprehensive scalability study it is demonstrated that DSMC is a lightweight technique tackling the complexity of NN analysis in combination with the state space explosion problem. III Zusammenfassung Diese Arbeit präsentiert Ansätze, die Techniken aus dem Model Checking, Planning und Learning Bereich verwenden, um Systeme verlässlicher und klarer verständlich zu machen. Zuerst werden zwei Algorithmen für heuristische Suche und dynamisches Programmieren angepasst, um Extremwerte für Erreichbarkeitswahrscheinlichkeiten, Erwartungswerte für Kosten und beschränkte Varianten davon, auf generellen Markov Entscheidungsprozessen (MDPs) zu untersuchen. Damit wird der Problemraum, der ursprünglich mit diesen Algorithmen gelöst wurde, deutlich erweitert. Korrektheits-und Optimalitätsbeweise für die angepassten Algorithmen werden gegeben und in einer umfassenden Fallstudie wird gezeigt, dass die Implementierung, namens Modysh, konkurrenzfähig mit den modernsten Model Checkern ist und deren Leistung auf sehr großen Zustandsräumen sogar übertrifft. Als Zweites wird Deep Statistical Model Checking (DSMC) für die Qualitätsbewertung und Lernanalyse von Systemen mit integrierten trainierten Entscheidungsgenten, wie z.B. neuronalen Netzen (NN), eingeführt. Die Idee von DSMC ist es, statistisches Model Checking zur Bewertung von NNs zu nutzen, die Nichtdeterminismus in Systemen, die als MDPs modelliert sind, auflösen. Die Vielseitigkeit des Ansatzes wird in mehreren Fallbeispielen auf Racetrack gezeigt, einer MDP Benchmark, die zu diesem Zweck entwickelt wurde und die Herausforderung des autonomen Fahrens flexibel modelliert. In einer umfassenden Skalierbarkeitsstudie wird demonstriert, dass DSMC eine leichtgewichtige Technik ist, die die Komplexität der NN-Analyse in Kombination mit dem State Space Explosion Problem bewältigt. V Some Personal Words My endeavor of pursuing a PhD at the Dependable Systems and Software chair started already in August 2017 before even signing a contract. After several tutor positions and a lot of fun and work in the mathematics preparatory course team, I already knew Felix, Gereon, and Sebastian who promised me that working here would be a lot of fun. To get to know also the others in the team, Holger offered me the opportunity to take part in the chair's retreat in Tanna, a very small city in Thüringen. Nobody expected that this trip would turn into a literal crash landing for me. But after surviving this first shock, returning home without getting lost at a toilet on a service station, and knowing that Vahid has a big heart for goats, I started officially in November and shared my office with Gilles and Yuliya. Dear Gilles, I have to confess, that at first glance I was misled like the grandma who changed to the other side of the street but very soon we turned into office mates who know the habits of each other very well. We had quite some philosophical and deep discussions about our work and doing a PhD in general. All this helped me so much in overcoming some doubts and I learned a lot from you. Thank you very much! Soon we established Game and Movie Nights in our new leisure room with the very extravagant orange zebra sofa which is exactly 1 m(ichaela) long. At CONFESTA 2018 I got the chance to go with Daniel, Gereon, and Sebastian on the greatest trip ever. We visited Beijing for the conference and the final CAP workshop. We made it without getting hacked, or thrown into prison, but with hurting legs from all the sightseeing, climbing up the wall, and especially from the soccer match Gereon, Rob van Glabbeek, Uwe Nestmann, and me won against Kim Larsen.Michaela Klauck, Universität Des Saarlandeswork_elsht7dndnf7jlue3wo3zte7l4Mon, 18 Jul 2022 00:00:00 GMTLeaderless and Multi-Leader Computation in Disconnected Anonymous Dynamic Networks
https://scholar.archive.org/work/i2euo24zwfgwbhhfijmbwpxuui
We give a simple and complete characterization of which functions can be deterministically computed by anonymous processes in disconnected dynamic networks, depending on the number of leaders in the network. In addition, we provide efficient distributed algorithms for computing all such functions assuming minimal or no knowledge about the network. Each of our algorithms comes in two versions: one that terminates with the correct output and a faster one that stabilizes on the correct output without explicit termination. Notably, all of our algorithms have running times that scale linearly both with the number of processes and with a parameter of the network which we call "dynamic disconnectivity". We also provide matching lower bounds, showing that all our algorithms are asymptotically optimal for any fixed number of leaders. While most of the existing literature on anonymous dynamic networks relies on classical mass-distribution techniques, our work makes use of a recently introduced combinatorial structure called "history tree". Among other contributions, our results establish a new state of the art on two popular fundamental problems for anonymous dynamic networks: leaderless "Average Consensus" (i.e., computing the mean value of input numbers distributed among the processes) and multi-leader "Counting" (i.e., determining the exact number of processes in the network).Giuseppe A. Di Luna, Giovanni Vigliettawork_i2euo24zwfgwbhhfijmbwpxuuiSun, 17 Jul 2022 00:00:00 GMTSampling from Pre-Images to Learn Heuristic Functions for Classical Planning
https://scholar.archive.org/work/pn4h3e2zxzcurjonmgbcs36ggq
We introduce a new algorithm, Regression based Supervised Learning (RSL), for learning per instance Neural Network (NN) defined heuristic functions for classical planning problems. RSL uses regression to select relevant sets of states at a range of different distances from the goal. RSL then formulates a Supervised Learning problem to obtain the parameters that define the NN heuristic, using the selected states labeled with exact or estimated distances to goal states. Our experimental study shows that RSL outperforms, in terms of coverage, previous classical planning NN heuristics functions while requiring two orders of magnitude less training time.Stefan O'Toole, Miquel Ramirez, Nir Lipovetzky, Adrian R. Pearcework_pn4h3e2zxzcurjonmgbcs36ggqThu, 07 Jul 2022 00:00:00 GMTCombinatorial generation via permutation languages. IV. Elimination trees
https://scholar.archive.org/work/wnq4hf2mpbbl3acit3avar77nq
An elimination tree for a connected graph G is a rooted tree on the vertices of G obtained by choosing a root x and recursing on the connected components of G-x to produce the subtrees of x. Elimination trees appear in many guises in computer science and discrete mathematics, and they encode many interesting combinatorial objects, such as bitstrings, permutations and binary trees. We apply the recent Hartung-Hoang-Mütze-Williams combinatorial generation framework to elimination trees, and prove that all elimination trees for a chordal graph G can be generated by tree rotations using a simple greedy algorithm. This yields a short proof for the existence of Hamilton paths on graph associahedra of chordal graphs. Graph associahedra are a general class of high-dimensional polytopes introduced by Carr, Devadoss, and Postnikov, whose vertices correspond to elimination trees and whose edges correspond to tree rotations. As special cases of our results, we recover several classical Gray codes for bitstrings, permutations and binary trees, and we obtain a new Gray code for partial permutations. Our algorithm for generating all elimination trees for a chordal graph G can be implemented in time 𝒪(σ) on average per generated elimination tree, where σ=σ(G) denotes the maximum number of edges of an induced star in G. If G is a tree, we improve this to a loopless algorithm running in time 𝒪(1) per generated elimination tree. We also prove that our algorithm produces a Hamilton cycle on the graph associahedron of G, rather than just Hamilton path, if the graph G is chordal and 2-connected. Moreover, our algorithm characterizes chordality, i.e., it computes a Hamilton path on the graph associahedron of G if and only if G is chordal.Jean Cardinal, Arturo Merino, Torsten Mützework_wnq4hf2mpbbl3acit3avar77nqMon, 04 Jul 2022 00:00:00 GMTAn Improved Algorithm for Finding the Shortest Synchronizing Words
https://scholar.archive.org/work/qjwcwef4jbbhfipxprut2liiii
A synchronizing word of a deterministic finite complete automaton is a word whose action maps every state to a single one. Finding a shortest or a short synchronizing word is a central computational problem in the theory of synchronizing automata and is applied in other areas such as model-based testing and the theory of codes. Because the problem of finding a shortest synchronizing word is computationally hard, among exact algorithms only exponential ones are known. We redesign the previously fastest known exact algorithm based on the bidirectional breadth-first search and improve it with respect to time and space in a practical sense. We develop new algorithmic enhancements and adapt the algorithm to multithreaded and GPU computing. Our experiments show that the new algorithm is multiple times faster than the previously fastest one and its advantage quickly grows with the hardness of the problem instance. Given a modest time limit, we compute the lengths of the shortest synchronizing words for random binary automata up to 570 states, significantly beating the previous record. We refine the experimental estimation of the average reset threshold of these automata. Finally, we develop a general computational package devoted to the problem, where an efficient and practical implementation of our algorithm is included, together with several well-known heuristics.Marek Szykuła, Adam Zyzikwork_qjwcwef4jbbhfipxprut2liiiiSun, 03 Jul 2022 00:00:00 GMTLIPIcs, Volume 230, ITC 2022, Complete Volume
https://scholar.archive.org/work/x5cobg6anzbgjazexwg7mkanie
LIPIcs, Volume 230, ITC 2022, Complete VolumeDana Dachman-Soledwork_x5cobg6anzbgjazexwg7mkanieThu, 30 Jun 2022 00:00:00 GMTConstraint-Driven Explanations for Black-Box ML Models
https://scholar.archive.org/work/nud2pczv6nhcdpevqv5gbm3l6u
The need to understand the inner workings of opaque Machine Learning models has prompted researchers to devise various types of post-hoc explanations. A large class of such explainers proceed in two phases: first perturb an input instance whose explanation is sought, and then generate an interpretable artifact to explain the prediction of the opaque model on that instance. Recently, Deutch and Frost proposed to use an additional input from the user: a set of constraints over the input space to guide the perturbation phase. While this approach affords the user the ability to tailor the explanation to their needs, striking a balance between flexibility, theoretical rigor and computational cost has remained an open challenge. We propose a novel constraint-driven explanation generation approach which simultaneously addresses these issues in a modular fashion. Our framework supports the use of expressive Boolean constraints giving the user more flexibility to specify the subspace to generate perturbations from. Leveraging advances in Formal Methods, we can theoretically guarantee strict adherence of the samples to the desired distribution. This also allows us to compute fidelity in a rigorous way, while scaling much better in practice. Our empirical study demonstrates concrete uses of our tool CLIME in obtaining more meaningful explanations with high fidelity.Aditya A. Shrotri, Nina Narodytska, Alexey Ignatiev, Kuldeep S Meel, Joao Marques-Silva, Moshe Y. Vardiwork_nud2pczv6nhcdpevqv5gbm3l6uTue, 28 Jun 2022 00:00:00 GMTAlgorithms and Data Structures for First-Order Logic with Connectivity Under Vertex Failures
https://scholar.archive.org/work/ma56pjiaczae5dq2nwrxtbmxyq
We introduce a new data structure for answering connectivity queries in undirected graphs subject to batched vertex failures. Precisely, given any graph G and integer parameter k, we can in fixed-parameter time construct a data structure that can later be used to answer queries of the form: "are vertices s and t connected via a path that avoids vertices u₁,..., u_k?" in time 2^𝒪(k). In the terminology of the literature on data structures, this gives the first deterministic data structure for connectivity under vertex failures where for every fixed number of failures, all operations can be performed in constant time. With the aim to understand the power and the limitations of our new techniques, we prove an algorithmic meta theorem for the recently introduced separator logic, which extends first-order logic with atoms for connectivity under vertex failures. We prove that the model-checking problem for separator logic is fixed-parameter tractable on every class of graphs that exclude a fixed topological minor. We also show a weak converse. This implies that from the point of view of parameterized complexity, under standard complexity theoretical assumptions, the frontier of tractability of separator logic is almost exactly delimited by classes excluding a fixed topological minor. The backbone of our proof relies on a decomposition theorem of Cygan, Lokshtanov, Pilipczuk, Pilipczuk, and Saurabh [SICOMP '19], which provides a tree decomposition of a given graph into bags that are unbreakable. Crucially, unbreakability allows to reduce separator logic to plain first-order logic within each bag individually. Guided by this observation, we design our model-checking algorithm using dynamic programming over the tree decomposition, where the transition at each bag amounts to running a suitable model-checking subprocedure for plain first-order logic. This approach is robust enough to provide also an extension to efficient enumeration of answers to a query expressed in separator logic.Michał Pilipczuk, Nicole Schirrmacher, Sebastian Siebertz, Szymon Toruńczyk, Alexandre Vigny, Mikołaj Bojańczyk, Emanuela Merelli, David P. Woodruffwork_ma56pjiaczae5dq2nwrxtbmxyqTue, 28 Jun 2022 00:00:00 GMTThe Gaussian conditional independence inference problem
https://scholar.archive.org/work/hzmnfg7kmbe77ijyxwv7efiryu
Die vorliegende Dissertation beschäftigt sich mit Strukturen Gaußscher bedingter Unabhängigkeit und ihrem Inferenzproblem. Bedingte Unabhängigkeit (engl. conditional independence, CI) ist ein Begriff aus der Wahrscheinlichkeits- und Informationstheorie und "Gaußsch" bezieht sich auf die bekannte multivariate Normalverteilung. Die CI-Relation einer multivariaten Zufallsvariable , deren Komponenten durch eine endliche Menge N indiziert sind, enthält Informationen darüber, welche Komponenten I die Verteilung anderer Komponenten J beeinflussen, wenn der Wert wieder anderer Komponenten K bekannt ist. Diese Relation wird als [ I ?? J j K] oder kurz (I; JjK) geschrieben. Bedingte Unabhängigkeit ist also eine dreiwertige Relation auf Teilvektoren von , die komplexe Abhängigkeiten zwischen den Variablen in kodiert. CI-Relationen werden formal in einem Zweig der künstlichen Intelligenz über logische Inferenzregeln studiert. Solche Inferenzregeln nehmen die folgende Form an: "wenn bestimmte bedingte Unabhängigkeiten gelten, welche (Disjunktionen von) anderen Unabhängigkeiten müssen ebenfalls gelten?" Kenntnis dieser Regeln erlaubt die automatische Deduktion von Informationen über die Abhängigkeitsstruktur von beobachteten Zufallsvariablen. Die Regeln, welche für CI-Relationen gelten, hängen von der Art der Wahrscheinlichkeitsverteilung ab. Binäre Verteilungen erfüllen beispielsweise andere Inferenzregeln als die kontinuierlichen Gaußschen Verteilungen. Eine multivariat Gauß-verteilte Zufallsvariable ist vollständig durch ihre Parameter, den Mittelwert 2 RN und die Kovarianzmatrix Σ 2 PDN, bestimmt. Unter dieser speziellen Annahme ist die bedingte Unabhängigkeitsaussage [ I ?? J j K] äquivalent zu einer Rangbedingung an die Teilmatrix von Σ mit Zeilen I [ K und Spalten J [ K, nämlich dass diese Matrix Rang jKj hat. Dieses Kriterium erlaubt die Behandlung von Gaußscher CI mit Mitteln der kommutativen Algebra, da die Rangbedingung als das Verschwinden einer Reihe von Polynomen in den Einträgen von Σ formuliert werden kann. Das [...]Tobias Boege, Universitäts- Und Landesbibliothek Sachsen-Anhalt, Martin-Luther Universität, Thomas Kahle, Volker Kaibelwork_hzmnfg7kmbe77ijyxwv7efiryuMon, 27 Jun 2022 00:00:00 GMTA Novel Length-Flexible Lightweight Cancelable Fingerprint Template for Privacy-Preserving Authentication Systems in Resource-Constrained IoT Applications
https://scholar.archive.org/work/d5wgimrrirhtrnpw4nciah5hx4
Fingerprint authentication techniques have been employed in various Internet of Things (IoT) applications for access control to protect private data, but raw fingerprint template leakage in unprotected IoT applications may render the authentication system insecure. Cancelable fingerprint templates can effectively prevent privacy breaches and provide strong protection to the original templates. However, to suit resource-constrained IoT devices, oversimplified templates would compromise authentication performance significantly. In addition, the length of existing cancelable fingerprint templates is usually fixed, making them difficult to be deployed in various memory-limited IoT devices. To address these issues, we propose a novel length-flexible lightweight cancelable fingerprint template for privacy-preserving authentication systems in various resource-constrained IoT applications. The proposed cancelable template design primarily consists of two components: 1) length-flexible partial-cancelable feature generation based on the designed re-indexing scheme; and 2) lightweight cancelable feature generation based on the designed encoding-nested-difference-XOR scheme. Comprehensive experimental results on public databases~FVC2002 DB1-DB4 and FVC2004 DB1-DB4 demonstrate that the proposed cancelable fingerprint template achieves equivalent authentication performance to state-of-the-art methods in IoT environments, but our design substantially reduces template storage space and computational cost. More importantly, the proposed length-flexible lightweight cancelable template is suitable for a variety of commercial smart cards (e.g., C5-M.O.S.T. Card Contact Microprocessor Smart Cards CLXSU064KC5). To the best of our knowledge, the proposed method is the first length-flexible lightweight, high-performing cancelable fingerprint template design for resource-constrained IoT applications.Xuefei Yin, Song Wang, Yanming Zhu, Jiankun Huwork_d5wgimrrirhtrnpw4nciah5hx4Sun, 26 Jun 2022 00:00:00 GMTScalable nanomechanical logic gate
https://scholar.archive.org/work/etosk3llzjbujfkjoievzwbwy4
Nanomechanical computers promise robust, low energy information processing. However, to date, electronics have generally been required to interconnect gates, while no scalable, purely nanomechanical approach to computing has been achieved. Here, we demonstrate a nanomechanical logic gate in a scalable architecture. Our gate uses the bistability of a nonlinear mechanical resonator to define logical states. These states are efficiently coupled into and out of the gate via nanomechanical waveguides, which provide the mechanical equivalent of electrical wires. Crucially, the input and output states share the same spatiotemporal characteristics, so that the output of one gate can serve as the input for the next. Our architecture is CMOS compatible, while realistic miniaturisation could allow both gigahertz frequencies and an energy cost that approaches the fundamental Landauer limit. Together this presents a pathway towards large-scale nanomechanical computers, as well as neuromorphic networks able to simulate computationally hard problems and interacting many-body systems.Erick Romero, Nicolas P. Mauranyapin, Timothy M. F. Hirsch, Rachpon Kalra, Christopher G. Baker, Glen I. Harris, Warwick P. Bowenwork_etosk3llzjbujfkjoievzwbwy4Fri, 24 Jun 2022 00:00:00 GMTPreparing many copies of a quantum state in the black-box model
https://scholar.archive.org/work/k4zaqjaxyjatxix26bqogpsh7e
We describe a simple quantum algorithm for preparing K copies of an N-dimensional quantum state whose amplitudes are given by a quantum oracle. Our result extends a previous work of Grover, who showed how to prepare one copy in time O(√(N)). In comparison with the naive O(K√(N)) solution obtained by repeating this procedure K times, our algorithm achieves the optimal running time of θ(√(KN)). Our technique uses a refinement of the quantum rejection sampling method employed by Grover. As a direct application, we obtain a similar speed-up for obtaining K independent samples from a distribution whose probability vector is given by a quantum oracle.Yassine Hamoudiwork_k4zaqjaxyjatxix26bqogpsh7eThu, 23 Jun 2022 00:00:00 GMTReclaiming scalability and privacy in the decentralized setting
https://scholar.archive.org/work/iyr7hfriavfklb6scz5p5gdy6i
The advent of blockchains has expanded the horizon of possibilities to novel decentralised applications and protocols that were not possible before. Designing and building such applications, be it for offering new ways for humans to interact or for circumventing the shortcomings of existing blockchains, requires analysing their security with a rigorous and multi-faceted approach. Indeed, the attack surface of decentralised, trustless applications is vastly more expansive than that of classical, server-client-based ones. Desirable properties such as security, privacy and scalability are attainable via established and widely applied approaches in the centralised case, where clients can afford to trust third party servers. Is it possible though for clients to self organize and attain these properties in use cases of interest without reliance on central authorities? We examine this question in the setting of a variety of blockchain-based applications. With an explicit aim of improving the state of the art and extending the limits of possible decentralised operations with precision and robustness, the present thesis explores, builds, analyses, and improves upon payments, content curation and decision making.Orfeas Stefanos Thyfronitis Litos, Orfeas Litos, University Of Edinburgh, Aggelos Kiayias, Kousha Etessamiwork_iyr7hfriavfklb6scz5p5gdy6iWed, 22 Jun 2022 00:00:00 GMTLearning to Search in Task and Motion Planning with Streams
https://scholar.archive.org/work/oxecvwct2nfcflc5ngxlw4qvni
Task and motion planning problems in robotics combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables. Recent works such as PDDLStream have focused on optimistic planning with an incrementally growing set of objects until a feasible trajectory is found. However, this set is exhaustively expanded in a breadth-first manner, regardless of the logical and geometric structure of the problem at hand, which makes long-horizon reasoning with large numbers of objects prohibitively time-consuming. To address this issue, we propose a geometrically informed symbolic planner that expands the set of objects and facts in a best-first manner, prioritized by a Graph Neural Network that is learned from prior search computations. We evaluate our approach on a diverse set of problems and demonstrate an improved ability to plan in large or difficult scenarios. We also apply our algorithm on a 7DOF robotic arm in several block-stacking manipulation tasks.Mohamed Khodeir and Ben Agro and Florian Shkurtiwork_oxecvwct2nfcflc5ngxlw4qvniTue, 21 Jun 2022 00:00:00 GMT