TY - JOUR AB - We report the visible light photocatalytic cleavage of trityl thioethers or ethers under pH-neutral conditions. The method results in the formation of the respective symmetrical disulfides and alcohols in moderate to excellent yield. The protocol only requires the addition of a suitable photocatalyst and light rendering it orthogonal to several functionalities, including acid labile protective groups. The same conditions can be used to directly convert trityl-protected thiols into unsymmetrical disulfides or selenosulfides, and to cleave trityl resins in solid phase organic synthesis. AU - Murakami, Sho AU - Brudy, Cosima AU - Bachmann, Moritz AU - Takemoto, Yoshiji AU - Pieber, Bartholomäus ID - 12919 IS - 09 JF - Synthesis KW - Organic Chemistry KW - Catalysis SN - 0039-7881 TI - Photocatalytic cleavage of trityl protected thiols and alcohols VL - 55 ER - TY - CONF AB - In this paper we introduce a pruning of the medial axis called the (λ,α)-medial axis (axλα). We prove that the (λ,α)-medial axis of a set K is stable in a Gromov-Hausdorff sense under weak assumptions. More formally we prove that if K and K′ are close in the Hausdorff (dH) sense then the (λ,α)-medial axes of K and K′ are close as metric spaces, that is the Gromov-Hausdorff distance (dGH) between the two is 1/4-Hölder in the sense that dGH (axλα(K),axλα(K′)) ≲ dH(K,K′)1/4. The Hausdorff distance between the two medial axes is also bounded, by dH (axλα(K),λα(K′)) ≲ dH(K,K′)1/2. These quantified stability results provide guarantees for practical computations of medial axes from approximations. Moreover, they provide key ingredients for studying the computability of the medial axis in the context of computable analysis. AU - Lieutier, André AU - Wintraecken, Mathijs ID - 13048 SN - 9781450399135 T2 - Proceedings of the 55th Annual ACM Symposium on Theory of Computing TI - Hausdorff and Gromov-Hausdorff stable subsets of the medial axis ER - TY - CONF AB - Deep neural networks (DNNs) often have to be compressed, via pruning and/or quantization, before they can be deployed in practical settings. In this work we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way, in order to produce models whose local loss behavior is stable under compression operations such as pruning. Thus, dense models trained via CrAM should be compressible post-training, in a single step, without significant accuracy loss. Experimental results on standard benchmarks, such as residual networks for ImageNet classification and BERT models for language modelling, show that CrAM produces dense models that can be more accurate than the standard SGD/Adam-based baselines, but which are stable under weight pruning: specifically, we can prune models in one-shot to 70-80% sparsity with almost no accuracy loss, and to 90% with reasonable (∼1%) accuracy loss, which is competitive with gradual compression methods. Additionally, CrAM can produce sparse models which perform well for transfer learning, and it also works for semi-structured 2:4 pruning patterns supported by GPU hardware. The code for reproducing the results is available at this https URL . AU - Peste, Elena-Alexandra AU - Vladu, Adrian AU - Kurtic, Eldar AU - Lampert, Christoph AU - Alistarh, Dan-Adrian ID - 13053 T2 - 11th International Conference on Learning Representations TI - CrAM: A Compression-Aware Minimizer ER - TY - CONF AB - GIMPS and PrimeGrid are large-scale distributed projects dedicated to searching giant prime numbers, usually of special forms like Mersenne and Proth primes. The numbers in the current search-space are millions of digits large and the participating volunteers need to run resource-consuming primality tests. Once a candidate prime N has been found, the only way for another party to independently verify the primality of N used to be by repeating the expensive primality test. To avoid the need for second recomputation of each primality test, these projects have recently adopted certifying mechanisms that enable efficient verification of performed tests. However, the mechanisms presently in place only detect benign errors and there is no guarantee against adversarial behavior: a malicious volunteer can mislead the project to reject a giant prime as being non-prime. In this paper, we propose a practical, cryptographically-sound mechanism for certifying the non-primality of Proth numbers. That is, a volunteer can – parallel to running the primality test for N – generate an efficiently verifiable proof at a little extra cost certifying that N is not prime. The interactive protocol has statistical soundness and can be made non-interactive using the Fiat-Shamir heuristic. Our approach is based on a cryptographic primitive called Proof of Exponentiation (PoE) which, for a group G, certifies that a tuple (x,y,T)∈G2×N satisfies x2T=y (Pietrzak, ITCS 2019 and Wesolowski, J. Cryptol. 2020). In particular, we show how to adapt Pietrzak’s PoE at a moderate additional cost to make it a cryptographically-sound certificate of non-primality. AU - Hoffmann, Charlotte AU - Hubáček, Pavel AU - Kamath, Chethan AU - Pietrzak, Krzysztof Z ID - 13143 SN - 0302-9743 T2 - Public-Key Cryptography - PKC 2023 TI - Certifying giant nonprimes VL - 13940 ER - TY - CONF AB - Reinforcement learning has received much attention for learning controllers of deterministic systems. We consider a learner-verifier framework for stochastic control systems and survey recent methods that formally guarantee a conjunction of reachability and safety properties. Given a property and a lower bound on the probability of the property being satisfied, our framework jointly learns a control policy and a formal certificate to ensure the satisfaction of the property with a desired probability threshold. Both the control policy and the formal certificate are continuous functions from states to reals, which are learned as parameterized neural networks. While in the deterministic case, the certificates are invariant and barrier functions for safety, or Lyapunov and ranking functions for liveness, in the stochastic case the certificates are supermartingales. For certificate verification, we use interval arithmetic abstract interpretation to bound the expected values of neural network functions. AU - Chatterjee, Krishnendu AU - Henzinger, Thomas A AU - Lechner, Mathias AU - Zikelic, Dorde ID - 13142 SN - 0302-9743 T2 - Tools and Algorithms for the Construction and Analysis of Systems TI - A learner-verifier framework for neural network controllers and certificates of stochastic systems VL - 13993 ER - TY - CONF AB - We automatically compute a new class of environment assumptions in two-player turn-based finite graph games which characterize an “adequate cooperation” needed from the environment to allow the system player to win. Given an ω-regular winning condition Φ for the system player, we compute an ω-regular assumption Ψ for the environment player, such that (i) every environment strategy compliant with Ψ allows the system to fulfill Φ (sufficiency), (ii) Ψ can be fulfilled by the environment for every strategy of the system (implementability), and (iii) Ψ does not prevent any cooperative strategy choice (permissiveness). For parity games, which are canonical representations of ω-regular games, we present a polynomial-time algorithm for the symbolic computation of adequately permissive assumptions and show that our algorithm runs faster and produces better assumptions than existing approaches—both theoretically and empirically. To the best of our knowledge, for ω -regular games, we provide the first algorithm to compute sufficient and implementable environment assumptions that are also permissive. AU - Anand, Ashwani AU - Mallik, Kaushik AU - Nayak, Satya Prakash AU - Schmuck, Anne Kathrin ID - 13141 SN - 0302-9743 T2 - TACAS 2023: Tools and Algorithms for the Construction and Analysis of Systems TI - Computing adequately permissive assumptions for synthesis VL - 13994 ER - TY - THES AB - During navigation, animals can infer the structure of the environment by computing the optic flow cues elicited by their own movements, and subsequently use this information to instruct proper locomotor actions. These computations require a panoramic assessment of the visual environment in order to disambiguate similar sensory experiences that may require distinct behavioral responses. The estimation of the global motion patterns is therefore essential for successful navigation. Yet, our understanding of the algorithms and implementations that enable coherent panoramic visual perception remains scarce. Here I pursue this problem by dissecting the functional aspects of interneuronal communication in the lobula plate tangential cell network in Drosophila melanogaster. The results presented in the thesis demonstrate that the basis for effective interpretation of the optic flow in this circuit are stereotyped synaptic connections that mediate the formation of distinct subnetworks, each extracting a particular pattern of global motion. Firstly, I show that gap junctions are essential for a correct interpretation of binocular motion cues by horizontal motion-sensitive cells. HS cells form electrical synapses with contralateral H2 neurons that are involved in detecting yaw rotation and translation. I developed an FlpStop-mediated mutant of a gap junction protein ShakB that disrupts these electrical synapses. While the loss of electrical synapses does not affect the tuning of the direction selectivity in HS neurons, it severely alters their sensitivity to horizontal motion in the contralateral side. These physiological changes result in an inappropriate integration of binocular motion cues in walking animals. While wild-type flies form a binocular perception of visual motion by non-linear integration of monocular optic flow cues, the mutant flies sum the monocular inputs linearly. These results indicate that rather than averaging signals in neighboring neurons, gap-junctions operate in conjunction with chemical synapses to mediate complex non-linear optic flow computations. Secondly, I show that stochastic manipulation of neuronal activity in the lobula plate tangential cell network is a powerful approach to study the neuronal implementation of optic flow-based navigation in flies. Tangential neurons form multiple subnetworks, each mediating course-stabilizing response to a particular global pattern of visual motion. Application of genetic mosaic techniques can provide sparse optogenetic activation of HS cells in numerous combinations. These distinct combinations of activated neurons drive an array of distinct behavioral responses, providing important insights into how visuomotor transformation is performed in the lobula plate tangential cell network. This approach can be complemented by stochastic silencing of tangential neurons, enabling direct assessment of the functional role of individual tangential neurons in the processing of specific visual motion patterns. Taken together, the findings presented in this thesis suggest that establishing specific activity patterns of tangential cells via stereotyped synaptic connectivity is a key to efficient optic flow-based navigation in Drosophila melanogaster. AU - Pokusaeva, Victoria ID - 12826 SN - 2663 - 337X TI - Neural control of optic flow-based navigation in Drosophila melanogaster ER - TY - JOUR AB - We present a simple algorithm for computing higher-order Delaunay mosaics that works in Euclidean spaces of any finite dimensions. The algorithm selects the vertices of the order-k mosaic from incrementally constructed lower-order mosaics and uses an algorithm for weighted first-order Delaunay mosaics as a black-box to construct the order-k mosaic from its vertices. Beyond this black-box, the algorithm uses only combinatorial operations, thus facilitating easy implementation. We extend this algorithm to compute higher-order α-shapes and provide open-source implementations. We present experimental results for properties of higher-order Delaunay mosaics of random point sets. AU - Edelsbrunner, Herbert AU - Osang, Georg F ID - 12086 JF - Algorithmica SN - 0178-4617 TI - A simple algorithm for higher-order Delaunay mosaics and alpha shapes VL - 85 ER - TY - JOUR AB - We study ergodic decompositions of Dirichlet spaces under intertwining via unitary order isomorphisms. We show that the ergodic decomposition of a quasi-regular Dirichlet space is unique up to a unique isomorphism of the indexing space. Furthermore, every unitary order isomorphism intertwining two quasi-regular Dirichlet spaces is decomposable over their ergodic decompositions up to conjugation via an isomorphism of the corresponding indexing spaces. AU - Dello Schiavo, Lorenzo AU - Wirth, Melchior ID - 12104 IS - 1 JF - Journal of Evolution Equations SN - 1424-3199 TI - Ergodic decompositions of Dirichlet forms under order isomorphisms VL - 23 ER - TY - JOUR AB - The Indian summer monsoon rainfall (ISMR) has been declining since the 1950s. However, since 2002 it is reported to have revived. For these observed changes in the ISMR, several explanations have been reported. Among these explanations, however, the role of the eastern equatorial Indian Ocean (EEIO) is missing despite being one of the warmest regions in the Indian Ocean, and monotonously warming. A recent study reported that EEIO warming impacts the rainfall over northern India. Here we report that warming in the EEIO weakens the low-level Indian summer monsoon circulation and reduces ISMR. A warm EEIO drives easterly winds in the Indo–Pacific sector as a Gill response. The warm EEIO also enhances nocturnal convection offshore the western coast of Sumatra. The latent heating associated with the increased convection augments the Gill response and the resultant circulation opposes the monsoon low-level circulation and weakens the seasonal rainfall. AU - Goswami, Bidyut B ID - 11434 JF - Climate Dynamics SN - 0930-7575 TI - Role of the eastern equatorial Indian Ocean warming in the Indian summer monsoon rainfall trend VL - 60 ER -