@article{434, abstract = {In this paper, we present a formal model-driven design approach to establish a safety-assured implementation of multifunction vehicle bus controller (MVBC), which controls the data transmission among the devices of the vehicle. First, the generic models and safety requirements described in International Electrotechnical Commission Standard 61375 are formalized as time automata and timed computation tree logic formulas, respectively. With model checking tool Uppaal, we verify whether or not the constructed timed automata satisfy the formulas and several logic inconsistencies in the original standard are detected and corrected. Then, we apply the code generation tool Times to generate C code from the verified model, which is later synthesized into a real MVBC chip, with some handwriting glue code. Furthermore, the runtime verification tool RMOR is applied on the integrated code, to verify some safety requirements that cannot be formalized on the timed automata. For evaluation, we compare the proposed approach with existing MVBC design methods, such as BeagleBone, Galsblock, and Simulink. Experiments show that more ambiguousness or bugs in the standard are detected during Uppaal verification, and the generated code of Times outperforms the C code generated by others in terms of the synthesized binary code size. The errors in the standard have been confirmed and the resulting MVBC has been deployed in the real train communication network.}, author = {Jiang, Yu and Liu, Han and Song, Huobing and Kong, Hui and Wang, Rui and Guan, Yong and Sha, Lui}, journal = {IEEE Transactions on Intelligent Transportation Systems}, number = {10}, pages = {3320 -- 3333}, publisher = {IEEE}, title = {{Safety-assured model-driven design of the multifunction vehicle bus controller}}, doi = {10.1109/TITS.2017.2778077}, volume = {19}, year = {2018}, } @article{162, abstract = {Facial shape is the basis for facial recognition and categorization. Facial features reflect the underlying geometry of the skeletal structures. Here, we reveal that cartilaginous nasal capsule (corresponding to upper jaw and face) is shaped by signals generated by neural structures: brain and olfactory epithelium. Brain-derived Sonic Hedgehog (SHH) enables the induction of nasal septum and posterior nasal capsule, whereas the formation of a capsule roof is controlled by signals from the olfactory epithelium. Unexpectedly, the cartilage of the nasal capsule turned out to be important for shaping membranous facial bones during development. This suggests that conserved neurosensory structures could benefit from protection and have evolved signals inducing cranial cartilages encasing them. Experiments with mutant mice revealed that the genomic regulatory regions controlling production of SHH in the nervous system contribute to facial cartilage morphogenesis, which might be a mechanism responsible for the adaptive evolution of animal faces and snouts.}, author = {Kaucka, Marketa and Petersen, Julian and Tesarova, Marketa and Szarowska, Bara and Kastriti, Maria and Xie, Meng and Kicheva, Anna and Annusver, Karl and Kasper, Maria and Symmons, Orsolya and Pan, Leslie and Spitz, Francois and Kaiser, Jozef and Hovorakova, Maria and Zikmund, Tomas and Sunadome, Kazunori and Matise, Michael P and Wang, Hui and Marklund, Ulrika and Abdo, Hind and Ernfors, Patrik and Maire, Pascal and Wurmser, Maud and Chagin, Andrei S and Fried, Kaj and Adameyko, Igor}, journal = {eLife}, publisher = {eLife Sciences Publications}, title = {{Signals from the brain and olfactory epithelium control shaping of the mammalian nasal capsule cartilage}}, doi = {10.7554/eLife.34465}, volume = {7}, year = {2018}, } @inproceedings{302, abstract = {At ITCS 2013, Mahmoody, Moran and Vadhan [MMV13] introduce and construct publicly verifiable proofs of sequential work, which is a protocol for proving that one spent sequential computational work related to some statement. The original motivation for such proofs included non-interactive time-stamping and universally verifiable CPU benchmarks. A more recent application, and our main motivation, are blockchain designs, where proofs of sequential work can be used – in combination with proofs of space – as a more ecological and economical substitute for proofs of work which are currently used to secure Bitcoin and other cryptocurrencies. The construction proposed by [MMV13] is based on a hash function and can be proven secure in the random oracle model, or assuming inherently sequential hash-functions, which is a new standard model assumption introduced in their work. In a proof of sequential work, a prover gets a “statement” χ, a time parameter N and access to a hash-function H, which for the security proof is modelled as a random oracle. Correctness requires that an honest prover can make a verifier accept making only N queries to H, while soundness requires that any prover who makes the verifier accept must have made (almost) N sequential queries to H. Thus a solution constitutes a proof that N time passed since χ was received. Solutions must be publicly verifiable in time at most polylogarithmic in N. The construction of [MMV13] is based on “depth-robust” graphs, and as a consequence has rather poor concrete parameters. But the major drawback is that the prover needs not just N time, but also N space to compute a proof. In this work we propose a proof of sequential work which is much simpler, more efficient and achieves much better concrete bounds. Most importantly, the space required can be as small as log (N) (but we get better soundness using slightly more memory than that). An open problem stated by [MMV13] that our construction does not solve either is achieving a “unique” proof, where even a cheating prover can only generate a single accepting proof. This property would be extremely useful for applications to blockchains.}, author = {Cohen, Bram and Pietrzak, Krzysztof Z}, location = {Tel Aviv, Israel}, pages = {451 -- 467}, publisher = {Springer}, title = {{Simple proofs of sequential work}}, doi = {10.1007/978-3-319-78375-8_15}, volume = {10821}, year = {2018}, } @article{31, abstract = {Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network and, thus, depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes in the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer population models of interacting neurons that collectively encode stimulus information. The key to disentangling intrinsic from extrinsic correlations is to infer the couplings between neurons separately from the encoding model and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach in retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus.}, author = {Ferrari, Ulisse and Deny, Stephane and Chalk, Matthew J and Tkacik, Gasper and Marre, Olivier and Mora, Thierry}, issn = {24700045}, journal = {Physical Review E}, number = {4}, publisher = {American Physical Society}, title = {{Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons}}, doi = {10.1103/PhysRevE.98.042410}, volume = {98}, year = {2018}, } @article{64, abstract = {Tropical geometry, an established field in pure mathematics, is a place where string theory, mirror symmetry, computational algebra, auction theory, and so forth meet and influence one another. In this paper, we report on our discovery of a tropical model with self-organized criticality (SOC) behavior. Our model is continuous, in contrast to all known models of SOC, and is a certain scaling limit of the sandpile model, the first and archetypical model of SOC. We describe how our model is related to pattern formation and proportional growth phenomena and discuss the dichotomy between continuous and discrete models in several contexts. Our aim in this context is to present an idealized tropical toy model (cf. Turing reaction-diffusion model), requiring further investigation.}, author = {Kalinin, Nikita and Guzmán Sáenz, Aldo and Prieto, Y and Shkolnikov, Mikhail and Kalinina, V and Lupercio, Ernesto}, issn = {00278424}, journal = {PNAS: Proceedings of the National Academy of Sciences of the United States of America}, number = {35}, pages = {E8135 -- E8142}, publisher = {National Academy of Sciences}, title = {{Self-organized criticality and pattern emergence through the lens of tropical geometry}}, doi = {10.1073/pnas.1805847115}, volume = {115}, year = {2018}, }