@article{11995,
  abstract     = {G protein-coupled receptors (GPCRs) regulate processes ranging from immune responses to neuronal signaling. However, ligands for many GPCRs remain unknown, suffer from off-target effects or have poor bioavailability. Additionally, dissecting cell type-specific responses is challenging when the same GPCR is expressed on different cells within a tissue. Here, we overcome these limitations by engineering DREADD-based GPCR chimeras that bind clozapine-N-oxide and mimic a GPCR-of-interest. We show that chimeric DREADD-β2AR triggers responses comparable to β2AR on second messenger and kinase activity, post-translational modifications, and protein-protein interactions. Moreover, we successfully recapitulate β2AR-mediated filopodia formation in microglia, an immune cell capable of driving central nervous system inflammation. When dissecting microglial inflammation, we included two additional DREADD-based chimeras mimicking microglia-enriched GPR65 and GPR109A. DREADD-β2AR and DREADD-GPR65 modulate the inflammatory response with high similarity to endogenous β2AR, while DREADD-GPR109A shows no impact. Our DREADD-based approach allows investigation of cell type-dependent pathways without known endogenous ligands.},
  author       = {Schulz, Rouven and Korkut, Medina and Venturino, Alessandro and Colombo, Gloria and Siegert, Sandra},
  issn         = {2041-1723},
  journal      = {Nature Communications},
  publisher    = {Springer Nature},
  title        = {{Chimeric GPCRs mimic distinct signaling pathways and modulate microglia responses}},
  doi          = {10.1038/s41467-022-32390-1},
  volume       = {13},
  year         = {2022},
}

@article{10564,
  abstract     = {We study a class of polaron-type Hamiltonians with sufficiently regular form factor in the interaction term. We investigate the strong-coupling limit of the model, and prove suitable bounds on the ground state energy as a function of the total momentum of the system. These bounds agree with the semiclassical approximation to leading order. The latter corresponds here to the situation when the particle undergoes harmonic motion in a potential well whose frequency is determined by the corresponding Pekar functional. We show that for all such models the effective mass diverges in the strong coupling limit, in all spatial dimensions. Moreover, for the case when the phonon dispersion relation grows at least linearly with momentum, the bounds result in an asymptotic formula for the effective mass quotient, a quantity generalizing the usual notion of the effective mass. This asymptotic form agrees with the semiclassical Landau–Pekar formula and can be regarded as the first rigorous confirmation, in a slightly weaker sense than usually considered, of the validity of the semiclassical formula for the effective mass.},
  author       = {Mysliwy, Krzysztof and Seiringer, Robert},
  issn         = {1572-9613},
  journal      = {Journal of Statistical Physics},
  number       = {1},
  publisher    = {Springer Nature},
  title        = {{Polaron models with regular interactions at strong coupling}},
  doi          = {10.1007/s10955-021-02851-w},
  volume       = {186},
  year         = {2022},
}

@phdthesis{11945,
  abstract     = {G protein-coupled receptors (GPCRs) respond to specific ligands and regulate multiple processes ranging from cell growth and immune responses to neuronal signal transmission. However, ligands for many GPCRs remain unknown, suffer from off-target effects or have poor bioavailability. Additional challenges exist to dissect cell-type specific responses when the same GPCR is expressed on several cell types within the body. Here, we overcome these limitations by engineering DREADD-based GPCR chimeras that selectively bind their agonist clozapine-N-oxide (CNO) and mimic a GPCR-of-interest in a desired cell type.
We validated our approach with β2-adrenergic receptor (β2AR/ADRB2) and show that our chimeric DREADD-β2AR triggers comparable responses on second messenger and kinase activity, post-translational modifications, and protein-protein interactions. Since β2AR is also enriched in microglia, which can drive inflammation in the central nervous system, we expressed chimeric DREADD-β2AR in primary microglia and successfully recapitulate β2AR-mediated filopodia formation through CNO stimulation. To dissect the role of selected GPCRs during microglial inflammation, we additionally generated DREADD-based chimeras for microglia-enriched GPR65 and GPR109A/HCAR2. In a microglia cell line, DREADD-β2AR and DREADD-GPR65 both modulated the inflammatory response with a similar profile as endogenously expressed β2AR, while DREADD-GPR109A showed no impact.
Our DREADD-based approach provides the means to obtain mechanistic and functional insights into GPCR signaling on a cell-type specific level.},
  author       = {Schulz, Rouven},
  issn         = {2663-337X},
  pages        = {133},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Chimeric G protein-coupled receptors mimic distinct signaling pathways and modulate microglia function}},
  doi          = {10.15479/at:ista:11945},
  year         = {2022},
}

@phdthesis{11626,
  abstract     = {Plant growth and development is well known to be both, flexible and dynamic. The high capacity for post-embryonic organ formation and tissue regeneration requires tightly regulated intercellular communication and coordinated tissue polarization. One of the most important drivers for patterning and polarity in plant development is the phytohormone auxin. Auxin has the unique characteristic to establish polarized channels for its own active directional cell to cell transport. This fascinating phenomenon is called auxin canalization. Those auxin transport channels are characterized by the expression and polar, subcellular localization of PIN auxin efflux carriers. PIN proteins have the ability to dynamically change their localization and auxin itself can affect this by interfering with trafficking. Most of the underlying molecular mechanisms of canalization still remain enigmatic. What is known so far is that canonical auxin signaling is indispensable but also other non-canonical signaling components are thought to play a role. In order to shed light into the mysteries auf auxin canalization this study revisits the branches of auxin signaling in detail. Further a new auxin analogue, PISA, is developed which triggers auxin-like responses but does not directly activate canonical transcriptional auxin signaling. We revisit the direct auxin effect on PIN trafficking where we found that, contradictory to previous observations, auxin is very specifically promoting endocytosis of PIN2 but has no overall effect on endocytosis. Further, we evaluate which cellular processes related to PIN subcellular dynamics are involved in the establishment of auxin conducting channels and the formation of vascular tissue. We are re-evaluating the function of AUXIN BINDING PROTEIN 1 (ABP1) and provide a comprehensive picture about its developmental phneotypes and involvement in auxin signaling and canalization. Lastly, we are focusing on the crosstalk between the hormone strigolactone (SL) and auxin and found that SL is interfering with essentially all processes involved in auxin canalization in a non-transcriptional manner. Lastly we identify a new way of SL perception and signaling which is emanating from mitochondria, is independent of canonical SL signaling and is modulating primary root growth.},
  author       = {Gallei, Michelle C},
  isbn         = {978-3-99078-019-0},
  issn         = {2663-337X},
  pages        = {248},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana}},
  doi          = {10.15479/at:ista:11626},
  year         = {2022},
}

@article{10411,
  abstract     = {The phytohormone auxin is the major growth regulator governing tropic responses including gravitropism. Auxin build-up at the lower side of stimulated shoots promotes cell expansion, whereas in roots it inhibits growth, leading to upward shoot bending and downward root bending, respectively. Yet it remains an enigma how the same signal can trigger such opposite cellular responses. In this review, we discuss several recent unexpected insights into the mechanisms underlying auxin regulation of growth, challenging several existing models. We focus on the divergent mechanisms of apoplastic pH regulation in shoots and roots revisiting the classical Acid Growth Theory and discuss coordinated involvement of multiple auxin signaling pathways. From this emerges a more comprehensive, updated picture how auxin regulates growth.},
  author       = {Li, Lanxin and Gallei, Michelle C and Friml, Jiří},
  issn         = {1360-1385},
  journal      = {Trends in Plant Science},
  number       = {5},
  pages        = {440--449},
  publisher    = {Cell Press},
  title        = {{Bending to auxin: Fast acid growth for tropisms}},
  doi          = {10.1016/j.tplants.2021.11.006},
  volume       = {27},
  year         = {2022},
}

@phdthesis{10799,
  abstract     = {Because of the increasing popularity of machine learning methods, it is becoming important to understand the impact of learned components on automated decision-making systems and to guarantee that their consequences are beneficial to society. In other words, it is necessary to ensure that machine learning is sufficiently trustworthy to be used in real-world applications. This thesis studies two properties of machine learning models that are highly desirable for the
sake of reliability: robustness and fairness. In the first part of the thesis we study the robustness of learning algorithms to training data corruption. Previous work has shown that machine learning models are vulnerable to a range
of training set issues, varying from label noise through systematic biases to worst-case data manipulations. This is an especially relevant problem from a present perspective, since modern machine learning methods are particularly data hungry and therefore practitioners often have to rely on data collected from various external sources, e.g. from the Internet, from app users or via crowdsourcing. Naturally, such sources vary greatly in the quality and reliability of the
data they provide. With these considerations in mind, we study the problem of designing machine learning algorithms that are robust to corruptions in data coming from multiple sources. We show that, in contrast to the case of a single dataset with outliers, successful learning within this model is possible both theoretically and practically, even under worst-case data corruptions. The second part of this thesis deals with fairness-aware machine learning. There are multiple areas where machine learning models have shown promising results, but where careful considerations are required, in order to avoid discrimanative decisions taken by such learned components. Ensuring fairness can be particularly challenging, because real-world training datasets are expected to contain various forms of historical bias that may affect the learning process. In this thesis we show that data corruption can indeed render the problem of achieving fairness impossible, by tightly characterizing the theoretical limits of fair learning under worst-case data manipulations. However, assuming access to clean data, we also show how fairness-aware learning can be made practical in contexts beyond binary classification, in particular in the challenging learning to rank setting.},
  author       = {Konstantinov, Nikola H},
  isbn         = {978-3-99078-015-2},
  issn         = {2663-337X},
  keywords     = {robustness, fairness, machine learning, PAC learning, adversarial learning},
  pages        = {176},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Robustness and fairness in machine learning}},
  doi          = {10.15479/at:ista:10799},
  year         = {2022},
}

@article{10802,
  abstract     = {Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. While many approaches have been developed for training fair models from data, little is known about the robustness of these methods to data corruption. In this work we consider fairness-aware learning under worst-case data manipulations. We show that an adversary can in some situations force any learner to return an overly biased classifier, regardless of the sample size and with or without degrading
accuracy, and that the strength of the excess bias increases for learning problems with underrepresented protected groups in the data. We also prove that our hardness results are tight up to constant factors. To this end, we study two natural learning algorithms that optimize for both accuracy and fairness and show that these algorithms enjoy guarantees that are order-optimal in terms of the corruption ratio and the protected groups frequencies in the large data
limit.},
  author       = {Konstantinov, Nikola H and Lampert, Christoph},
  issn         = {1533-7928},
  journal      = {Journal of Machine Learning Research},
  keywords     = {Fairness, robustness, data poisoning, trustworthy machine learning, PAC learning},
  pages        = {1--60},
  publisher    = {ML Research Press},
  title        = {{Fairness-aware PAC learning from corrupted data}},
  volume       = {23},
  year         = {2022},
}

@phdthesis{10759,
  abstract     = {In this Thesis, I study composite quantum impurities with variational techniques, both inspired by machine learning as well as fully analytic. I supplement this with exploration of other applications of machine learning, in particular artificial neural networks, in many-body physics. In Chapters 3 and 4, I study quasiparticle systems with variational approach. I derive a Hamiltonian describing the angulon quasiparticle in the presence of a magnetic field. I apply analytic variational treatment to this Hamiltonian. Then, I introduce a variational approach for non-additive systems, based on artificial neural networks. I exemplify this approach on the example of the polaron quasiparticle (Fröhlich Hamiltonian). In Chapter 5, I continue using artificial neural networks, albeit in a different setting. I apply artificial neural networks to detect phases from snapshots of two types physical systems. Namely, I study Monte Carlo snapshots of multilayer classical spin models as well as molecular dynamics maps of colloidal systems. The main type of networks that I use here are convolutional neural networks, known for their applicability to image data.},
  author       = {Rzadkowski, Wojciech},
  issn         = {2663-337X},
  pages        = {120},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Analytic and machine learning approaches to composite quantum impurities}},
  doi          = {10.15479/at:ista:10759},
  year         = {2022},
}

@unpublished{11366,
  abstract     = {Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during inference. However, the improved robustness does not
come for free but rather is accompanied by a decrease in overall model accuracy and performance. Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off
but inflict a net loss when measured in holistic robot performance. This work revisits the robustness-accuracy trade-off in robot learning by systematically analyzing if recent advances in robust training methods and theory in
conjunction with adversarial robot learning can make adversarial training suitable for real-world robot applications. We evaluate a wide variety of robot learning tasks ranging from autonomous driving in a high-fidelity environment
amenable to sim-to-real deployment, to mobile robot gesture recognition. Our results demonstrate that, while these techniques make incremental improvements on the trade-off on a relative scale, the negative side-effects caused by
adversarial training still outweigh the improvements by an order of magnitude. We conclude that more substantial advances in robust learning methods are necessary before they can benefit robot learning tasks in practice.},
  author       = {Lechner, Mathias and Amini, Alexander and Rus, Daniela and Henzinger, Thomas A},
  booktitle    = {arXiv},
  title        = {{Revisiting the adversarial robustness-accuracy tradeoff in robot learning}},
  doi          = {10.48550/arXiv.2204.07373},
  year         = {2022},
}

@unpublished{21673,
  abstract     = {When impinging on optical structures or passing in their vicinity, free electrons can spontaneously emit electromagnetic radiation, a phenomenon generally known as cathodoluminescence. Free-electron radiation comes in many guises: Cherenkov, transition, and Smith-Purcell radiation, but also electron scintillation, commonly referred to as incoherent cathodoluminescence. While those effects have been at the heart of many fundamental discoveries and technological developments in high-energy physics in the past century, their recent demonstration in photonic and nanophotonic systems has attracted a lot of attention. Those developments arose from predictions that exploit nanophotonics for novel radiation regimes, now becoming accessible thanks to advances in nanofabrication. In general, the proper design of nanophotonic structures can enable shaping, control, and enhancement of free-electron radiation, for any of the above-mentioned effects. Free-electron radiation in nanophotonics opens the way to promising applications, such as widely-tunable integrated light sources from x-ray to THz frequencies, miniaturized particle accelerators, and highly sensitive high-energy particle detectors. Here, we review the emerging field of free-electron radiation in nanophotonics. We first present a general, unified framework to describe free-electron light-matter interaction in arbitrary nanophotonic systems. We then show how this framework sheds light on the physical underpinnings of many methods in the field used to control and enhance free-electron radiation. Namely, the framework points to the central role played by the photonic eigenmodes in controlling the output properties of free-electron radiation (e.g., frequency, directionality, and polarization). [... see full abstract in paper]},
  author       = {Roques-Carmes, Charles and Kooi, Steven E. and Yang, Yi and Rivera, Nicholas and Keathley, Phillip D. and Joannopoulos, John D. and Johnson, Steven G. and Kaminer, Ido and Berggren, Karl K. and Soljačić, Marin},
  booktitle    = {arXiv},
  title        = {{Free-electron-light interactions in nanophotonics}},
  doi          = {10.48550/arXiv.2208.02368},
  year         = {2022},
}

@misc{14520,
  abstract     = {This dataset comprises all data shown in the figures of the submitted article "Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses" at arxiv.org/abs/2206.14104. Additional raw data are available from the corresponding author on reasonable request.},
  author       = {Zemlicka, Martin and Redchenko, Elena and Peruzzo, Matilda and Hassani, Farid and Trioni, Andrea and Barzanjeh, Shabir and Fink, Johannes M},
  publisher    = {Zenodo},
  title        = {{Compact vacuum gap transmon qubits: Selective and sensitive probes for superconductor surface losses}},
  doi          = {10.5281/ZENODO.8408897},
  year         = {2022},
}

@article{11344,
  abstract     = {Until recently, Shigella and enteroinvasive Escherichia coli were thought to be primate-restricted pathogens. The base of their pathogenicity is the type 3 secretion system (T3SS) encoded by the pINV virulence plasmid, which facilitates host cell invasion and subsequent proliferation. A large family of T3SS effectors, E3 ubiquitin-ligases encoded by the ipaH genes, have a key role in the Shigella pathogenicity through the modulation of cellular ubiquitination that degrades host proteins. However, recent genomic studies identified ipaH genes in the genomes of Escherichia marmotae, a potential marmot pathogen, and an E. coli extracted from fecal samples of bovine calves, suggesting that non-human hosts may also be infected by these strains, potentially pathogenic to humans. We performed a comparative genomic study of the functional repertoires in the ipaH gene family in Shigella and enteroinvasive Escherichia from human and predicted non-human hosts. We found that fewer than half of Shigella genomes had a complete set of ipaH genes, with frequent gene losses and duplications that were not consistent with the species tree and nomenclature. Non-human host IpaH proteins had a diverse set of substrate-binding domains and, in contrast to the Shigella proteins, two variants of the NEL C-terminal domain. Inconsistencies between strains phylogeny and composition of effectors indicate horizontal gene transfer between E. coli adapted to different hosts. These results provide a framework for understanding of ipaH-mediated host-pathogens interactions and suggest a need for a genomic study of fecal samples from diseased animals.},
  author       = {Dranenko, NO and Tutukina, MN and Gelfand, MS and Kondrashov, Fyodor and Bochkareva, Olga},
  issn         = {2045-2322},
  journal      = {Scientific Reports},
  publisher    = {Springer Nature},
  title        = {{Chromosome-encoded IpaH ubiquitin ligases indicate non-human enteroinvasive Escherichia}},
  doi          = {10.1038/s41598-022-10827-3},
  volume       = {12},
  year         = {2022},
}

@phdthesis{12390,
  abstract     = {The scope of this thesis is to study quantum systems exhibiting a continuous symmetry that
is broken on the level of the corresponding effective theory. In particular we are going to
investigate translation-invariant Bose gases in the mean field limit, effectively described by
the Hartree functional, and the Fröhlich Polaron in the regime of strong coupling, effectively
described by the Pekar functional. The latter is a model describing the interaction between a
charged particle and the optical modes of a polar crystal. Regarding the former, we assume in
addition that the particles in the gas are unconfined, and typically we will consider particles
that are subject to an attractive interaction. In both cases the ground state energy of the
Hamiltonian is not a proper eigenvalue due to the underlying translation-invariance, while on
the contrary there exists a whole invariant orbit of minimizers for the corresponding effective
functionals. Both, the absence of proper eigenstates and the broken symmetry of the effective
theory, make the study significantly more involved and it is the content of this thesis to
develop a frameworks which allows for a systematic way to circumvent these issues.
It is a well-established result that the ground state energy of Bose gases in the mean field limit,
as well as the ground state energy of the Fröhlich Polaron in the regime of strong coupling, is
to leading order given by the minimal energy of the corresponding effective theory. As part
of this thesis we identify the sub-leading term in the expansion of the ground state energy,
which can be interpreted as the quantum correction to the classical energy, since the effective
theories under consideration can be seen as classical counterparts.
We are further going to establish an asymptotic expression for the energy-momentum relation
of the Fröhlich Polaron in the strong coupling limit. In the regime of suitably small momenta,
this asymptotic expression agrees with the energy-momentum relation of a free particle having
an effectively increased mass, and we find that this effectively increased mass agrees with the
conjectured value in the physics literature.
In addition we will discuss two unrelated papers written by the author during his stay at ISTA
in the appendix. The first one concerns the realization of anyons, which are quasi-particles
acquiring a non-trivial phase under the exchange of two particles, as molecular impurities.
The second one provides a classification of those vector fields defined on a given manifold
that can be written as the gradient of a given functional with respect to a suitable metric,
provided that some mild smoothness assumptions hold. This classification is subsequently
used to identify those quantum Markov semigroups that can be written as a gradient flow of
the relative entropy.
},
  author       = {Brooks, Morris},
  issn         = {2663-337X},
  pages        = {196},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Translation-invariant quantum systems with effectively broken symmetry}},
  doi          = {10.15479/at:ista:12390},
  year         = {2022},
}

@article{10890,
  abstract     = {Upon the arrival of action potentials at nerve terminals, neurotransmitters are released from synaptic vesicles (SVs) by exocytosis. CaV2.1, 2.2, and 2.3 are the major subunits of the voltage-gated calcium channel (VGCC) responsible for increasing intraterminal calcium levels and triggering SV exocytosis in the central nervous system (CNS) synapses. The two-dimensional analysis of CaV2 distributions using sodium dodecyl sulfate (SDS)-digested freeze-fracture replica labeling (SDS-FRL) has revealed their numbers, densities, and nanoscale clustering patterns in individual presynaptic active zones. The variation in these properties affects the coupling of VGCCs with calcium sensors on SVs, synaptic efficacy, and temporal precision of transmission. In this study, we summarize how the morphological parameters of CaV2 distribution obtained using SDS-FRL differ depending on the different types of synapses and could correspond to functional properties in synaptic transmission.},
  author       = {Eguchi, Kohgaku and Montanaro-Punzengruber, Jacqueline-Claire and Le Monnier, Elodie and Shigemoto, Ryuichi},
  issn         = {1662-5129},
  journal      = {Frontiers in Neuroanatomy},
  publisher    = {Frontiers},
  title        = {{The number and distinct clustering patterns of voltage-gated Calcium channels in nerve terminals}},
  doi          = {10.3389/fnana.2022.846615},
  volume       = {16},
  year         = {2022},
}

@article{11552,
  abstract     = {Rotational dynamics of D2 molecules inside helium nanodroplets is induced by a moderately intense femtosecond pump pulse and measured as a function of time by recording the yield of HeD+ ions, created through strong-field dissociative ionization with a delayed femtosecond probe pulse. The yield oscillates with a period of 185 fs, reflecting field-free rotational wave packet dynamics, and the oscillation persists for more than 500 periods. Within the experimental uncertainty, the rotational constant BHe of the in-droplet D2 molecule, determined by Fourier analysis, is the same as Bgas for an isolated D2 molecule. Our observations show that the D2 molecules inside helium nanodroplets essentially rotate as free D2 molecules.},
  author       = {Qiang, Junjie and Zhou, Lianrong and Lu, Peifen and Lin, Kang and Ma, Yongzhe and Pan, Shengzhe and Lu, Chenxu and Jiang, Wenyu and Sun, Fenghao and Zhang, Wenbin and Li, Hui and Gong, Xiaochun and Averbukh, Ilya Sh and Prior, Yehiam and Schouder, Constant A. and Stapelfeldt, Henrik and Cherepanov, Igor and Lemeshko, Mikhail and Jäger, Wolfgang and Wu, Jian},
  issn         = {1079-7114},
  journal      = {Physical Review Letters},
  number       = {24},
  publisher    = {American Physical Society},
  title        = {{Femtosecond rotational dynamics of D2 molecules in superfluid helium nanodroplets}},
  doi          = {10.1103/PhysRevLett.128.243201},
  volume       = {128},
  year         = {2022},
}

@phdthesis{12358,
  abstract     = {The complex yarn structure of knitted and woven fabrics gives rise to both a mechanical and
visual complexity. The small-scale interactions of yarns colliding with and pulling on each
other result in drastically different large-scale stretching and bending behavior, introducing
anisotropy, curling, and more. While simulating cloth as individual yarns can reproduce this
complexity and match the quality of real fabric, it may be too computationally expensive for
large fabrics. On the other hand, continuum-based approaches do not need to discretize the
cloth at a stitch-level, but it is non-trivial to find a material model that would replicate the
large-scale behavior of yarn fabrics, and they discard the intricate visual detail. In this thesis,
we discuss three methods to try and bridge the gap between small-scale and large-scale yarn
mechanics using numerical homogenization: fitting a continuum model to periodic yarn simulations, adding mechanics-aware yarn detail onto thin-shell simulations, and quantitatively
fitting yarn parameters to physical measurements of real fabric.
To start, we present a method for animating yarn-level cloth effects using a thin-shell solver.
We first use a large number of periodic yarn-level simulations to build a model of the potential
energy density of the cloth, and then use it to compute forces in a thin-shell simulator. The
resulting simulations faithfully reproduce expected effects like the stiffening of woven fabrics
and the highly deformable nature and anisotropy of knitted fabrics at a fraction of the cost of
full yarn-level simulation.
While our thin-shell simulations are able to capture large-scale yarn mechanics, they lack
the rich visual detail of yarn-level simulations. Therefore, we propose a method to animate
yarn-level cloth geometry on top of an underlying deforming mesh in a mechanics-aware
fashion in real time. Using triangle strains to interpolate precomputed yarn geometry, we are
able to reproduce effects such as knit loops tightening under stretching at negligible cost.
Finally, we introduce a methodology for inverse-modeling of yarn-level mechanics of cloth,
based on the mechanical response of fabrics in the real world. We compile a database from
physical tests of several knitted fabrics used in the textile industry spanning diverse physical
properties like stiffness, nonlinearity, and anisotropy. We then develop a system for approximating these mechanical responses with yarn-level cloth simulation, using homogenized
shell models to speed up computation and adding some small-but-necessary extensions to
yarn-level models used in computer graphics.
},
  author       = {Sperl, Georg},
  isbn         = {978-3-99078-020-6},
  issn         = {2663-337X},
  pages        = {138},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting}},
  doi          = {10.15479/at:ista:12103},
  year         = {2022},
}

@inproceedings{10774,
  abstract     = {We study the problem of specifying sequential information-flow properties of systems. Information-flow properties are hyperproperties, as they compare different traces of a system. Sequential information-flow properties can express changes, over time, in the information-flow constraints. For example, information-flow constraints during an initialization phase of a system may be different from information-flow constraints that are required during the operation phase. We formalize several variants of interpreting sequential information-flow constraints, which arise from different assumptions about what can be observed of the system. For this purpose, we introduce a first-order logic, called Hypertrace Logic, with both trace and time quantifiers for specifying linear-time hyperproperties. We prove that HyperLTL, which corresponds to a fragment of Hypertrace Logic with restricted quantifier prefixes, cannot specify the majority of the studied variants of sequential information flow, including all variants in which the transition between sequential phases (such as initialization and operation) happens asynchronously. Our results rely on new equivalences between sets of traces that cannot be distinguished by certain classes of formulas from Hypertrace Logic. This presents a new approach to proving inexpressiveness results for HyperLTL.},
  author       = {Bartocci, Ezio and Ferrere, Thomas and Henzinger, Thomas A and Nickovic, Dejan and Da Costa, Ana Oliveira},
  booktitle    = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  isbn         = {9783030945824},
  issn         = {1611-3349},
  location     = {Philadelphia, PA, United States},
  pages        = {1--19},
  publisher    = {Springer Nature},
  title        = {{Flavors of sequential information flow}},
  doi          = {10.1007/978-3-030-94583-1_1},
  volume       = {13182},
  year         = {2022},
}

@article{11938,
  abstract     = {A matching is compatible to two or more labeled point sets of size n with labels {1, . . . , n} if its straight-line drawing on each of these point sets is crossing-free. We study the maximum number of edges in a matching compatible to two or more labeled point sets in general position in the plane. We show that for any two labeled sets of n points in convex position there exists a compatible matching with ⌊√2n + 1 − 1⌋ edges. More generally, for any ℓ labeled point sets we construct compatible matchings of size Ω(n1/ℓ). As a corresponding upper bound, we use probabilistic arguments to show that for any ℓ given sets of n points there exists a labeling of each set such that the largest compatible matching has O(n2/(ℓ+1)) edges. Finally, we show that Θ(log n) copies of any set of n points are necessary and sufficient for the existence of labelings of these point sets such that any compatible matching consists only of a single edge.},
  author       = {Aichholzer, Oswin and Arroyo Guevara, Alan M and Masárová, Zuzana and Parada, Irene and Perz, Daniel and Pilz, Alexander and Tkadlec, Josef and Vogtenhuber, Birgit},
  issn         = {1526-1719},
  journal      = {Journal of Graph Algorithms and Applications},
  number       = {2},
  pages        = {225--240},
  publisher    = {Brown University},
  title        = {{On compatible matchings}},
  doi          = {10.7155/jgaa.00591},
  volume       = {26},
  year         = {2022},
}

@phdthesis{11362,
  abstract     = {Deep learning has enabled breakthroughs in challenging computing problems and has emerged as the standard problem-solving tool for computer vision and natural language processing tasks.
One exception to this trend is safety-critical tasks where robustness and resilience requirements contradict the black-box nature of neural networks. 
To deploy deep learning methods for these tasks, it is vital to provide guarantees on neural network agents' safety and robustness criteria. 
This can be achieved by developing formal verification methods to verify the safety and robustness properties of neural networks.

Our goal is to design, develop and assess safety verification methods for neural networks to improve their reliability and trustworthiness in real-world applications.
This thesis establishes techniques for the verification of compressed and adversarially trained models as well as the design of novel neural networks for verifiably safe decision-making.

First, we establish the problem of verifying quantized neural networks. Quantization is a technique that trades numerical precision for the computational efficiency of running a neural network and is widely adopted in industry.
We show that neglecting the reduced precision when verifying a neural network can lead to wrong conclusions about the robustness and safety of the network, highlighting that novel techniques for quantized network verification are necessary. We introduce several bit-exact verification methods explicitly designed for quantized neural networks and experimentally confirm on realistic networks that the network's robustness and other formal properties are affected by the quantization.

Furthermore, we perform a case study providing evidence that adversarial training, a standard technique for making neural networks more robust, has detrimental effects on the network's performance. This robustness-accuracy tradeoff has been studied before regarding the accuracy obtained on classification datasets where each data point is independent of all other data points. On the other hand, we investigate the tradeoff empirically in robot learning settings where a both, a high accuracy and a high robustness, are desirable.
Our results suggest that the negative side-effects of adversarial training outweigh its robustness benefits in practice.

Finally, we consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with systems over the infinite time horizon. Bayesian neural networks are probabilistic models for learning uncertainties in the data and are therefore often used on robotic and healthcare applications where data is inherently stochastic.
We introduce a method for recalibrating Bayesian neural networks so that they yield probability distributions over safe decisions only.
Our method learns a safety certificate that guarantees safety over the infinite time horizon to determine which decisions are safe in every possible state of the system.
We demonstrate the effectiveness of our approach on a series of reinforcement learning benchmarks.},
  author       = {Lechner, Mathias},
  isbn         = {978-3-99078-017-6},
  keywords     = {neural networks, verification, machine learning},
  pages        = {124},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Learning verifiable representations}},
  doi          = {10.15479/at:ista:11362},
  year         = {2022},
}

@article{21638,
  abstract     = {We introduce end-to-end inverse design for multi-channel imaging, in which a nanophotonic frontend is optimized in conjunction with an image-processing backend to extract depth, spectral and polarization channels from a single monochrome image. Unlike diffractive optics, we show that subwavelength-scale “metasurface” designs can easily distinguish similar wavelength and polarization inputs. The proposed technique integrates a single-layer metasurface frontend with an efficient Tikhonov reconstruction backend, without any additional optics except a grayscale sensor. Our method yields multi-channel imaging by spontaneous demultiplexing: the metaoptics front-end separates different channels into distinct spatial domains whose locations on the sensor are optimally discovered by the inverse-design algorithm. We present large-area metasurface designs, compatible with standard lithography, for multi-spectral imaging, depth-spectral imaging, and “all-in-one” spectro-polarimetric-depth imaging with robust reconstruction performance (≲ 10% error with 1% detector noise). In contrast to neural networks, our framework is physically interpretable and does not require large training sets. It can be used to reconstruct arbitrary three-dimensional scenes with full multi-wavelength spectra and polarization textures.},
  author       = {Lin, Zin and Pestourie, Raphaël and Roques-Carmes, Charles and Li, Zhaoyi and Capasso, Federico and Soljačić, Marin and Johnson, Steven G.},
  issn         = {1094-4087},
  journal      = {Optics Express},
  number       = {16},
  pages        = {28358--28370},
  publisher    = {Optica Publishing Group},
  title        = {{End-to-end metasurface inverse design for single-shot multi-channel imaging}},
  doi          = {10.1364/oe.449985},
  volume       = {30},
  year         = {2022},
}