@inproceedings{9645, abstract = {We consider the fundamental problem of reachability analysis over imperative programs with real variables. Previous works that tackle reachability are either unable to handle programs consisting of general loops (e.g. symbolic execution), or lack completeness guarantees (e.g. abstract interpretation), or are not automated (e.g. incorrectness logic). In contrast, we propose a novel approach for reachability analysis that can handle general and complex loops, is complete, and can be entirely automated for a wide family of programs. Through the notion of Inductive Reachability Witnesses (IRWs), our approach extends ideas from both invariant generation and termination to reachability analysis. We first show that our IRW-based approach is sound and complete for reachability analysis of imperative programs. Then, we focus on linear and polynomial programs and develop automated methods for synthesizing linear and polynomial IRWs. In the linear case, we follow the well-known approaches using Farkas' Lemma. Our main contribution is in the polynomial case, where we present a push-button semi-complete algorithm. We achieve this using a novel combination of classical theorems in real algebraic geometry, such as Putinar's Positivstellensatz and Hilbert's Strong Nullstellensatz. Finally, our experimental results show we can prove complex reachability objectives over various benchmarks that were beyond the reach of previous methods.}, author = {Asadi, Ali and Chatterjee, Krishnendu and Fu, Hongfei and Goharshady, Amir Kafshdar and Mahdavi, Mohammad}, booktitle = {Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation}, isbn = {9781450383912}, location = {Online}, pages = {772--787}, publisher = {Association for Computing Machinery}, title = {{Polynomial reachability witnesses via Stellensätze}}, doi = {10.1145/3453483.3454076}, year = {2021}, } @inproceedings{9646, abstract = {We consider the fundamental problem of deriving quantitative bounds on the probability that a given assertion is violated in a probabilistic program. We provide automated algorithms that obtain both lower and upper bounds on the assertion violation probability. The main novelty of our approach is that we prove new and dedicated fixed-point theorems which serve as the theoretical basis of our algorithms and enable us to reason about assertion violation bounds in terms of pre and post fixed-point functions. To synthesize such fixed-points, we devise algorithms that utilize a wide range of mathematical tools, including repulsing ranking supermartingales, Hoeffding's lemma, Minkowski decompositions, Jensen's inequality, and convex optimization. On the theoretical side, we provide (i) the first automated algorithm for lower-bounds on assertion violation probabilities, (ii) the first complete algorithm for upper-bounds of exponential form in affine programs, and (iii) provably and significantly tighter upper-bounds than the previous approaches. On the practical side, we show our algorithms can handle a wide variety of programs from the literature and synthesize bounds that are remarkably tighter than previous results, in some cases by thousands of orders of magnitude.}, author = {Wang, Jinyi and Sun, Yican and Fu, Hongfei and Chatterjee, Krishnendu and Goharshady, Amir Kafshdar}, booktitle = {Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation}, isbn = {9781450383912}, location = {Online}, pages = {1171--1186}, publisher = {Association for Computing Machinery}, title = {{Quantitative analysis of assertion violations in probabilistic programs}}, doi = {10.1145/3453483.3454102}, year = {2021}, } @article{9647, abstract = {Gene expression is regulated by the set of transcription factors (TFs) that bind to the promoter. The ensuing regulating function is often represented as a combinational logic circuit, where output (gene expression) is determined by current input values (promoter bound TFs) only. However, the simultaneous arrival of TFs is a strong assumption, since transcription and translation of genes introduce intrinsic time delays and there is no global synchronisation among the arrival times of different molecular species at their targets. We present an experimentally implementable genetic circuit with two inputs and one output, which in the presence of small delays in input arrival, exhibits qualitatively distinct population-level phenotypes, over timescales that are longer than typical cell doubling times. From a dynamical systems point of view, these phenotypes represent long-lived transients: although they converge to the same value eventually, they do so after a very long time span. The key feature of this toy model genetic circuit is that, despite having only two inputs and one output, it is regulated by twenty-three distinct DNA-TF configurations, two of which are more stable than others (DNA looped states), one promoting and another blocking the expression of the output gene. Small delays in input arrival time result in a majority of cells in the population quickly reaching the stable state associated with the first input, while exiting of this stable state occurs at a slow timescale. In order to mechanistically model the behaviour of this genetic circuit, we used a rule-based modelling language, and implemented a grid-search to find parameter combinations giving rise to long-lived transients. Our analysis shows that in the absence of feedback, there exist path-dependent gene regulatory mechanisms based on the long timescale of transients. The behaviour of this toy model circuit suggests that gene regulatory networks can exploit event timing to create phenotypes, and it opens the possibility that they could use event timing to memorise events, without regulatory feedback. The model reveals the importance of (i) mechanistically modelling the transitions between the different DNA-TF states, and (ii) employing transient analysis thereof.}, author = {Petrov, Tatjana and Igler, Claudia and Sezgin, Ali and Henzinger, Thomas A and Guet, Calin C}, issn = {0304-3975}, journal = {Theoretical Computer Science}, pages = {1--16}, publisher = {Elsevier}, title = {{Long lived transients in gene regulation}}, doi = {10.1016/j.tcs.2021.05.023}, volume = {893}, year = {2021}, } @article{9656, abstract = {Tropisms, growth responses to environmental stimuli such as light or gravity, are spectacular examples of adaptive plant development. The plant hormone auxin serves as a major coordinative signal. The PIN auxin exporters, through their dynamic polar subcellular localizations, redirect auxin fluxes in response to environmental stimuli and the resulting auxin gradients across organs underly differential cell elongation and bending. In this review, we discuss recent advances concerning regulations of PIN polarity during tropisms, focusing on PIN phosphorylation and trafficking. We also cover how environmental cues regulate PIN actions during tropisms, and a crucial role of auxin feedback on PIN polarity during bending termination. Finally, the interactions between different tropisms are reviewed to understand plant adaptive growth in the natural environment.}, author = {Han, Huibin and Adamowski, Maciek and Qi, Linlin and Alotaibi, SS and Friml, Jiří}, issn = {1469-8137}, journal = {New Phytologist}, number = {2}, pages = {510--522}, publisher = {Wiley}, title = {{PIN-mediated polar auxin transport regulations in plant tropic responses}}, doi = {10.1111/nph.17617}, volume = {232}, year = {2021}, } @article{9657, abstract = {To overcome nitrogen deficiency, legume roots establish symbiotic interactions with nitrogen-fixing rhizobia that is fostered in specialized organs (nodules). Similar to other organs, nodule formation is determined by a local maximum of the phytohormone auxin at the primordium site. However, how auxin regulates nodule development remains poorly understood. Here, we found that in soybean, (Glycine max), dynamic auxin transport driven by PIN-FORMED (PIN) transporter GmPIN1 is involved in nodule primordium formation. GmPIN1 was specifically expressed in nodule primordium cells and GmPIN1 was polarly localized in these cells. Two nodulation regulators, (iso)flavonoids trigger expanded distribution of GmPIN1b to root cortical cells, and cytokinin rearranges GmPIN1b polarity. Gmpin1abc triple mutants generated with CRISPR-Cas9 showed impaired establishment of auxin maxima in nodule meristems and aberrant divisions in the nodule primordium cells. Moreover, overexpression of GmPIN1 suppressed nodule primordium initiation. GmPIN9d, an ortholog of Arabidopsis thaliana PIN2, acts together with GmPIN1 later in nodule development to acropetally transport auxin in vascular bundles, fine-tuning the auxin supply for nodule enlargement. Our findings reveal how PIN-dependent auxin transport modulates different aspects of soybean nodule development and suggest that establishment of auxin gradient is a prerequisite for the proper interaction between legumes and rhizobia.}, author = {Gao, Z and Chen, Z and Cui, Y and Ke, M and Xu, H and Xu, Q and Chen, J and Li, Y and Huang, L and Zhao, H and Huang, D and Mai, S and Xu, T and Liu, X and Li, S and Guan, Y and Yang, W and Friml, Jiří and Petrášek, J and Zhang, J and Chen, X}, issn = {1532-298x}, journal = {Plant Cell}, number = {9}, pages = {2981–3003}, publisher = {American Society of Plant Biologists}, title = {{GmPIN-dependent polar auxin transport is involved in soybean nodule development}}, doi = {10.1093/plcell/koab183}, volume = {33}, year = {2021}, } @article{9669, abstract = {The set of known stable phases of water may not be complete, and some of the phase boundaries between them are fuzzy. Starting from liquid water and a comprehensive set of 50 ice structures, we compute the phase diagram at three hybrid density-functional-theory levels of approximation, accounting for thermal and nuclear fluctuations as well as proton disorder. Such calculations are only made tractable because we combine machine-learning methods and advanced free-energy techniques. The computed phase diagram is in qualitative agreement with experiment, particularly at pressures ≲ 8000 bar, and the discrepancy in chemical potential is comparable with the subtle uncertainties introduced by proton disorder and the spread between the three hybrid functionals. None of the hypothetical ice phases considered is thermodynamically stable in our calculations, suggesting the completeness of the experimental water phase diagram in the region considered. Our work demonstrates the feasibility of predicting the phase diagram of a polymorphic system from first principles and provides a thermodynamic way of testing the limits of quantum-mechanical calculations.}, author = {Reinhardt, Aleks and Cheng, Bingqing}, issn = {2041-1723}, journal = {Nature Communications}, number = {1}, publisher = {Springer Nature}, title = {{Quantum-mechanical exploration of the phase diagram of water}}, doi = {10.1038/s41467-020-20821-w}, volume = {12}, year = {2021}, } @inproceedings{9678, abstract = {We introduce a new graph problem, the token dropping game, and we show how to solve it efficiently in a distributed setting. We use the token dropping game as a tool to design an efficient distributed algorithm for stable orientations and more generally for locally optimal semi-matchings. The prior work by Czygrinow et al. (DISC 2012) finds a stable orientation in O(Δ^5) rounds in graphs of maximum degree Δ, while we improve it to O(Δ^4) and also prove a lower bound of Ω(Δ). For the more general problem of locally optimal semi-matchings, the prior upper bound is O(S^5) and our new algorithm runs in O(C · S^4) rounds, which is an improvement for C = o(S); here C and S are the maximum degrees of customers and servers, respectively.}, author = {Brandt, Sebastian and Keller, Barbara and Rybicki, Joel and Suomela, Jukka and Uitto, Jara}, booktitle = {Annual ACM Symposium on Parallelism in Algorithms and Architectures}, isbn = {9781450380706}, location = { Virtual Event, United States}, pages = {129--139}, title = {{Efficient load-balancing through distributed token dropping}}, doi = {10.1145/3409964.3461785}, year = {2021}, } @article{9679, abstract = {The relative motion of three impenetrable particles on a ring, in our case two identical fermions and one impurity, is isomorphic to a triangular quantum billiard. Depending on the ratio κ of the impurity and fermion masses, the billiards can be integrable or non-integrable (also referred to in the main text as chaotic). To set the stage, we first investigate the energy level distributions of the billiards as a function of 1/κ ∈ [0, 1] and find no evidence of integrable cases beyond the limiting values 1/κ = 1 and 1/κ = 0. Then, we use machine learning tools to analyze properties of probability distributions of individual quantum states. We find that convolutional neural networks can correctly classify integrable and non-integrable states. The decisive features of the wave functions are the normalization and a large number of zero elements, corresponding to the existence of a nodal line. The network achieves typical accuracies of 97%, suggesting that machine learning tools can be used to analyze and classify the morphology of probability densities obtained in theory or experiment.}, author = {Huber, David and Marchukov, Oleksandr V. and Hammer, Hans Werner and Volosniev, Artem}, issn = {13672630}, journal = {New Journal of Physics}, number = {6}, publisher = {IOP Publishing}, title = {{Morphology of three-body quantum states from machine learning}}, doi = {10.1088/1367-2630/ac0576}, volume = {23}, year = {2021}, } @unpublished{9695, abstract = {Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure. When assessing the similarity between data points, one can build various distance measures using subsets of these features. Using the fewest features but still retaining sufficient information about the system is crucial in many statistical learning approaches, particularly when data are sparse. We introduce a statistical test that can assess the relative information retained when using two different distance measures, and determine if they are equivalent, independent, or if one is more informative than the other. This in turn allows finding the most informative distance measure out of a pool of candidates. The approach is applied to find the most relevant policy variables for controlling the Covid-19 epidemic and to find compact yet informative representations of atomic structures, but its potential applications are wide ranging in many branches of science.}, author = {Glielmo, Aldo and Zeni, Claudio and Cheng, Bingqing and Csanyi, Gabor and Laio, Alessandro}, booktitle = {arXiv}, title = {{Ranking the information content of distance measures}}, year = {2021}, } @unpublished{9696, abstract = {Most water in the universe may be superionic, and its thermodynamic and transport properties are crucial for planetary science but difficult to probe experimentally or theoretically. We use machine learning and free energy methods to overcome the limitations of quantum mechanical simulations, and characterize hydrogen diffusion, superionic transitions, and phase behaviors of water at extreme conditions. We predict that a close-packed superionic phase with mixed stacking is stable over a wide temperature and pressure range, while a body-centered cubic phase is only thermodynamically stable in a small window but is kinetically favored. Our phase boundaries, which are consistent with the existing-albeit scarce-experimental observations, help resolve the fractions of insulating ice, different superionic phases, and liquid water inside of ice giants.}, author = {Cheng, Bingqing and Bethkenhagen, Mandy and Pickard, Chris J. and Hamel, Sebastien}, booktitle = {arXiv}, title = {{Predicting the phase behaviors of superionic water at planetary conditions}}, year = {2021}, } @article{9698, abstract = {Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We then follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.}, author = {Keith, John A. and Valentin Vassilev-Galindo, Valentin and Cheng, Bingqing and Chmiela, Stefan and Gastegger, Michael and Müller, Klaus-Robert and Tkatchenko, Alexandre}, issn = {1520-6890}, journal = {Chemical Reviews}, number = {16}, pages = {9816--9872}, publisher = {American Chemical Society}, title = {{Combining machine learning and computational chemistry for predictive insights into chemical systems}}, doi = {10.1021/acs.chemrev.1c00107}, volume = {121}, year = {2021}, } @phdthesis{9728, abstract = {Most real-world flows are multiphase, yet we know little about them compared to their single-phase counterparts. Multiphase flows are more difficult to investigate as their dynamics occur in large parameter space and involve complex phenomena such as preferential concentration, turbulence modulation, non-Newtonian rheology, etc. Over the last few decades, experiments in particle-laden flows have taken a back seat in favour of ever-improving computational resources. However, computers are still not powerful enough to simulate a real-world fluid with millions of finite-size particles. Experiments are essential not only because they offer a reliable way to investigate real-world multiphase flows but also because they serve to validate numerical studies and steer the research in a relevant direction. In this work, we have experimentally investigated particle-laden flows in pipes, and in particular, examined the effect of particles on the laminar-turbulent transition and the drag scaling in turbulent flows. For particle-laden pipe flows, an earlier study [Matas et al., 2003] reported how the sub-critical (i.e., hysteretic) transition that occurs via localised turbulent structures called puffs is affected by the addition of particles. In this study, in addition to this known transition, we found a super-critical transition to a globally fluctuating state with increasing particle concentration. At the same time, the Newtonian-type transition via puffs is delayed to larger Reynolds numbers. At an even higher concentration, only the globally fluctuating state is found. The dynamics of particle-laden flows are hence determined by two competing instabilities that give rise to three flow regimes: Newtonian-type turbulence at low, a particle-induced globally fluctuating state at high, and a coexistence state at intermediate concentrations. The effect of particles on turbulent drag is ambiguous, with studies reporting drag reduction, no net change, and even drag increase. The ambiguity arises because, in addition to particle concentration, particle shape, size, and density also affect the net drag. Even similar particles might affect the flow dissimilarly in different Reynolds number and concentration ranges. In the present study, we explored a wide range of both Reynolds number and concentration, using spherical as well as cylindrical particles. We found that the spherical particles do not reduce drag while the cylindrical particles are drag-reducing within a specific Reynolds number interval. The interval strongly depends on the particle concentration and the relative size of the pipe and particles. Within this interval, the magnitude of drag reduction reaches a maximum. These drag reduction maxima appear to fall onto a distinct power-law curve irrespective of the pipe diameter and particle concentration, and this curve can be considered as the maximum drag reduction asymptote for a given fibre shape. Such an asymptote is well known for polymeric flows but had not been identified for particle-laden flows prior to this work.}, author = {Agrawal, Nishchal}, issn = {2663-337X}, keywords = {Drag Reduction, Transition to Turbulence, Multiphase Flows, particle Laden Flows, Complex Flows, Experiments, Fluid Dynamics}, pages = {118}, publisher = {Institute of Science and Technology Austria}, title = {{Transition to turbulence and drag reduction in particle-laden pipe flows}}, doi = {10.15479/at:ista:9728}, year = {2021}, } @phdthesis{9733, abstract = {This thesis is the result of the research carried out by the author during his PhD at IST Austria between 2017 and 2021. It mainly focuses on the Fröhlich polaron model, specifically to its regime of strong coupling. This model, which is rigorously introduced and discussed in the introduction, has been of great interest in condensed matter physics and field theory for more than eighty years. It is used to describe an electron interacting with the atoms of a solid material (the strength of this interaction is modeled by the presence of a coupling constant α in the Hamiltonian of the system). The particular regime examined here, which is mathematically described by considering the limit α →∞, displays many interesting features related to the emergence of classical behavior, which allows for a simplified effective description of the system under analysis. The properties, the range of validity and a quantitative analysis of the precision of such classical approximations are the main object of the present work. We specify our investigation to the study of the ground state energy of the system, its dynamics and its effective mass. For each of these problems, we provide in the introduction an overview of the previously known results and a detailed account of the original contributions by the author.}, author = {Feliciangeli, Dario}, issn = {2663-337X}, pages = {180}, publisher = {Institute of Science and Technology Austria}, title = {{The polaron at strong coupling}}, doi = {10.15479/at:ista:9733}, year = {2021}, } @article{9746, abstract = {Evolutionary adaptation is a major source of antibiotic resistance in bacterial pathogens. Evolution-informed therapy aims to constrain resistance by accounting for bacterial evolvability. Sequential treatments with antibiotics that target different bacterial processes were previously shown to limit adaptation through genetic resistance trade-offs and negative hysteresis. Treatment with homogeneous sets of antibiotics is generally viewed to be disadvantageous, as it should rapidly lead to cross-resistance. We here challenged this assumption by determining the evolutionary response of Pseudomonas aeruginosa to experimental sequential treatments involving both heterogenous and homogeneous antibiotic sets. To our surprise, we found that fast switching between only β-lactam antibiotics resulted in increased extinction of bacterial populations. We demonstrate that extinction is favored by low rates of spontaneous resistance emergence and low levels of spontaneous cross-resistance among the antibiotics in sequence. The uncovered principles may help to guide the optimized use of available antibiotics in highly potent, evolution-informed treatment designs.}, author = {Batra, Aditi and Römhild, Roderich and Rousseau, Emilie and Franzenburg, Sören and Niemann, Stefan and Schulenburg, Hinrich}, issn = {2050-084X}, journal = {eLife}, publisher = {eLife Sciences Publications}, title = {{High potency of sequential therapy with only beta-lactam antibiotics}}, doi = {10.7554/elife.68876}, volume = {10}, year = {2021}, } @inbook{9756, abstract = {High-resolution visualization and quantification of membrane proteins contribute to the understanding of their functions and the roles they play in physiological and pathological conditions. Sodium dodecyl sulfate-digested freeze-fracture replica labeling (SDS-FRL) is a powerful electron microscopy method to study quantitatively the two-dimensional distribution of transmembrane proteins and their tightly associated proteins. During treatment with SDS, intracellular organelles and proteins not anchored to the replica are dissolved, whereas integral membrane proteins captured and stabilized by carbon/platinum deposition remain on the replica. Their intra- and extracellular domains become exposed on the surface of the replica, facilitating the accessibility of antibodies and, therefore, providing higher labeling efficiency than those obtained with other immunoelectron microscopy techniques. In this chapter, we describe the protocols of SDS-FRL adapted for mammalian brain samples, and optimization of the SDS treatment to increase the labeling efficiency for quantification of Cav2.1, the alpha subunit of P/Q-type voltage-dependent calcium channels utilizing deep learning algorithms.}, author = {Kaufmann, Walter and Kleindienst, David and Harada, Harumi and Shigemoto, Ryuichi}, booktitle = { Receptor and Ion Channel Detection in the Brain}, isbn = {9781071615218}, keywords = {Freeze-fracture replica: Deep learning, Immunogold labeling, Integral membrane protein, Electron microscopy}, pages = {267--283}, publisher = {Humana}, title = {{High-Resolution localization and quantitation of membrane proteins by SDS-digested freeze-fracture replica labeling (SDS-FRL)}}, doi = {10.1007/978-1-0716-1522-5_19}, volume = {169}, year = {2021}, } @article{9759, author = {Bartlett, Michael John and Arslan, Feyza N and Bankston, Adriana and Sarabipour, Sarvenaz}, issn = {15537358}, journal = {PLoS Computational Biology}, number = {7}, publisher = {Public Library of Science}, title = {{Ten simple rules to improve academic work- life balance}}, doi = {10.1371/journal.pcbi.1009124}, volume = {17}, year = {2021}, } @article{9760, abstract = {The quantum approximate optimization algorithm (QAOA) is a prospective near-term quantum algorithm due to its modest circuit depth and promising benchmarks. However, an external parameter optimization required in the QAOA could become a performance bottleneck. This motivates studies of the optimization landscape and search for heuristic ways of parameter initialization. In this work we visualize the optimization landscape of the QAOA applied to the MaxCut problem on random graphs, demonstrating that random initialization of the QAOA is prone to converging to local minima with suboptimal performance. We introduce the initialization of QAOA parameters based on the Trotterized quantum annealing (TQA) protocol, parameterized by the Trotter time step. We find that the TQA initialization allows to circumvent the issue of false minima for a broad range of time steps, yielding the same performance as the best result out of an exponentially scaling number of random initializations. Moreover, we demonstrate that the optimal value of the time step coincides with the point of proliferation of Trotter errors in quantum annealing. Our results suggest practical ways of initializing QAOA protocols on near-term quantum devices and reveal new connections between QAOA and quantum annealing.}, author = {Sack, Stefan and Serbyn, Maksym}, issn = {2521-327X}, journal = {Quantum}, publisher = {Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften}, title = {{Quantum annealing initialization of the quantum approximate optimization algorithm}}, doi = {10.22331/Q-2021-07-01-491}, volume = {5}, year = {2021}, } @article{9761, abstract = {The important roles of mitochondrial function and dysfunction in the process of neurodegeneration are widely acknowledged. Retinal ganglion cells (RGCs) appear to be a highly vulnerable neuronal cell type in the central nervous system with respect to mitochondrial dysfunction but the actual reasons for this are still incompletely understood. These cells have a unique circumstance where unmyelinated axons must bend nearly 90° to exit the eye and then cross a translaminar pressure gradient before becoming myelinated in the optic nerve. This region, the optic nerve head, contains some of the highest density of mitochondria present in these cells. Glaucoma represents a perfect storm of events occurring at this location, with a combination of changes in the translaminar pressure gradient and reassignment of the metabolic support functions of supporting glia, which appears to apply increased metabolic stress to the RGC axons leading to a failure of axonal transport mechanisms. However, RGCs themselves are also extremely sensitive to genetic mutations, particularly in genes affecting mitochondrial dynamics and mitochondrial clearance. These mutations, which systemically affect the mitochondria in every cell, often lead to an optic neuropathy as the sole pathologic defect in affected patients. This review summarizes knowledge of mitochondrial structure and function, the known energy demands of neurons in general, and places these in the context of normal and pathological characteristics of mitochondria attributed to RGCs. }, author = {Muench, Nicole A. and Patel, Sonia and Maes, Margaret E and Donahue, Ryan J. and Ikeda, Akihiro and Nickells, Robert W.}, issn = {20734409}, journal = {Cells}, number = {7}, publisher = {MDPI}, title = {{The influence of mitochondrial dynamics and function on retinal ganglion cell susceptibility in optic nerve disease}}, doi = {10.3390/cells10071593}, volume = {10}, year = {2021}, } @article{9769, abstract = {A few years ago, flow equations were introduced as a technique for calculating the ground-state energies of cold Bose gases with and without impurities. In this paper, we extend this approach to compute observables other than the energy. As an example, we calculate the densities, and phase fluctuations of one-dimensional Bose gases with one and two impurities. For a single mobile impurity, we use flow equations to validate the mean-field results obtained upon the Lee-Low-Pines transformation. We show that the mean-field approximation is accurate for all values of the boson-impurity interaction strength as long as the phase coherence length is much larger than the healing length of the condensate. For two static impurities, we calculate impurity-impurity interactions induced by the Bose gas. We find that leading order perturbation theory fails when boson-impurity interactions are stronger than boson-boson interactions. The mean-field approximation reproduces the flow equation results for all values of the boson-impurity interaction strength as long as boson-boson interactions are weak.}, author = {Brauneis, Fabian and Hammer, Hans-Werner and Lemeshko, Mikhail and Volosniev, Artem}, issn = {2542-4653}, journal = {SciPost Physics}, number = {1}, publisher = {SciPost}, title = {{Impurities in a one-dimensional Bose gas: The flow equation approach}}, doi = {10.21468/scipostphys.11.1.008}, volume = {11}, year = {2021}, } @article{9770, abstract = {We study an effective one-dimensional quantum model that includes friction and spin-orbit coupling (SOC), and show that the model exhibits spin polarization when both terms are finite. Most important, strong spin polarization can be observed even for moderate SOC, provided that the friction is strong. Our findings might help to explain the pronounced effect of chirality on spin distribution and transport in chiral molecules. In particular, our model implies static magnetic properties of a chiral molecule, which lead to Shiba-like states when a molecule is placed on a superconductor, in accordance with recent experimental data.}, author = {Volosniev, Artem and Alpern, Hen and Paltiel, Yossi and Millo, Oded and Lemeshko, Mikhail and Ghazaryan, Areg}, issn = {2469-9969}, journal = {Physical Review B}, number = {2}, publisher = {American Physical Society}, title = {{Interplay between friction and spin-orbit coupling as a source of spin polarization}}, doi = {10.1103/physrevb.104.024430}, volume = {104}, year = {2021}, }