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Two-dimensional (2D) organic-inorganic hybrid perovskites emerged as a versatile platform for light-emitting and photovol-taic applications due to their unique structural design and chemical flexibility. Their properties depend heavily on both the choice of the inorganic lead halide framework and the surrounding organic layers. Recently, the introduction of chiral cations into 2D perovskites has attracted major interest due to their potential for introducing chirality and tuning the chiro-optical response. Importantly, the optical properties in these materials are dominated by tightly bound excitons that also serve as primary carriers for the energy transport. The mobility of photoinjected excitons is thus important from the perspectives of fundamental material properties and optoelectronic applications, yet remains an open question. Here, we demonstrate exciton propagation in a 2D chiral perovskite methylbenzylammonium lead iodide (MBA2PbI4) using transient photoluminescence microscopy and reveal density-dependent transport over more than 100 nanometers at room temperature with diffusion coeffi-cients as high as 2 cm2/s. We observe two distinct regimes of initially rapid diffusive propagation and subsequent localiza-tion. Moreover, perovskites with enantiomer pure cations are found to exhibit faster exciton diffusion compared to the race-mic mixture, correlated with the impact of the material composition on disorder. Altogether, the observations of efficient exciton diffusion at room temperature highlight the potential of 2D chiral perovskites to merge chiro-optical properties with strong light-matter interaction and efficient energy transport.

The quest for platforms to generate and control exotic excitonic states has greatly benefited from the advent of transition metal dichalcogenide (TMD) monolayers and their heterostructures. Among the unconventional excitonic states, quadrupolar excitons - a hybridized combination of two dipolar excitons with anti-aligned dipole moments - are of great interest for applications in quantum simulations and for the investigation of many-body physics. Here, we unambiguously demonstrate for the first time in natural MoSe$_2$ homobilayers the emergence of quadrupolar excitons, whose energy shifts quadratically in electric field. In contrast to, so far reported trilayer systems hosting quadrupolar excitons, MoSe$_2$ homobilayers have many advantages, a stronger interlayer hybridization, cleaner potential landscapes and inherent stability with respect to moir\'e potentials or post-stacking reconstruction. Our experimental observations are complemented by many-particle theory calculations offering microscopic insights in the formation of quadrupole excitons. Our results suggest TMD homobilayers as ideal platform for the engineering of excitonic states and their interaction with light and thus candidate for carrying out on-chip simulations., Comment: Main manuscript contains 8 pages and 3 figures, supplementary information contains 14 pages and 6 figures (22 pages and 9 figures in total). This manuscript was submitted to Nature Nanotechnology

For $n \in \mathbb{N}$ and $\varepsilon > 0$, given a sufficiently long sequence of events in a probability space all of measure at least $\varepsilon$, some $n$ of them will have a common intersection. A more subtle pattern: for any $0 < p < q < 1$, we cannot find events $A_i$ and $B_i$ so that $\mu \left( A_i \cap B_j \right) \leq p$ and $\mu \left( A_j \cap B_i\right) \geq q$ for all $1 < i < j < n$, assuming $n$ is sufficiently large. This is closely connected to model-theoretic stability of probability algebras. We survey some results from our recent work on more complicated patterns that arise when our events are indexed by multiple indices. In particular, how such results are connected to higher arity generalizations of de Finetti's theorem in probability, structural Ramsey theory, hypergraph regularity in combinatorics, and model theory., Comment: 6 pages; to appear in the Proceedings of the 14th Panhellenic Logic Symposium

We study idempotent measures and the structure of the convolution semigroups of measures over definable groups. We isolate the property of generic transitivity and demonstrate that it is sufficient (and necessary) to develop stable group theory localizing on a generically stable type, including invariant stratified ranks and connected components. We establish generic transitivity of generically stable idempotent types in important new cases, including abelian groups in arbitrary theories and arbitrary groups in rosy theories, and characterize them as generics of connected type-definable subgroups. Using tools from Keisler's randomization theory, we generalize some of these results from types to generically stable Keisler measures, and classify idempotent generically stable measures in abelian groups as (unique) translation-invariant measures on type-definable fsg subgroups. This provides a partial definable counterpart to the classical work of Rudin, Cohen and Pym for locally compact topological groups. Finally, we provide an explicit construction of a minimal left ideal in the convolution semigroup of measures for an arbitrary countable NIP group, from a minimal left ideal in the corresponding semigroup on types and a canonical measure constructed on its ideal subgroup. In order to achieve it, we in particular prove the revised Ellis group conjecture of Newelski for countable NIP groups., Comment: 63 pages

We investigate various forms of (model-theoretic) stability for hypergraphs and their corresponding strengthenings of the hypergraph regularity lemma with respect to partitions of vertices. On the one hand, we provide a complete classification of the various possibilities in the ternary case. On the other hand, we provide an example of a family of slice-wise stable 3-hypergraphs so that for no partition of the vertices, any triple of parts has density close to 0 or 1. In particular, this addresses some questions and conjectures of Terry and Wolf. We work in the general measure theoretic context of graded probability spaces, so all our results apply both to measures in ultraproducts of finite graphs, leading to the aforementioned combinatorial applications, and to commuting definable Keisler measures, leading to applications in model theory., Comment: 67 pages

Exciton-exciton interactions are key to understanding non-linear optical and transport phenomena in van der Waals heterobilayers, which emerged as versatile platforms to study correlated electronic states. We present a combined theory-experiment study of excitonic many-body effects based on first-principle band structures and Coulomb interaction matrix elements. Key to our approach is the explicit treatment of the fermionic substructure of excitons and dynamical screening effects for density-induced energy renormalization and dissipation. We demonstrate that dipolar blue shifts are almost perfectly compensated by many-body effects, mainly by screening-induced self-energy corrections. Moreover, we identify a crossover between attractive and repulsive behavior at elevated exciton densities. Theoretical findings are supported by experimental studies of spectrally-narrow interlayer excitons in atomically-reconstructed, hBN-encapsulated MoSe$_2$/WSe$_2$ heterobilayers. Both theory and experiment show energy renormalization on a scale of a few meV even for high injection densities in the vicinity of the Mott transition. Our results revise the established picture of dipolar repulsion dominating exciton-exciton interactions in van der Waals heterostructures and open up opportunities for their external design.

Global warming accelerates permafrost degradation, impacting the reliability of critical infrastructure used by more than five million people daily. Furthermore, permafrost thaw produces substantial methane emissions, further accelerating global warming and climate change and putting more than eight billion people at additional risk. To mitigate the upcoming risk, policymakers and stakeholders must be given an accurate prediction of the thaw development. Unfortunately, comprehensive physics-based permafrost models require location-specific fine-tuning that is challenging in practice. Models of intermediate complexity require few input parameters but have relatively low accuracy. The performance of pure data-driven models is low as well as the observational data is sparse and limited. In this work, we designed a physics-informed machine-learning approach for permafrost thaw prediction. The method uses a heat equation to regularize data-driven approach trained over permafrost monitoring data and climate projections. The latter leads to higher precision and better numerical stability allowing for reliable decision-making or construction and maintenance in the areas endangered by permafrost thaw with a time horizon of decades., Comment: 30 pages, 24 figures

The topology of the electronic band structure of solids can be described by its Berry curvature distribution across the Brillouin zone. We theoretically introduce and experimentally demonstrate a general methodology based on the measurement of energy- and momentum-resolved optical transition rates, allowing to reveal signatures of Berry curvature texture in reciprocal space. By performing time- and angle-resolved photoemission spectroscopy of atomically thin WSe$_2$ using polarization-modulated excitations, we demonstrate that excitons become an asset in extracting the quantum geometrical properties of solids. We also investigate the resilience of our measurement protocol against ultrafast scattering processes following direct chiroptical transitions., Comment: 11 pages, 4 figures + supplementary materials

Data profiling is an essential process in modern data-driven industries. One of its critical components is the discovery and validation of complex statistics, including functional dependencies, data constraints, association rules, and others. However, most existing data profiling systems that focus on complex statistics do not provide proper integration with the tools used by contemporary data scientists. This creates a significant barrier to the adoption of these tools in the industry. Moreover, existing systems were not created with industrial-grade workloads in mind. Finally, they do not aim to provide descriptive explanations, i.e. why a given pattern is not found. It is a significant issue as it is essential to understand the underlying reasons for a specific pattern's absence to make informed decisions based on the data. Because of that, these patterns are effectively rest in thin air: their application scope is rather limited, they are rarely used by the broader public. At the same time, as we are going to demonstrate in this presentation, complex statistics can be efficiently used to solve many classic data quality problems. Desbordante is an open-source data profiler that aims to close this gap. It is built with emphasis on industrial application: it is efficient, scalable, resilient to crashes, and provides explanations. Furthermore, it provides seamless Python integration by offloading various costly operations to the C++ core, not only mining. In this demonstration, we show several scenarios that allow end users to solve different data quality problems. Namely, we showcase typo detection, data deduplication, and data anomaly detection scenarios.

Interlayer exciton diffusion is studied in atomically-reconstructed MoSe2/WSe2 heterobilayers with suppressed disorder. Local atomic registry is confirmed by characteristic optical absorption, circularly-polarized photoluminescence, and g-factor measurements. Using transient microscopy we observe propagation properties of interlayer excitons that are independent from trapping at moir\'e- or disorder-induced local potentials. Confirmed by characteristic temperature dependence for free particles, linear diffusion coefficients of interlayer excitons at liquid helium temperature and low excitation densities are almost 1000 times higher than in previous observations. We further show that exciton-exciton repulsion and annihilation contribute nearly equally to non-linear propagation by disentangling the two processes in the experiment and simulations. Finally, we demonstrate effective shrinking of the light-emission over time across several 100's of picoseconds at the transition from exciton- to the plasma-dominated regimes. Supported by microscopic calculations for bandgap renormalization to identify Mott threshold, this indicates transient crossing between rapidly expanding, short-lived electron-hole plasma and slower, long-lived exciton populations.

In this paper we present genetic algorithms based search technique for the linear optics schemes, performing two-qubit quantum gates. We successfully applied this technique for finding heralded two-qubit gates and obtained the new schemes with performance parameters equal to the best currently known. The new simple metrics is introduced which enables comparison of schemes with different heralding mechanisms. The scheme performance degradation is discussed for the cases when detectors in the heralding part of the scheme are not photon-number-resolving. We propose a procedure for overcoming this drawback which allows us to restore the reliable heralding signal even with not-photon-number-resolving detectors.

In the presented work we consider the influence of a hypothetical sterile neutrino (with eV-scale mass) on the determination of cosmological parameters. If it is detected, it will be necessary to include it into the $\Lambda \rm CDM$ model with the fixed values of its mass $m_{\rm s}$ and mixing angle $\theta_{s}$, which is the main method used through this paper. Apart from that, the seesaw mechanism requires there to be at least two sterile states, one of them being much heavier than the active ones. The heavier sterile state ($m_{s}\sim1$ keV) would decay and increase the effective number of active neutrinos. Therefore, the influence of a change in the effective number of relativistic neutrino species $N_{\rm eff}$ was studied as well, which could be caused by, for example, the decay processes of the above-mentioned sterile neutrinos, as well as processes leading to an increase in the temperature of relic neutrinos $T_{\rm C\nu B}$. The effects studied in this work lead to a significant change in the estimates of the cosmological parameters, including the value of $H_{0}$. It has been discovered that the accounting of the sterile neutrino with masses $m=1$ and $2.7$ eV leads to a decrease in the estimate of the current Hubble parameter value $H_{0}$ and, therefore, exacerbates the ``$H_{0}$-tension'' problem. An increase in the value of the effective number of relativistic neutrino species leads, on the contrary, to an increase in the $H_{0}$ estimate, resolving the above-mentioned problem at $N_{\rm eff}=3.0+0.9$, which is equivalent to an increase of the neutrino temperature up to $T ^{\,0}_{\rm C\nu B}=1.95+0.14\,\rm K$. At the same time, the rest of the cosmological parameters do not change significantly, leaving us within the framework of the standard $\Lambda \rm CDM$ model., Comment: 19 pages, 6 figures

Pioneering data profiling systems such as Metanome and OpenClean brought public attention to science-intensive data profiling. This type of profiling aims to extract complex patterns (primitives) such as functional dependencies, data constraints, association rules, and others. However, these tools are research prototypes rather than production-ready systems. The following work presents Desbordante - a high-performance science-intensive data profiler with open source code. Unlike similar systems, it is built with emphasis on industrial application in a multi-user environment. It is efficient, resilient to crashes, and scalable. Its efficiency is ensured by implementing discovery algorithms in C++, resilience is achieved by extensive use of containerization, and scalability is based on replication of containers. Desbordante aims to open industrial-grade primitive discovery to a broader public, focusing on domain experts who are not IT professionals. Aside from the discovery of various primitives, Desbordante offers primitive validation, which not only reports whether a given instance of primitive holds or not, but also points out what prevents it from holding via the use of special screens. Next, Desbordante supports pipelines - ready-to-use functionality implemented using the discovered primitives, for example, typo detection. We provide built-in pipelines, and the users can construct their own via provided Python bindings. Unlike other profilers, Desbordante works not only with tabular data, but with graph and transactional data as well. In this paper, we present Desbordante, the vision behind it and its use-cases. To provide a more in-depth perspective, we discuss its current state, architecture, and design decisions it is built on. Additionally, we outline our future plans.

This empirical study investigates the impact of the Hofstede cultural dimensions (HCD) on the Global Innovation Index (GII) scores in four different years (2007, 2009, 2019 and 2021) to compare the impacts during the pre- and post-crisis (financial and COVID-19) period by employing ordinary least square (OLS) and robust least square (Robust) analyses. The purpose of this study is to identify the impact of cultural factors on the innovation development for different income groups during the pre- and post-crisis period. We found that, in general, the same cultural properties were required for countries to enhance innovation inputs and outputs regardless of pre- and post-crisis periods and time variances. The significant cultural factors (driving forces) of the innovation performance do not change over time. However, our empirical results revealed that not the crisis itself but the income group (either developed or developing) is the factor that influences the relationship between cultural properties and innovation. It is also worth noting that cultural properties have lost much of their impact on innovation, particularly in developing countries, during recent periods. It is highly likely that in terms of innovation, no cultural development or change can significantly impact the innovation output of developing countries without the construction of the appropriate systems., Comment: Keywords Hofstede cultural dimensions (HCD); Global Innovation Index (GII); financial crisis; COVID-19; comparative analysis

Considering the growing significance of Eurasian economic ties because of South Korea s New Northern Policy and Russia s New Eastern Policy, this study investigates the motivations and locational factors of South Korean foreign direct investment (FDI) in three countries in the Commonwealth of Independent States (CIS: Kazakhstan, Russia, and Uzbekistan) by employing panel analysis (pooled ordinary least squares (OLS), fixed effects, random effects) using data from 1993 to 2017. The results show the positive and significant coefficients of GDP, resource endowments, and inflation. Unlike conventional South Korean outward FDI, labour-seeking is not defined as a primary purpose. Exchange rates, political rights, and civil liberties are identified as insignificant. The authors conclude that South Korean FDI in Kazakhstan, Russia, and Uzbekistan is associated with market-seeking (particularly in Kazakhstan and Russia) and natural resource-seeking, especially the former. From a policy perspective, our empirical evidence suggests that these countries host governments could implement mechanisms to facilitate the movement of goods across regions and countries to increase the attractiveness of small local markets. The South Korean government could develop financial support and risk sharing programmes to enhance natural resource-seeking investments and mutual exchange programmes to overcome the red syndrome complex in South Korean society.

There are significant differences in innovation performance between countries. Additionally, the pharmaceutical sector is stronger in some countries than in others. This suggests that the development of the pharmaceutical industry can influence a country's innovation performance. Using the Global Innovation Index and selected performance measures of the pharmaceutical sector, this study examines how the pharmaceutical sector influences the innovation performance of countries from the European context. The dataset of 27 European countries was analysed using simple, and multiple linear regressions and Pearson correlation. Our findings show that only three indicators of the pharmaceutical industry, more precisely pharmaceutical Research and Development, pharmaceutical exports, and pharmaceutical employment explain the innovation performance of a country largely. Pharmaceutical Research and Development and exports have a significant positive impact on a country's innovation performance, whereas employment in the pharmaceutical industry has a slightly negative impact. Additionally, global innovation performance has been found to positively influence life expectancy. We further outline the implications and possible policy directions based on these findings.

Feature extraction methods help in dimensionality reduction and capture relevant information. In time series forecasting (TSF), features can be used as auxiliary information to achieve better accuracy. Traditionally, features used in TSF are handcrafted, which requires domain knowledge and significant data-engineering work. In this research, we first introduce a notion of static and dynamic features, which then enables us to develop our autonomous Feature Retrieving Autoregressive Network for Static features (FRANS) that does not require domain knowledge. The method is based on a CNN classifier that is trained to create for each series a collective and unique class representation either from parts of the series or, if class labels are available, from a set of series of the same class. It allows to discriminate series with similar behaviour but from different classes and makes the features extracted from the classifier to be maximally discriminatory. We explore the interpretability of our features, and evaluate the prediction capabilities of the method within the forecasting meta-learning environment FFORMA. Our results show that our features lead to improvement in accuracy in most situations. Once trained our approach creates features orders of magnitude faster than statistical methods.

We study theoretically effects of an anisotropic elastic strain on the exciton energy spectrum fine structure and optical selection rules in atom-thin crystals based on transition-metal dichalcogenides. The presence of strain breaks the chiral selection rules at the $\bm K$-points of the Brillouin zone and makes optical transitions linearly polarized. The orientation of the induced linear polarization is related to the main axes of the strain tensor. Elastic strain provides an additive contribution to the exciton fine structure splitting in agreement with experimental evidence obtained from uniaxially strained WSe$_2$ monolayer. The applied strain also induces momentum-dependent Zeeman splitting. Depending on the strain orientation and magnitude, Dirac points with a linear dispersion can be formed in the exciton energy spectrum. We provide a symmetry analysis of the strain effects and develop a microscopic theory for all relevant strain-induced contributions to the exciton fine structure Hamiltonian., Comment: 12 pages, 5 figures

Heterobilayers consisting of MoSe$_2$ and WSe$_2$ monolayers can host optically bright interlayer excitons with intriguing properties such as ultralong lifetimes and pronounced circular polarization of their photoluminescence due to valley polarization, which can be induced by circularly polarized excitation or applied magnetic fields. Here, we report on the observation of an intrinsic valley-magnetophonon resonance for localized interlayer excitons promoted by invervalley hole scattering. It leads to a resonant increase of the photoluminescence polarization degree at the same field of 24.2 Tesla for H-type and R-type stacking configurations despite their vastly different excitonic energy splittings. As a microscopic mechanism of the hole intervalley scattering we identify the scattering with chiral TA phonons of MoSe$_2$ between excitonic states mixed by the long-range electron hole exchange interaction., Comment: 7+5 pages, 2+0 figures

We introduce and study semi-equational and weakly semi-equational theories, generalizing equationality in stable theories (in the sense of Srour) to the NIP context. In particular, we establish a connection to distality via one-sided strong honest definitions; demonstrate that certain trees are semi-equational, while algebraically closed valued fields are not weakly semi-equational; and obtain a general criterion for weak semi-equationality of an expansion of a distal structure by a new predicate., Comment: Version 2: 28 pages. This version of the article was significantly shortened for the journal publication, resulting in many details, examples and general results about semi-equationality being omitted. For the full version of the article see arXiv:2204.13790v1. Accepted to the Journal of Symbolic Logic