Miracle-dz/UniFAR-FTUs huggingface dataset:
Slow diffusion and Thouless localization criterion in modulated spin chains

In recent years the ergodicity of disordered spin chains has been investigated via extensive numerical studies of the level statistics or the transport properties. However, a clear relationship between these results has yet to be established. We present the relation between the diffusion constant and the energy-level structure, which leads to the Thouless localization criterion. Together with the exponential-like dependence of the diffusion constant on the strength of quasiperiodic or random fields, the Thouless criterion explains the nearly linear drift with the system size of the crossover/transition to the nonergodic regime. Moreover, we show that the Heisenberg spin chain in the presence of the quasiperiodic fields can be well approached via a sequence of simple periodic systems, where diffusion remains finite even at large fields.
On affine Lusztig varieties

Affine Lusztig varieties encode the orbital integrals of Iwahori-Hecke functions and serve as building blocks for the (conjectural) theory of affine character sheaves. In this paper, we establish a close relationship between affine Lusztig varieties and affine Deligne-Lusztig varieties. Consequently, we give an explicit nonemptiness pattern and dimension formula for affine Lusztig varieties in most cases.
Gibbs-like measure for spectrum of a class of quasi-crystals

Abstract Let α∈(0,1) be an irrational, and [0;a1,a2,…] the continued fraction expansion of α. Let Hα,V be the one-dimensional Schrödinger operator with Sturmian potential of frequency α. Suppose the potential strength V >20 and the sequence (ai)i≥1 is bounded. We proceed by developing some new ideas on dimensional theory of Cookie-cutter sets. We prove that the spectral generating bands satisfy the principles of bounded variation and bounded covariation, and then we show that there exists a Gibbs-like measure on the spectrum σ(Hα,V). As an application, we prove that \[ \dim _H \sigma (H_{\alpha ,V})=s_*, \quad \overline {\dim }_B\, \sigma (H_{\alpha ,V})=s^*, \] where s* and s* are the lower and upper pre-dimensions. Moreover, if (an)n≥1 is ultimately periodic, then s* =s*.
Unsupervised Deep Learning for IoT Time Series

Internet of Things (IoT) time-series analysis has found numerous applications in a wide variety of areas, ranging from health informatics to network security. Nevertheless, the complex spatial–temporal dynamics and high dimensionality of IoT time series make the analysis increasingly challenging. In recent years, the powerful feature extraction and representation learning capabilities of deep learning (DL) have provided an effective means for IoT time-series analysis. However, few existing surveys on time series have systematically discussed unsupervised DL-based methods. To fill this void, we investigate unsupervised DL for IoT time series, i.e., unsupervised anomaly detection and clustering, under a unified framework. We also discuss the application scenarios, public data sets, existing challenges, and future research directions in this area.
Label-free imaging of goblet cells as a marker for differentiating colonic polyps by multiphoton microscopy

Discrimination of adenomas from hyperplastic polyps can reduce the risk of unnecessary complications and healthcare cost. However, it is challenging during colonoscopy screening, and histological analysis remains the ``gold standard'' for the final diagnosis. Here, we describe a label-free imaging method, multiphoton microscopy (MPM), to the discrimination between adenomas and hyperplastic polyps. We find that multiphoton imaging provides cellular and subcellular details to the identification of adenomas from hyperplastic polyps. In particular, there is significant difference in the population density of goblet cells among normal colon, hyperplastic polyp, and adenoma, providing substantial potential to become a quantitative intrinsic marker for in vivo clinical diagnosis of early colonic lesions. To our knowledge, this is the first demonstration of the potential of MPM for differentiation of colonic polyps.
Ab initio study of the nonlinear optical properties and d.c. photocurrent of the Weyl semimetal TaIrTe

We present a ﬁrst principles theoretical study employing nonlinear response theory to investigate the d.c. photocurrent generated by linearly polarized light in the type-II Weyl semimetal TaIrTe 4 . We report the low energy spectrum of several nonlinear optical effects. At second-order, we consider the shift an injection currents. Assuming the presence of a static built-in electric ﬁeld, at third-order we study the current-induced shift and injection currents, as well as the jerk current. We discuss our result in the context of a recent experiment measuring exceptionally large photoconductivity [1]. According to our results, the jerk current is the most likely origin of the large response. Finally, we propose means to discern the importance of the various mechanisms involved in a time-resolved experiment.
Exploitation and exploration in text evolution. Quantifying planning and translation flows during writing

Writing is a complex process at the center of much of modern human activity. Despite appearing to be a linear process, writing conceals many highly non-linear processes. Previous research has focused on three phases of writing: planning, translation and transcription, and revision. While research has shown these are non-linear, they are often treated linearly when measured. Here, we introduce measures to detect and quantify subcycles of planning (exploration) and translation (exploitation) during the writing process. We apply these to a novel dataset that recorded the creation of a text in all its phases, from early attempts to the finishing touches on a final version. This dataset comes from a series of writing workshops in which, through innovative versioning software, we were able to record all the steps in the construction of a text. 61 junior researchers in science wrote a scientific essay intended for a general readership. We recorded each essay as a writing cloud, defined as a complex topological structure capturing the history of the essay itself. Through this unique dataset of writing clouds, we expose a representation of the writing process that quantifies its complexity and the writer’s efforts throughout the draft and through time. Interestingly, this representation highlights the phases of “translation flow”, where authors improve existing ideas, and exploration, where creative deviations appear as the writer returns to the planning phase. These turning points between translation and exploration become rarer as the writing process progresses and the author approaches the final version. Our results and the new measures introduced have the potential to foster the discussion about the non-linear nature of writing and support the development of tools that can lead to more creative and impactful writing processes.
Flexible Phased Array Sheets: A Techno-Economic Analysis

Phased arrays have enabled advances in communications, sensing, imaging, and wireless power transfer. In all these applications, large apertures enable higher power, higher data rates, higher resolution, and complex functionalities, but are elusive owing to a correspondingly large cost, mass, and physical size. Flexible phased arrays (FPAs) show potential in breaking this trade-off. Their thinness and extremely low mass allow FPAs to be folded, rolled, or otherwise compressed into smaller sizes, thus enabling new regimes of transport and entirely new applications currently not possible. Though a number laboratory prototypes of FPAs have been constructed, the economics of large-scale FPA production has yet to be explored. This paper presents a model FPA architecture and a cost model for producing it at large-scale. The estimate of the per-unit-area cost is bounded by a three-tiered approach. The cost model projects a"middle"estimate for FPA production at $89 per square meter. Estimates for aerial mass density and startup cost are also discussed. This cost model demonstrates that an FPA can be produced at an efficient price point and can potentially replace existing solutions for space, communications, and vehicular applications that demand lightweight, portability, and durability in extreme conditions.
Future instability of FLRW fluid solutions for linear equations of state $p=K\rho$ with $1/3

Using numerical methods, we examine the dynamics of nonlinear perturbations in the expanding time direction, under a Gowdy symmetry assumption, of FLRW fluid solutions to the Einstein-Euler equations with a positive cosmological constant $\Lambda>0$ and a linear equation of state $p = K\rho$ for the parameter values $1/3<K<1$. This paper builds upon the numerical work in \cite{Marshalloliynyk:2022} in which the simpler case of a fluid on a fixed FLRW background spacetime was studied. The numerical results presented here confirm that the instabilities observed in \cite{Marshalloliynyk:2022} are also present when coupling to gravity is included as was previously conjectured in \cite{Rendall:2004,Speck:2013}. In particular, for the full parameter range $1/3<K<1$, we find that the density contrast of the nonlinear perturbations develop steep gradients near a finite number of spatial points and becomes unbounded there at future timelike infinity. This instability is of particular interest since it is not consistent with the standard picture for late time expansion in cosmology.
STAR-RIS-enabled simultaneous indoor and outdoor 3D localisation: Theoretical analysis and algorithmic design

Recent research and development interests deal with metasurfaces for wireless systems beyond their consideration as intelligent tunable reflectors. Among the latest proposals is the simultaneously transmitting (a.k.a. refracting) and reflecting reconfigurable intelligent surface (STAR-RIS) which intends to enable bidirectional indoor-to-outdoor, and vice versa, communications thanks to its additional refraction capability. This double functionality provides increased flexibility in concurrently satisfying the quality-of-service requirements of users located at both sides of the metasurfaces, for example, the achievable data rate and localization accuracy. In this paper, we focus on STAR-RIS-empowered simultaneous indoor and outdoor three-dimensional (3D) localization, and study the fundamental performance limits via Fisher information analyses and Cram\'er Rao lower bounds (CRLBs). We also devise an efficient localization algorithm based on an off-grid compressive sensing (CS) technique relying on atomic norm minimization (ANM). The impact of the training overhead, the power splitting at the STAR-RIS, the power allocation between the users, the STAR-RIS size, the imperfections of the STAR-RIS-to-BS channel, as well as the role of the multi-path components on the positioning performance are assessed via extensive computer simulations. It is theoretically showcased that high-accuracy, up to centimeter level, 3D localization can be simultaneously achieved for indoor and outdoor users, which is also accomplished via the proposed ANM-based estimation algorithm.
Theoretical demonstration of enhancement of light extraction of flip-chip GaN light-emitting diodes with photonic crystals

The authors demonstrate, using finite-difference time-domain modeling, an enhancement in the extraction efficiency of flip-chip GaN light-emitting diodes (LEDs) using photonic crystals. The authors compare the extraction efficiencies of four configurations of a flip-chip GaN LED: with and without photonic crystal (PhC) layers, with a perfect reflecting mirror, and a bottom PhC reflector on GaN in combination with a top PhC extractor on sapphire. The authors show that, by using a photonic crystal layer as a bottom reflector, they can enhance the extraction efficiency similar to that of a mirror, yet the PhC reflector has the advantage that the metallic mirror loss can be avoided.
Transfer learning for process design with reinforcement learning

Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, reinforcement learning (RL) has shown some success in automating process design by integrating data-driven models that learn to build process flowsheets with process simulation in an iterative design process. However, one major challenge in the learning process is that the RL agent demands numerous process simulations in rigorous process simulators, thereby requiring long simulation times and expensive computational power. Therefore, typically short-cut simulation methods are employed to accelerate the learning process. Short-cut methods can, however, lead to inaccurate results. We thus propose to utilize transfer learning for process design with RL in combination with rigorous simulation methods. Transfer learning is an established approach from machine learning that stores knowledge gained while solving one problem and reuses this information on a different target domain. We integrate transfer learning in our RL framework for process design and apply it to an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles, our method can design economically feasible flowsheets with stable interaction with DWSIM. Our results show that transfer learning enables RL to economically design feasible flowsheets with DWSIM, resulting in a flowsheet with an 8% higher revenue. And the learning time can be reduced by a factor of 2.
The ALMOND Survey: Molecular cloud properties and gas density tracers across 25 nearby spiral galaxies with ALMA

We use new HCN(1–0) data from the ALMOND (ACA Large-sample Mapping Of Nearby galaxies in Dense gas) survey to trace the kpc-scale molecular gas density structure and CO(2–1) data from PHANGS-ALMA to trace the bulk molecular gas across 25 nearby, star-forming galaxies. At 2.1 kpc scale, we measure the density-sensitive HCN/CO line ratio and the SFR/HCN ratio to trace the star formation efficiency in the denser molecular medium. At 150 pc scale, we measure structural and dynamical properties of the molecular gas via CO(2–1) line emission, which is linked to the lower resolution data using an intensity-weighted averaging method. We find positive correlations (negative) of HCN/CO (SFR/HCN) with the surface density, the velocity dispersion and the internal turbulent pressure of the molecular gas. These observed correlations agree with expected trends from turbulent models of star formation, which consider a single free-fall time gravitational collapse. Our results show that the kpc-scale HCN/CO line ratio is a powerful tool to trace the 150 pc scale average density distribution of the molecular clouds. Lastly, we find systematic variations of the SFR/HCN ratio with cloud-scale molecular gas properties, which are incompatible with a universal star formation efficiency. Overall, these findings show that mean molecular gas density, molecular cloud properties and star formation are closely linked in a coherent way, and observations of density-sensitive molecular gas tracers are a useful tool to analyse these variations, linking molecular gas physics to stellar output across galaxy discs.
Distribution dependent BSDEs driven by Gaussian processes

In this paper we are concerned with distribution dependent backward stochastic differential equations (DDBSDEs) driven by Gaussian processes. We first show the existence and uniqueness of solutions to this type of equations. This is done by formulating a transfer principle to transfer the well-posedness problem to an auxiliary DDBSDE driven by Brownian motion. Then, we establish a comparison theorem under Lipschitz condition and boundedness of Lions derivative imposed on the generator. Furthermore, we get a new representation for DDBSDEs driven by Gaussian processes, this representation is even new for the case of the equations driven by Brownian motion. The new obtained representation enables us to prove a converse comparison theorem. Finally, we derive transportation inequalities and Logarithmic-Sobolev inequalities via the stability of the Wasserstein distance and the relative entropy of measures under the homeomorphism condition.
Dividing Good and Better Items Among Agents with Submodular Valuations

We study the problem of fairly allocating a set of indivisible goods among agents with bivalued submodular valuations — each good provides a marginal gain of either a or b ( a < b ) and goods have decreasing marginal gains. This is a natural generalization of two well-studied valuation classes — bivalued additive valuations and binary submodular valuations. We present a simple sequential algorithmic framework, based on the recently introduced Yankee Swap mechanism, that can be adapted to compute a variety of solution concepts, including leximin, max Nash welfare (MNW) and p -mean welfare maximizing allocations when a divides b . This result is complemented by an existing result on the computational intractability of leximin and MNW allocations when a does not divide b . We further examine leximin and MNW allocations with respect to two well-known properties — envy freeness and the maximin share guarantee. On envy freeness, we show that neither the leximin nor the MNW allocation is guaranteed to be envy free up to one good (EF1). This is surprising since for the simpler classes of bivalued additive valuations and binary submodular valuations, MNW allocations are known to be envy free up to any good (EFX). On the maximin share guarantee, we show that MNW and leximin allocations guarantee each agent 14 and a b +3 a of their maximin share respectively when a divides b . This fraction improves to 13 and a b +2 a respectively when agents have bivalued additive valuations.
NGC 5982: a smooth triaxial elliptical

NGC 5982 is known to be one of those elliptical galaxies that have peculiar kinematics in their centres. In this paper, optical spectroscopic observations are presented, which show that a specific feature is present in the velocity field of NGC 5982. This suggests that the kinematical behaviour is the result of projection effects in a dynamically smooth triaxial elliptical galaxy, and that it is not due to a dynamically distinct, central mass component. Nevertheless, we suggest that it may be related to a merger event
Consistent and Asymptotically Efficient Localization From Range- Difference Measurements

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source’s position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased and further consistent estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves a 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case.
Identification of Power System Oscillation Modes using Blind Source Separation based on Copula Statistic

The dynamics of a power system with large penetration of renewable energy resources are becoming more nonlinear due to the intermittence of these resources and the switching of their power electronic devices. Therefore, it is crucial to accurately identify the dynamical modes of oscillation of such a power system when it is subject to disturbances to initiate appropriate preventive or corrective control actions. In this paper, we propose a high-order blind source identification (HOBI) algorithm based on the copula statistic to address these non-linear dynamics in modal analysis. The method combined with Hilbert transform (HOBI-HT) and iteration procedure (HOBMI) can identify all the modes as well as the model order from the observation signals obtained from the number of channels as low as one. We access the performance of the proposed method on numerical simulation signals and recorded data from a simulation of time domain analysis on the classical 11-Bus 4-Machine test system. Our simulation results outperform the state-of-the-art method in accuracy and effectiveness.
Non-unique stationary solutions of forced SQG

We show the existence of non-unique stationary weak solutions for forced surface quasi-geostrophic (SQG) equation via a convex integration scheme. The scheme is implemented for the sum-difference system of two distinct solutions. Through this scheme, one observes the external forcing is naturally generated accompanying the flexibility in means of lack of uniqueness. It thus provides a transparent way to reveal the flexibility of the system with the presence of a forcing.
Landscape of High-Performance Python to Develop Data Science and Machine Learning Applications

Python has become the prime language for application development in the data science and machine learning domains. However, data scientists are not necessarily experienced programmers. Although Python lets them quickly implement their algorithms, when moving at scale, computation efficiency becomes inevitable. Thus, harnessing high-performance devices such as multi-core processors and graphical processing units to their potential is generally not trivial. The present narrative survey can be thought of as a reference document for such practitioners to help them make their way in the wealth of tools and techniques available for the Python language. Our document revolves around user scenarios, which are meant to cover most situations they may face. We believe that this document may also be of practical use to tool developers, who may use our work to identify potential lacks in existing tools and help them motivate their contributions.