Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure property and mesomorphic behaviour of S-shaped liquid crystal oligomers possessing two azobenzene moieties

ABSTRACT A new series of symmetrical S-shaped oligomers 4,4ʹ-bis(ω-2-(ω-[(bromophenyl)diazenyl]-alkoxy)phenoxy)hexylbiphenyl consisting of two different spacers (inner -(CH2)6- and outer -(CH2)n-) have been synthesised. Their physical, thermal and texture observation over various transition temperatures are reported. The outer spacers for these compounds vary from n = 4 to n = 9. The oligomers with even number of members exhibit monotropic phase in which the compound with n = 4 shows nematic (N) phase whilst those with members n = 6 and 8 exhibit N and smectic A (SmA) phases. However, the homologs with odd number of members display enantiotropic phase in which the compound with n = 5 exhibits N and smectic phases whereas the members with n = 7 and 9 are predominantly smectogenic. The temperature range of N phase for even-numbered member decreased with elongation of the outer spacer. The smectic phase stability among the members in the present series increases when the outer spacer n is increased from 5 to 8. Graphical Abstract
Label Disentanglement in Partition-based Extreme Multilabel Classification

Partition-based methods are increasingly-used in extreme multi-label classification (XMC) problems due to their scalability to large output spaces (e.g., millions or more). However, existing methods partition the large label space into mutually exclusive clusters, which is sub-optimal when labels have multi-modality and rich semantics. For instance, the label"Apple"can be the fruit or the brand name, which leads to the following research question: can we disentangle these multi-modal labels with non-exclusive clustering tailored for downstream XMC tasks? In this paper, we show that the label assignment problem in partition-based XMC can be formulated as an optimization problem, with the objective of maximizing precision rates. This leads to an efficient algorithm to form flexible and overlapped label clusters, and a method that can alternatively optimizes the cluster assignments and the model parameters for partition-based XMC. Experimental results on synthetic and real datasets show that our method can successfully disentangle multi-modal labels, leading to state-of-the-art (SOTA) results on four XMC benchmarks.
Comparison between safety methods control barrier function vs. reachability analysis

This report aims to compare two safety methods: control barrier function and Hamilton-Jacobi reachability analysis. We will consider the difference with a focus on the following aspects: generality of system dynamics, difficulty of construction and computation cost. A standard Dubins car model will be evaluated numerically to make the comparison more concrete.
Fairness via Representation Neutralization

Existing bias mitigation methods for DNN models primarily work on learning debiased encoders. This process not only requires a lot of instance-level annotations for sensitive attributes, it also does not guarantee that all fairness sensitive information has been removed from the encoder. To address these limitations, we explore the following research question: Can we reduce the discrimination of DNN models by only debiasing the classification head, even with biased representations as inputs? To this end, we propose a new mitigation technique, namely, Representation Neutralization for Fairness (RNF) that achieves fairness by debiasing only the task-specific classification head of DNN models. To this end, we leverage samples with the same ground-truth label but different sensitive attributes, and use their neutralized representations to train the classification head of the DNN model. The key idea of RNF is to discourage the classification head from capturing spurious correlation between fairness sensitive information in encoder representations with specific class labels. To address low-resource settings with no access to sensitive attribute annotations, we leverage a bias-amplified model to generate proxy annotations for sensitive attributes. Experimental results over several benchmark datasets demonstrate our RNF framework to effectively reduce discrimination of DNN models with minimal degradation in task-specific performance.
Quantum Error Correction with the Gottesman-Kitaev-Preskill Code

The Gottesman-Kitaev-Preskill (GKP) code was proposed in 2001 by Daniel Gottesman, Alexei Kitaev, and John Preskill as a way to encode a qubit in an oscillator. The GKP codewords are coherent superpositions of periodically displaced squeezed vacuum states. Because of the challenge of merely preparing the codewords, the GKP code was for a long time considered to be impractical. However, the remarkable developments in quantum hardware and control technology in the last two decades has made the GKP code a frontrunner in the race to build practical, fault-tolerant bosonic quantum technology. In this Perspective, we provide an overview of the GKP code with emphasis on its implementation in the circuit-QED architecture and present our outlook on the challenges and opportunities for scaling it up for hardware-efficient, fault-tolerant quantum error correction.
Fast ORB-SLAM with Descriptor Independent Keypoint Matching

—Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 [1] is a benchmark method in this domain, however, it consumes signiﬁcant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is light-weight and efﬁcient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two stage coarse-to-ﬁne descriptor independent keypoint matching method is proposed based on sparse optical ﬂow. In the ﬁrst stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical ﬂow tracking. In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to reﬁne the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on TUM and ICL-NUIM RGB-D datasets and compare its accuracy and efﬁciency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2.
Synthesis and Characterization of Ca-Substituted Infinite-Layer Nickelate Crystals

Rare-earth nickelates with the infinite-layer crystal structure have been synthesized in thin film and powder form via topotactic oxygen reduction of the perovskite phase. The infinite-layer phase exhibits remarkable properties, such as superconductivity and magnetic excitations with extraordinarily large bandwidth. Yet, superconductivity was exclusively reported for infinite-layer nickelate films, while polycrystalline powder samples of similar composition were insulating at all measured temperatures. Here, a high-pressure method was used to synthesize high-quality single crystals of the perovskite nickelate La1−xCaxNiO3 that were subsequently reduced to the infinite-layer phase La1−xCaxNiO2+δ. The obtained samples were characterized by X-ray diffraction, electron microscopy, Raman spectroscopy, magnetometry, and electrical transport measurements. Notably, the metal-like electrical conductivity of the infinite-layer crystals is reminiscent of weakly hole-doped infinite-layer thin films. Moreover, local electron energy-loss spectroscopy reveals close similarities between the electronic structures of the crystals and thin films. This work demonstrates the realization of infinite-layer nickelate crystals with macroscopic size as well as superior crystalline quality, and paves the way for future studies exploring whether more heavily Ca-substituted crystals host superconductivity in analogy to sufficiently hole-doped films.
Resource Management for Transmit Power Minimization in UAV-Assisted RIS HetNets Supported by Dual Connectivity

This paper proposes a novel approach to improve the performance of a heterogeneous network (HetNet) supported by dual connectivity (DC) by adopting multiple unmanned aerial vehicles (UAVs) as passive relays that carry reconfigurable intelligent surfaces (RISs). More specifically, RISs are deployed under the UAVs termed as UAVs-RISs that operate over the micro-wave (<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> channel based on orthogonal multiple access (OMA), while small base stations (SBSs) operate over the millimeter-wave (mmW) channel based on non-orthogonal multiple access (NOMA). We study the problem of total transmit power minimization by jointly optimizing the trajectory/velocity of each UAV, RISs’ phase shifts, subcarrier allocations, and active beamformers at each BS. The underlying problem is highly non-convex and the global optimal solution is intractable. To handle it, we decompose the original problem into two subproblems, i.e., a subproblem which deals with the UAVs’ trajectories/velocities, RISs’ phase shifts, and subcarrier allocations for <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>; and a subproblem for active beamforming design and subcarrier allocation for mmW. In particular, we solve the first subproblem via the dueling deep Q-Network (DQN) learning approach by developing a distributed algorithm which leads to a better policy evaluation. Then, we solve the active beamforming design and subcarrier allocation for the mmW via the successive convex approximation (SCA) method. Simulation results exhibit the effectiveness of the proposed resource allocation scheme compared to other baseline schemes. In particular, it is revealed that by deploying UAVs-RISs, the transmit power can be reduced by 6 dBm while maintaining similar guaranteed QoS.
A finite element elasticity complex in three dimensions

A finite element elasticity complex on tetrahedral meshes is devised. The $H^1$ conforming finite element is the smooth finite element developed by Neilan for the velocity field in a discrete Stokes complex. The symmetric div-conforming finite element is the Hu-Zhang element for stress tensors. The construction of an $H(\textrm{inc})$-conforming finite element for symmetric tensors is the main focus of this paper. The key tools of the construction are the decomposition of polynomial tensor spaces and the characterization of the trace of the $\textrm{inc}$ operator. The polynomial elasticity complex and Koszul elasticity complex are created to derive the decomposition of polynomial tensor spaces. The trace of the $\textrm{inc}$ operator is induced from a Green's identity. Trace complexes and bubble complexes are also derived to facilitate the construction. Our construction appears to be the first $H(\textrm{inc})$-conforming finite elements on tetrahedral meshes without further splits.
User Scheduling for Precoded Satellite Systems with Individual Quality of Service Constraints

Multibeam high throughput satellite (MB-HTS) systems will play a key role in delivering broadband services to a large number of users with diverse Quality of Service (QoS) requirements. This paper focuses on MB-HTS where the same spectrum is re-used by all user links and, in particular, we propose a novel user scheduling design capable to provide guarantees in terms of individual QoS requirements while maximizing the system throughput. This is achieved by precoding to mitigate mutual interference. The combinatorial optimization structure requires an extremely high cost to obtain the global optimum even with a reduced number of users. We, therefore, propose a heuristic algorithm yielding a good local solution and tolerable computational complexity, applicable for large-scale networks. Numerical results demonstrate the effectiveness of our proposed algorithm on scheduling many users with better sum throughput than the other benchmarks. Besides, the QoS requirements for all scheduled users are guaranteed.
Membraneless organelles formed by liquid-liquid phase separation increase bacterial fitness

Liquid-liquid phase separation is emerging as a crucial phenomenon in several fundamental cell processes. A range of eukaryotic systems exhibit liquid condensates. However, their function in bacteria, which in general lack membrane-bound compartments, remains less clear. Here, we used high-resolution optical microscopy to observe single bacterial aggresomes, nanostructured intracellular assemblies of proteins, to undercover their role in cell stress. We find that proteins inside aggresomes are mobile and undergo dynamic turnover, consistent with a liquid state. Our observations are in quantitative agreement with phase-separated liquid droplet formation driven by interacting proteins under thermal equilibrium that nucleate following diffusive collisions in the cytoplasm. We have discovered aggresomes in multiple species of bacteria, and show that these emergent, metastable liquid-structured protein assemblies increase bacterial fitness by enabling cells to tolerate environmental stresses. One Sentence Summary Bacteria use subcellular proteinaceous liquid droplets to survive stress
Correlation between Spin and Orbital Dynamics during Laser-Induced Femtosecond Demagnetization

Spin and orbital angular momenta are two intrinsic properties of an electron and are responsible for the physics of a solid. How the spin and orbital evolve with respect to each other on several hundred femtoseconds is largely unknown, but it is at the center of laser-induced ultrafast demagnetization. In this paper, we introduce a concept of the spin-orbital correlation diagram, where spin angular momentum is plotted against orbital angular momentum, much like the position-velocity phase diagram in classical mechanics. We use four sets of highly accurate time-resolved x-ray magnetic circular dichroism (TR-XMCD) data to construct four correlation diagrams for iron and cobalt. To our surprise, a pattern emerges. The trace on the correlation diagram for iron is an arc, and at the end of demagnetization, it has a pronounced cusp. The correlation diagram for cobalt is different and appears more linear, but with kinks. We carry out first-principles calculations with two different methods: time-dependent density functional theory (TDDFT) and time-dependent Liouville density functional theory (TDLDFT). These two methods agree that the experimental findings for both Fe and Co are not due to experimental errors. It is the spin-orbit coupling that correlates the spin dynamics to the orbital dynamics. Microscopically, Fe and Co have different orbital occupations, which leads to distinctive correlation diagrams. We believe that this correlation diagram presents a useful tool to better understand spin and orbital dynamics on an ultrafast time scale. A brief discussion on the magnetic anisotropy energy is also provided. PACS numbers: 75.40. Gb, 78.20. Ls, 75.70. -i, 78.47. J
An open source MATLAB ® package to perform basic statistical analysis of turbulence data and other complex systems along with its application to Fokker-Planck equation and Integral ﬂuctuation theorem

We present a user-friendly open-source MATLAB ® package developed by the research group Turbulence, Wind energy and Stochastics (TWiSt) at the Carl von Ossietzky University of Oldenburg. This package enables to perform a standard analysis of given turbulent data and extracts the stochastic equations describing the scale-dependent cascade process in turbulent ﬂows through Fokker-Planck equations. As a precondition, Markovian properties of the process in scale are tested. Such a stochastic scale-dependent cascade process allows a comprehensive statistical description in terms of the complexity of the data. Cascade trajectories can be deﬁned as single events, for each of which a total entropy production can be determined. For such entropy ﬂuctuations a rigorous law of non-equilibrium stochastic thermodynamics, namely the integral ﬂuctuation theorem, will be veriﬁed. As the analysis of the scale-dependent cascade process through a hierarchy of spatial and temporal scales in turbulent ﬂows is an integral part of turbulence theory, this interdisciplinary treatment of the turbulent cascade process has the potential for a new way to link the statistical description of turbulence (via common two-point increment statistics), non-equilibrium stochastic thermodynamics and local turbulent ﬂow structures. The presented package can be used also for the analysis of other data with turbulent like complexity.
APure HTTP/3 Alternative to MQTT-over-QUIC in Resource-Constrained IoT

In this paper, we address the issue of scalable, inter-operable, and timely dissemination of information in resource-constrained IoT. Scalability is addressed by adopting a publish-subscribe architecture. To address interoperable and timely dissemination, we propose an HTTP/3 (H3) solution that exploits the wide-ranging improvements made over H2. We evaluated our solution by comparing it to a state-of-the-art work: MQTT-over-QUIC. Because QUIC and H3 have undergone standardization in tandem, we hypothesized that H3 would take better advantage of QUIC transport than an MQTT mapping would. Performance, network overhead, and device overhead were investigated for both protocols. Our H3-based solution satisfied our timely dissemination requirement by offering a key performance savings of 1 RoundTrip Time (RTT) for publish messages to arrive at the broker. In IoT networks, with typically high RTT, this savings is significant. On the other hand, we found that MQTT-over-QUIC put marginally less strain over the network.
Interacting dark energy from redshift-space galaxy clustering

Interacting dark energy models have been proposed as attractive alternatives to ΛCDM. Forthcoming Stage-IV galaxy clustering surveys will constrain these models, but they require accurate modelling of the galaxy power spectrum multipoles on mildly non-linear scales. In this work we consider a dark scattering model with a simple 1-parameter extension to wCDM — adding only A, which describes a pure momentum exchange between dark energy and dark matter. We then provide a comprehensive comparison of three approaches of modeling non-linearities, while including the effects of this dark sector coupling. We base our modeling of non-linearities on the two most popular perturbation theory approaches: TNS and EFTofLSS. To test the validity and precision of the modelling, we perform an MCMC analysis using simulated data corresponding to a ΛCDM fiducial cosmology and Stage-IV surveys specifications in two redshift bins, z=0.5 and z=1. We find the most complex EFTofLSS-based model studied to be better suited at both, describing the mock data up to smaller scales, and extracting the most information. Using this model, we forecast uncertainties on the dark energy equation of state, w, and on the interaction parameter, A, finding σ_w=0.06 and σ A =1.1 b/GeV for the analysis at z=0.5 and σ w =0.06 and σ A =2.0 b/GeV for the analysis at z=1. In addition, we show that a false detection of exotic dark energy up to 3σ would occur should the non-linear modelling be incorrect, demonstrating the importance of the validation stage for accurate interpretation of measurements.
A revised density split statistic model for general filters

Studying the statistical properties of the large-scale structure in the Universe with weak gravitational lensing is a prime goal of several current and forthcoming galaxy surveys. The power that weak lensing has to constrain cosmological parameters can be enhanced by considering statistics beyond second-order shear correlation functions or power spectra. One such higher-order probe that has proven successful in observational data is the density split statistics (DSS), in which one analyses the mean shear profiles around points that are classified according to their foreground galaxy density. In this paper, we generalise the most accurate DSS model to allow for a broad class of angular filter functions used for the classification of the different local density regions. This approach is motivated by earlier findings showing that an optimised filter can provide tighter constraints on model parameters compared to the standard top-hat case. We build on large deviation theory approaches and approximations thereof to model the matter density PDF, and on perturbative calculations of higher-order moments of the density field. The novel addition relies on the generalisation of these previously employed calculations to allow for general filter functions and is validated on several sets of numerical simulations. The revised model fits well the simulation measurements, with a residual systematic offset that is small compared to the statistical accuracy of current weak lensing surveys. The accuracy of the model is slightly lower for a compensated filter than for a non-negative filter function, and that it increases with the filter size. Using a Fisher matrix approach, we find constraints comparable to the commonly used two-point cosmic shear measures. Hence, our DSS model can be used in competitive analyses of current cosmic shear data, while it may need refinements for forthcoming lensing surveys.
Perceptually Guided Adversarial Perturbations

It is well known that carefully crafted imperceptible perturbations can cause state-of-the art deep learning classiﬁ-cation models to misclassify. Understanding and analyzing these adversarial perturbations play a crucial role in the design of robust convolutional neural networks. However, the reasons for the existence of adversarial perturbations and their mechanics are not well understood. In this work, we attempt to systematically answer the following question: do imperceptible adversarial perturbations focus on changing the regions of the image that are important for classiﬁ-cation? Most current methods use l p distance to generate and characterize the imperceptibility of the adversarial perturbations. However, since l p distances only measure the pixel to pixel distances and do not consider the structure in the image, these methods do not provide a satisfactory answer to the above question. To address this issue, we propose a novel framework for generating adversarial perturbations by explicitly incorporating a “perceptual quality ball” constraint in our formulation. Speciﬁcally, we pose the adversarial example generation problem as a tractable convex optimization problem, with constraints taken from a mathematically amenable variant of the popular SSIM index. We show that the perturbations generated by the proposed method result in a high fooling rate with minimal impact on perceptual quality compared to the norm bounded adversarial perturbations. Further, through SSIM maps, we show that the perceptually guided perturbations introduce changes speciﬁcally in the regions that contribute to classi-ﬁcation decisions. We use networks trained on MNIST and CIFAR-10 datasets quantitative analysis