Miracle-dz/UniFAR-FTUs huggingface dataset:
Cavity hairpin ThT-light nucleic acid switches: the construction of label- and enzyme-free sensing and imaging platforms

Abstract Thioflavin T (ThT) is a classical fluorescent dye gaining prominence in current research regarding nucleic acid conformations (NACs). However, most NACs with the ability to excite ThT fluorescent are unique or form in demanding conditions, limiting the extensiveness and depth of ThT application in sensing and imaging. Therefore, this study proposed CGG-AAA mismatched cavity hairpin ThT-light nucleic acid switches (CHTLNAS) with excellent fluorescence excitation over 500-fold higher than spontaneous, 17∼20-fold higher than ssDNA and 2.5∼5-fold higher than complementary duplex. Based on the excellent fluorescence excitation, convenient conformation formation, good sequence programmability, and flexible allosteric ability (known as the Worm-crack pod mechanism mediated by the target), it achieved the label- and enzyme-free detection of tetracycline (TET) and berberine (BB) at the pM level within 10 min. Moreover, it was found enable to realize the sensitive tracking of intracellular carriers at the nM level of ThT entry concentration, and prolongated its cell nuclear-entry time of ThT over 8 h, overcoming the non-specific high background signal interference of ThT in the nuclear region, and expanding the diversified application of ThT in cell biology research. Therefore, CHTLNAS is a more universal, practical tool than G-quadruplex or other kinds of NACs for ThT development and utilization in sensing and imaging platforms.
Radial Basis Approximation Functions In TheBoundary Element Dual Reciprocity Method

The most important class of functions employed in the Dual Reciprocity Method are the Radial Basis Functions, which are used both on their own or combined with augmentation functions. Since the publication of a book about the method in 1992 [2] there has been much research into the choice of Functions employed with the method. Many new functions have been proposed and used to solve engineering problems. Given this wealth of new functions, it is necessary to know something about the properties and behaviour of each in order to be able to define an adequate function for a given problem. To this end in this paper some examples are solved using a wide range of functions and the results obtained with each function compared.
A comprehensive and systemic review of ginseng‐based nanomaterials: Synthesis, targeted delivery, and biomedical applications

Among 17 Panax species identified across the world, Panax ginseng (Korean ginseng), Panax quinquefolius (American ginseng), and Panax notoginseng (Chinese ginseng) are highly recognized for the presence of bioactive compound, ginsenosides and their pharmacological effects. P. ginseng is widely used for synthesis of different types of nanoparticles compared to P. quinquefolius and P. notoginseng. The use of nano‐ginseng could increase the oral bioavailability, membrane permeability, and thus provide effective delivery of ginsenosides to the target sites through transport system. In this review, we explore the synthesis of ginseng nanoparticles using plant extracts from various organs, microbes, and polymers, as well as their biomedical applications. Furthermore, we highlight transporters involved in transport of ginsenoside nanoparticles to the target sites. Size, zeta potential, temperature, and pH are also discussed as the critical parameters affecting the quality of ginseng nanoparticles synthesis.
A driven two-component BEC: Chaos in a Macroscopic Quantum System

This work discusses the experimental realization of a mixed regular-chaotic phase space in a macroscopic quantum system. The effective Planck's constant of the system can be varied in a large range. Therefore, the semi-classical limit of the system can be studied, where the initial size of the many particle wave packet is much smaller than typical structures of the phase space. Thus, different structures of the phase space can be resolved. In a weakly driven system, the Poincare-Birkhoff scenario is investigated by the dispersion of the wave packet at a stable or unstable fixed point. In a strongly driven system, different dynamics of a wave packet is found, dependent on preparation in a regular island or the chaotic sea. The time evolution can be described by a classical model, which is in good agreement with experiment as well as the mean field model for the considered observables. In an independent experiment using an atomic beam setup the generation of motional coherence of a single atom by spontaneous emission of a single photon has been demonstrated.
Stress‐sensitive dynamics of miRNAs and Elba1 in Drosophila embryogenesis

Early‐life stress can result in life‐long effects that impact adult health and disease risk, but little is known about how such programming is established and maintained. Here, we show that such epigenetic memories can be initiated in the Drosophila embryo before the major wave of zygotic transcription, and higher‐order chromatin structures are established. An early short heat shock results in elevated levels of maternal miRNA and reduced levels of a subgroup of zygotic genes in stage 5 embryos. Using a Dicer‐1 mutant, we show that the stress‐induced decrease in one of these genes, the insulator‐binding factor Elba1, is dependent on functional miRNA biogenesis. Reduction in Elba1 correlates with the upregulation of early developmental genes and promotes a sustained weakening of heterochromatin in the adult fly as indicated by an increased expression of the PEV wm4h reporter. We propose that maternal miRNAs, retained in response to an early embryonic heat shock, shape the subsequent de novo heterochromatin establishment that occurs during early development via direct or indirect regulation of some of the earliest expressed genes, including Elba1.
Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation

Understanding and manipulating deformable objects (e.g., ropes and fabrics) is an essential yet challenging task with broad applications. Difficulties come from complex states and dynamics, diverse configurations and high-dimensional action space of deformable objects. Besides, the manipulation tasks usually require multiple steps to accomplish, and greedy policies may easily lead to local optimal states. Existing studies usually tackle this problem using reinforcement learning or imitating expert demonstrations, with limitations in modeling complex states or requiring hand-crafted expert policies. In this paper, we study deformable object manipulation using dense visual affordance, with generalization towards diverse states, and propose a novel kind of foresightful dense affordance, which avoids local optima by estimating states’ values for long-term manipulation. We propose a framework for learning this representation, with novel designs such as multi-stage stable learning and efficient self-supervised data collection without experts. Experiments demonstrate the superiority of our proposed foresightful dense affordance. Project page: https://hyperplane-lab.github. io/DeformableAffordance
Optoelectronic Noise and Photocurrent Measurement on GaAs/AlGaAs Laser Diode with Single Quantum Well

The current progress in optomechatronic applications needs more efficient devices and components with higher reliability, operational lifetime, reduced cost, and dimensions as laser diodes, fast photodetectors, and modulators. The laser is one key technology in a highly industrialized economy. To understand modern efficient laser diode (LD) structures and evaluate their performance, an electrical and optical characterization is necessary. Therefore, low-frequency electrical and optical fluctuation spectra and their cross-correlation factor have been investigated in GaAs/AlGaAs ridge waveguide laser diode with an active region containing a single quantum well layer. It is shown that in the lasing region the intensive Lorentzian-type electrical and optical noises with negative cross-correlation factor are characteristic. This noise is due to charge carrier recombination processes in the interfaces between the active layer and the neighboring layers. To verify it, a complementary insight into the same laser diode structure provided by near-field-induced photocurrent technique with 100 nm spatial resolution is also discussed.
DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models

Many large vision models have been deployed on the cloud for real-time services. Meanwhile, fresh samples are continuously generated on the served mobile device. How to leverage the device-side samples to improve the cloud-side large model becomes a practical requirement, but falls into the dilemma of no raw sample up-link and no large model down-link. Specifically, the user may opt out of sharing raw samples with the cloud due to the concern of privacy or communication overhead, while the size of some large vision models far exceeds the mobile device's runtime capacity. In this work, we propose a device-cloud collaborative controlled learning framework, called DC-CCL, enabling a cloud-side large vision model that cannot be directly deployed on the mobile device to still benefit from the device-side local samples. In particular, DC-CCL vertically splits the base model into two submodels, one large submodel for learning from the cloud-side samples and the other small submodel for learning from the device-side samples and performing device-cloud knowledge fusion. Nevertheless, on-device training of the small submodel requires the output of the cloud-side large submodel to compute the desired gradients. DC-CCL thus introduces a light-weight model to mimic the large cloud-side submodel with knowledge distillation, which can be offloaded to the mobile device to control its small submodel's optimization direction. Given the decoupling nature of two submodels in collaborative learning, DC-CCL also allows the cloud to take a pre-trained model and the mobile device to take another model with a different backbone architecture.
Quasiperiodicity in the $\alpha-$Fermi-Pasta-Ulam-Tsingou problem revisited: an approach using ideas from wave turbulence

The Fermi-Pasta-Ulam-Tsingou (FPUT) problem addresses fundamental questions in statistical physics, and attempts to understand the origin of recurrences in the system have led to many great advances in nonlinear dynamics and mathematical physics. In this work we revisit the problem and study quasiperiodic recurrences in the weakly nonlinear $\alpha-$FPUT system in more detail. We aim to reconstruct the quasiperiodic behaviour observed in the original paper from the canonical transformation used to remove the three wave interactions, which is necessary before applying the wave turbulence formalism. We expect the construction to match the observed quasiperiodicity if we are in the weakly nonlinear regime. Surprisingly, in our work we show that this is not always the case and in particular, the recurrences observed in the original paper cannot be constructed by our method. We attribute this disagreement to the presence of small denominators in the canonical transformation used to remove the three wave interactions before arriving at the starting point of wave turbulence. We also show that these small denominators are present even in the weakly nonlinear regime, and they become more significant as the system size is increased. We also discuss our results in the context of the problem of equilibration in the $\alpha-$FPUT system, and point out some mathematical challenges when the wave turbulence formalism is applied to explain thermalization in the $\alpha-$FPUT problem. We argue that certain aspects of the $\alpha-$FPUT system such as presence of the stochasticity threshold, thermalization in the thermodynamic limit and the cause of quasiperiodicity are not clear, and that they require further mathematical and numerical studies.
Efficient Lyapunov-Based Stabilizability and Detectability Tests: From LTI to LPV Systems

In this technical note, we generalize the well-known Lyapunov-based stabilizability and detectability tests for linear time-invariant (LTI) systems to the context of discrete-time (DT) polytopic linear parameter-varying (LPV) systems. To do so, we exploit the mathematical structure of the class of poly-quadratic Lyapunov functions, which enables us to formulate conditions in the form of linear matrix inequalities (LMIs). Our results differ from existing conditions in that we adopt weaker assumptions on the parameter dependence of the controllers/observers and our method does not require explicitly incorporating these gains, which renders the new conditions less computationally demanding. Interestingly, our results also have important implications for existing controller and observer synthesis techniques based on poly-QLFs. In fact, we show that existing observer synthesis results are stronger than was anticipated in the sense that they are necessary for a larger class of observers. Moreover, we also introduce new controller synthesis conditions and illustrate our results by means of a numerical case study.
On Correlation Functions as Higher-Spin Invariants

(Chern–Simons) vector models exhibit an infinite-dimensional symmetry, the slightly-broken higher-spin symmetry with the unbroken higher-spin symmetry being the first approximation. In this note, we compute the n-point correlation functions of the higher-spin currents as higher-spin invariants directly on the CFT side, which complements earlier results that have a holographic perspective.
Hessenberg-Toeplitz matrix determinants with Schröder and Fine number entries

In this paper, we find determinant formulas of several Hessenberg-Toeplitz matrices whose nonzero entries are derived from the small and large Schröder and Fine number sequences. Algebraic proofs of these results can be given which make use of Trudi's formula and the generating function of the associated sequence of determinants. We also provide direct arguments of our results that utilize various counting techniques, among them sign-changing involutions, on combinatorial structures related to classes of lattice paths enumerated by the Schröder and Fine numbers. As a consequence of our results, we obtain some new formulas for the Schröder and Catalan numbers as well as for some additional sequences from the OEIS in terms of determinants of certain Hessenberg-Toeplitz matrices.
BlanketSet - A Clinical Real-World In-Bed Action Recognition and Qualitative Semi-Synchronised Motion Capture Dataset

Clinical in-bed video-based human motion analysis is a very relevant computer vision topic for several relevant biomedical applications. Nevertheless, the main public large datasets (e.g. ImageNet or 3DPW) used for deep learning approaches lack annotated examples for these clinical scenarios. To address this issue, we introduce BlanketSet, an RGB-IR-D action recognition dataset of sequences performed in a hospital bed. This dataset has the potential to help bridge the improvements attained in more general large datasets to these clinical scenarios. Information on how to access the dataset is available at rdm.inesctec. pt/dataset/nis-2022-004.
Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching

The matching of 3D shapes has been extensively studied for shapes represented as surface meshes, as well as for shapes represented as point clouds. While point clouds are a common representation of raw real-world 3D data (e.g. from laser scanners), meshes encode rich and expressive topological information, but their creation typically requires some form of (often manual) curation. In turn, methods that purely rely on point clouds are unable to meet the matching quality of mesh-based methods that utilise the additional topological structure. In this work we close this gap by introducing a self-supervised multimodal learning strategy that combines mesh-based functional map regularisation with a contrastive loss that couples mesh and point cloud data. Our shape matching approach allows to obtain intramodal correspondences for triangle meshes, complete point clouds, and partially observed point clouds, as well as correspondences across these data modalities. We demonstrate that our method achieves state-of-the-art results on several challenging benchmark datasets even in comparison to recent supervised methods, and that our method reaches previously unseen cross-dataset generalisation ability. Our code is available at https://github.com/dongliangcao/Self-Supervised-Multimodal-Shape-Matching.
IMF: Interactive Multimodal Fusion Model for Link Prediction

Link prediction aims to identify potential missing triples in knowledge graphs. To get better results, some recent studies have introduced multimodal information to link prediction. However, these methods utilize multimodal information separately and neglect the complicated interaction between different modalities. In this paper, we aim at better modeling the inter-modality information and thus introduce a novel Interactive Multimodal Fusion (IMF) model to integrate knowledge from different modalities. To this end, we propose a two-stage multimodal fusion framework to preserve modality-specific knowledge as well as take advantage of the complementarity between different modalities. Instead of directly projecting different modalities into a unified space, our multimodal fusion module limits the representations of different modalities independent while leverages bilinear pooling for fusion and incorporates contrastive learning as additional constraints. Furthermore, the decision fusion module delivers the learned weighted average over the predictions of all modalities to better incorporate the complementarity of different modalities. Our approach has been demonstrated to be effective through empirical evaluations on several real-world datasets. The implementation code is available online at https://github.com/HestiaSky/IMF-Pytorch.
Constitutive theory of saturated porous media considering porosity-dependent skeleton strain and chemical activity

In order to reveal the coupling effect among the chemical activity and the hydraulic seepage as well as the mechanical properties, a constitutive theoretical framework considering the chemical activity for saturated porous media is derived from the mixture theory incorporated with the chemical thermodynamics. First, to highlight the important role of porosity in the hydro-mechanical-chemical multi-field coupling mechanism, the solid strain is divided into the porosity-dependent skeleton strain, the matrix strain and the mass-exchange strain. The stress and strain state variables are determined from the energy-conjugated form of the energy balance equation for establishing constitutive equations. Second, under infinitesimal case, general elastic constitutive equations including the relationship between mass fraction and its chemical potential are expressed by the free energy potential. Plastic model and the constitutive equation of thermodynamic flux and force are derived from the dissipative potential. Finally, under the guide of this theoretical framework, the complete swelling constitutive models in the confined compression are established for bentonite. The corresponding governing equations are formulated for multi-field two-phase saturated porous media. Compared with the experimental data, the proposed model can well reflect the chemical and mechanic coupling characteristics of representative elementary volume in the different NaCl concentrations of solution for saturated bentonite.
Conformal Generative Modeling on Triangulated Surfaces

We propose conformal generative modeling, a framework for generative modeling on 2D surfaces approximated by discrete triangle meshes. Our approach leverages advances in discrete conformal geometry to develop a map from a source triangle mesh to a target triangle mesh of a simple manifold such as a sphere. After accounting for errors due to the mesh discretization, we can use any generative modeling approach developed for simple manifolds as a plug-and-play subroutine. We demonstrate our framework on multiple complicated manifolds and multiple generative modeling subroutines, where we show that our approach can learn good estimates of distributions on meshes from samples, and can also learn simultaneously from multiple distinct meshes of the same underlying manifold.
Dark siren cosmology with binary black holes in the era of third-generation gravitational wave detectors

Third-generation (3G) gravitational wave detectors, in particular Einstein Telescope (ET) and Cosmic Explorer (CE), will explore unprecedented cosmic volumes in search for compact binary mergers, providing us with tens of thousands of detections per year. In this study, we simulate and employ binary black holes detected by 3G interferometers as dark sirens, to extract and infer cosmological parameters by cross-matching gravitational wave data with electromagnetic information retrieved from a simulated galaxy catalog. Considering a standard $\Lambda$CDM model, we apply a suitable Bayesian framework to obtain joint posterior distributions for the Hubble constant $H_0$ and the matter energy density parameter $\Omega_m$ in different scenarios. Assuming a galaxy catalog complete up to $z=1$ and dark sirens detected with a network signal-to-noise ratio greater than 300, we show that a network made of ET and two CEs can constrain $H_0$ ($\Omega_m$) to a promising $0.8\%$ ($10.0\%$) at $90\%$ confidence interval within one year of continuous observations. Additionally, we find that most of the information on $H_0$ is contained in local, single-host dark sirens, and that dark sirens at $z>1$ do not substantially improve these estimates. Our results imply that a sub-percent measure of $H_0$ can confidently be attained by a network of 3G detectors, highlighting the need for characterising all systematic effects to a higher accuracy.
Low-complexity processing of randomly correlated signals

edu Abstract - Linear pre-processing of systems with large numbers of concurrent channels such as asyn- chronous CDMA systems or MIMO systems is used to transform the multi-access channel into parallel single-user channels. The linear filters such as decor- relator and MMSE filters typically have a cubic com- plexity in terms of the filter size. We study the infor- mation capacities of low-complexity iterative approxi- mations of these linear filters, and show that for large operation ranges, these simple iterative filters provide nearly the same capacity as original complex filters. Conditional correlation reception of multi-access signals is limited by mutual interference, hence joint detection method have been studied for a number of years to improve the spec- tral efficiency. The optimal detection (2) has an exponential complexity, and is feasible only for very small systems. As a result, linear filters (l, 3)are considered as the low-complexity alternatives. Linear pre-processing can be understood as conditioning the channel between a single transmitter and its designated receiver. These filters turn the noise and interference into a Gaussian noise source affecting the equivalent single-user channel. The information theoretic capacity is then calculated using Shannon capacity formula for the AWGN channel (4). The linear reception of multi-access signals bears the general form as