Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly Sensitive Strain Sensor from Topological‐Structure Modulated Dielectric Elastic Nanocomposites

Flexible strain sensors are critical to several potential intelligent applications, such as human–machine interfaces, soft robotics, human motion detection, and safety monitoring of components. Stretchable functional materials are important components of strain sensors, and they are still a major challenge for high‐performance strain sensors. Herein, a novel strategy of designing and optimizing a flexible strain sensor by developing topological structure modulated high dielectric elastic nanocomposite is demonstrated. The topological structure produces synergistic effects of space charge enhancement and local electric field modulation, and it gives rise to an ultrahigh dielectric permittivity (113.4, at 1 kHz) and excellent comprehensive electromechanical performance for the dielectric elastic nanocomposite. An interdigital capacitive strain sensor is prepared based on the topological structured elastic dielectric nanocomposite, and it possesses positive capacitance response with strain, which breaks through disadvantages of negative sensitivity and narrow linear range for conventional interdigital strain sensors. The strain sensor achieves high signal‐to‐noise ratio, high positive capacitance response sensitivity of 5.7 pF %–1, and wide linear range (strain from 0.1% to 100%). The high permittivity elastic nanocomposite is capable to fabricate integrated microsensor array, making conditions for detecting local strain of convoluted surfaces and motion states of flexible actuators.
Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-Order Latent Domain

The crux of single-channel speech separation is how to encode the mixture of signals into such a latent embedding space that the signals from different speakers can be precisely separated. Existing methods for speech separation either transform the speech signals into frequency domain to perform separation or seek to learn a separable embedding space by constructing a latent domain based on convolutional filters. While the latter type of methods learning an embedding space achieves substantial improvement for speech separation, we argue that the embedding space defined by only one latent domain does not suffice to provide a thoroughly separable encoding space for speech separation. In this paper, we propose the Stepwise-Refining Speech Separation Network (SRSSN), which follows a coarse-to-fine separation framework. It first learns a 1-order latent domain to define an encoding space and thereby performs a rough separation in the coarse phase. Then the proposed SRSSN learns a new latent domain along each basis function of the existing latent domain to obtain a high-order latent domain in the refining phase, which enables our model to perform a refining separation to achieve a more precise speech separation. We demonstrate the effectiveness of our SRSSN by conducting extensive experiments, including speech separation in a clean (noise-free) setting on WSJ0-2/3mix datasets as well as in noisy/reverberant settings on WHAM! /WHAMR! datasets. Furthermore, we also perform experiments of speech recognition on separated speech signals by our model to evaluate the performance of speech separation indirectly.
The dark matter: DAMA/LIBRA and its perspectives

Experimental observations and theoretical arguments point out that Dark Matter (DM) particles are one of the most prominent component of the Universe. This motivated the pioneer DAMA experiment to investigate the presence of these particles in the galactic halo, by exploiting the model independent signature of the DM annual modulation of the rate and very highly radio-pure apparatus in underground site. In this paper the results obtained by other two annual cycles of DAMA/LIBRA-phase2 are presented and the long-standing model-independent annual modulation effect measured by DAMA deep underground at the Gran Sasso National Laboratory (LNGS) of the I.N. F.N. with different experimental configurations is summarized. The improved experimental configuration of DAMA/LIBRA-phase2, $\simeq$ 250 kg highly radio-pure NaI(Tl), allowed to lower the software energy threshold. The total exposure of DAMA/LIBRA-phase2 over 8 annual cycles is 1.53 ton $\times$ yr. DAMA/LIBRA-phase2 confirms the evidence of a signal that meets all the requirements of the model independent Dark Matter annual modulation signature, at 11.8 $\sigma$ C.L. in the energy region (1-6) keV. In the energy region between 2 and 6 keV, where data are also available from DAMA/NaI and DAMA/LIBRA-phase1 (2.86 ton $\times$ yr), the achieved C.L. is 13.7 $\sigma$; the modulation amplitude of the single-hit scintillation events is: $(0.01014 \pm 0.00074)$ cpd/kg/keV, the measured phase is $(142.4 \pm 4.2)$ days and the measured period is $(0.99834 \pm 0.00067)$ yr, all these values are well in agreement with those expected for DM particles. No systematics or side reaction able to mimic the exploited DM signature (i.e. to account for the whole measured modulation amplitude and to simultaneously satisfy all the requirements of the signature), has been found or suggested by anyone throughout some decades thus far.
DNA-polymer architecture orchestrates the segregation and spatio-temporal organization of E. coli chromosomes during replication in slow growth

The mechanism and driving forces of chromosome segregation in the bacterial cell cycle of E. coli is one of the least understood events in its life cycle. Using principles of entropic repulsion between polymer loops confined in a cylinder, we use Monte carlo simulations to show that the segregation dynamics is spontaneously enhanced by the adoption of a certain DNA-polymer architecture as replication progresses. Secondly, the chosen polymer-topology ensures its self-organization along the cell axis while segregation is in progress, such that various chromosomal loci get spatially localized. The time evolution of loci positions quantitatively match the corresponding experimentally reported results, including observation of the cohesion time and the ter-transition. Additionally, the contact map generated using our bead-spring model reproduces the four macro-domains of the experimental Hi-C maps. Lastly, the proposed mechanism reproduces the observed universal dynamics as the sister loci separate during segregation. It was already hypothesized and expected that SMC proteins, e.g. MukBEF contribute over and above entropic repulsion between bacterial-DNA ring-polymers to aid the segregation of daughter DNAs in the E.coli cell cycle. We propose that cross-links (plausibly induced by SMC proteins) at crucial positions along the contour is enough to provide sufficient forces for segregation within reasonable time scales. A mapping between Monte Carlo diffusive dynamics time scales and real time units helps us use experimentally relevant numbers for our modeling.
Learning Single/Multi-Attribute of Object With Symmetry and Group

Attributes and objects can compose diverse compositions. To model the compositional nature of these concepts, it is a good choice to learn them as transformations, e.g., coupling and decoupling. However, complex transformations need to satisfy specific principles to guarantee rationality. Here, we first propose a previously ignored principle of attribute-object transformation: <italic>Symmetry</italic>. For example, coupling <monospace>peeled-apple</monospace> with attribute <monospace>peeled</monospace> should result in <monospace>peeled-apple</monospace>, and decoupling <monospace>peeled</monospace> from <monospace>apple</monospace> should still output <monospace>apple</monospace>. Incorporating the symmetry, we propose a transformation framework inspired by group theory, i.e., SymNet. It consists of two modules: Coupling Network and Decoupling Network. We adopt deep neural networks to implement SymNet and train it in an end-to-end paradigm with the group axioms and symmetry as objectives. Then, we propose a Relative Moving Distance (RMD) based method to utilize the attribute change instead of the attribute pattern itself to classify attributes. Besides the compositions of single-attribute and object, our RMD is also suitable for complex compositions of multiple attributes and objects when incorporating attribute correlations. SymNet can be utilized for attribute learning, compositional zero-shot learning and outperforms the state-of-the-art on four widely-used benchmarks. Code is at <uri>https://github.com/DirtyHarryLYL/SymNet</uri>.
Nonminimally coupled ultralight axions as cold dark matter

We consider a nonminimally coupled scalar ﬁeld as a potential cold dark matter candidate. These models are natural extensions of the ultralight axion models which are based on minimally coupled scalar ﬁelds. Such ultralight scalar ﬁelds are motivated by string theory and, in particular, have been studied in the context of the axiverse scenario. For a nonminimally coupled ﬁeld, the scalar-ﬁeld energy density behaves as radiation at early times, which yields a bound on the coupling constant, ξ (cid:46) 10, from the primordial nucleosynthesis theory. The ﬁrst-order perturbations of the nonminimally coupled ﬁeld with adiabatic initial conditions cause the gravitational potential to decay on large scales. A comparison of the cosmological data with the theoretical matter power spectrum yields the following constraint on the coupling constant: ξ (cid:46) 0 . 01. We also consider isocurvature modes in our analysis. We argue that a mix of adiabatic and isocurvature initial conditions for a nonminimally coupled scalar ﬁeld might allow one to obtain the usual adiabatic CDM power spectrum.
A Quantitative Comparison of Physical Accuracy and Numerical Stability of Lattice Boltzmann Color Gradient and Pseudopotential Multicomponent Models for Microfluidic Applications

The performances of the Color-Gradient (CG) and of the Shan-Chen (SC) multicomponent Lattice Boltzmann models are quantitatively compared side-by-side on multiple physical flow problems where breakup, coalescence and contraction of fluid ligaments are important. The flow problems are relevant to microfluidic applications, jetting of microdroplets as seen in inkjet printing, as well as emulsion dynamics. A significantly wider range of parameters is shown to be accessible for CG in terms of density-ratio, viscosity-ratio and surface tension values. Numerical stability for a high density ratio $\mathcal{O}(1000)$ is required for simulating the drop formation process during inkjet printing which we show here to be achievable using the CG model but not using the SC model. In terms of physical accuracy, the CG model shows good agreement with analytical solutions for droplet oscillation and ligament contraction test-cases. The SC model is effective in accurately simulating ligament contraction, but less accurate in handling droplet oscillations. Rayleigh-Plateau instability simulations show that both the CG and SC models give physically realistic results when breakup occurs, although smaller satellite droplets quickly vanish in the SC model due to droplet mass evaporating into the ambient phase. Our results show that the CG model is a suitable choice for challenging simulations of droplet formation, due to a combination of both numerical stability and physical accuracy. We also present a novel approach to incorporate repulsion forces between interfaces for CG, with possible applications to the study of stabilized emulsions. Specifically, we show that the CG model can produce similar results to a known multirange potentials extension of the SC model for modelling a disjoining pressure, opening up its use for the study of dense stabilized emulsions.
On the energy landscape of symmetric quantum signal processing

Symmetric quantum signal processing provides a parameterized representation of a real polynomial, which can be translated into an efficient quantum circuit for performing a wide range of computational tasks on quantum computers. For a given polynomial f, the parameters (called phase factors) can be obtained by solving an optimization problem. However, the cost function is non-convex, and has a very complex energy landscape with numerous global and local minima. It is therefore surprising that the solution can be robustly obtained in practice, starting from a fixed initial guess Φ0 that contains no information of the input polynomial. To investigate this phenomenon, we first explicitly characterize all the global minima of the cost function. We then prove that one particular global minimum (called the maximal solution) belongs to a neighborhood of Φ0, on which the cost function is strongly convex under the condition ‖f‖∞=O(d−1) with d=deg(f). Our result provides a partial explanation of the aforementioned success of optimization algorithms.
Interpretable Aesthetic Analysis Model for Intelligent Photography Guidance Systems

An aesthetics evaluation model is at the heart of predicting users’ aesthetic experience and developing user interfaces with higher quality. However, previous methods on aesthetic evaluation largely ignore the interpretability of the model and are consequently not suitable for many human-computer interaction tasks. We solve this problem by using a hyper-network to learn the overall aesthetic rating as a combination of individual aesthetic attribute scores. We further introduce a specially designed attentional mechanism in attribute score estimators to enable the users to know exactly which parts/elements of visual inputs lead to the estimated score. We demonstrate our idea by designing an intelligent photography guidance system. Computational results and user studies demonstrate the interpretability and effectiveness of our method.
LSC-GAN: Latent Style Code Modeling for Continuous Image-to-image Translation

Image-to-image (I2I) translation is usually carried out among discrete domains. However, image domains, often corresponding to a physical value, are usually continuous. In other words, images gradually change with the value, and there exists no obvious gap between different domains. This paper intends to build the model for I2I translation among continuous varying domains. We first divide the whole domain coverage into discrete intervals, and explicitly model the latent style code for the center of each interval. To deal with continuous translation, we design the editing modules, changing the latent style code along two directions. These editing modules help to constrain the codes for domain centers during training, so that the model can better understand the relation among them. To have diverse results, the latent style code is further diversified with either the random noise or features from the reference image, giving the individual style code to the decoder for label-based or reference-based synthesis. Extensive experiments on age and viewing angle translation show that the proposed method can achieve high-quality results, and it is also flexible for users.
Beyond Road Extraction: A Dataset for Map Update using Aerial Images

The increasing availability of satellite and aerial imagery has sparked substantial interest in automatically updating street maps by processing aerial images. Until now, the community has largely focused on road extraction, where road networks are inferred from scratch from an aerial image. However, given that relatively high-quality maps exist in most parts of the world, in practice, inference approaches must be applied to update existing maps rather than infer new ones. With recent road extraction methods showing high accuracy, we argue that it is time to transition to the more practical map update task, where an existing map is updated by adding, removing, and shifting roads, without introducing errors in parts of the existing map that remain up-to-date. In this paper, we develop a new dataset called MUNO21 for the map update task, and show that it poses several new and interesting research challenges. We evaluate several state-of-the-art road extraction methods on MUNO21, and find that substantial further improvements in accuracy will be needed to realize automatic map update.
Tail Risk Inference via Expectiles in Heavy-Tailed Time Series

Abstract Expectiles define the only law-invariant, coherent and elicitable risk measure apart from the expectation. The popularity of expectile-based risk measures is steadily growing and their properties have been studied for independent data, but further results are needed to establish that extreme expectiles can be applied with the kind of dependent time series models relevant to finance. In this article we provide a basis for inference on extreme expectiles and expectile-based marginal expected shortfall in a general β-mixing context that encompasses ARMA and GARCH models with heavy-tailed innovations. Our methods allow the estimation of marginal (pertaining to the stationary distribution) and dynamic (conditional on the past) extreme expectile-based risk measures. Simulations and applications to financial returns show that the new estimators and confidence intervals greatly improve on existing ones when the data are dependent.
Formal and informal science advice in emergencies: COVID-19 in the UK

The importance of scientific advice to government gains greater recognition in emergencies but inevitably has to be done in an environment of uncertainty, with limited data and at high speed. Adapting existing structures is more effective than creating new ones in an emergency. Between emergencies, the UK has a structured scientific advice system, including Chief Scientific Advisers, scientists in government, regulatory bodies and independent expert committees, which were adapted to COVID-19 under the umbrella of the Scientific Advisory Group for Emergencies. These worked alongside networks of informal scientific advice, including internationally. Multiple sciences were needed, including from the social sciences and engineering in addition to clinical science and epidemiology, and these had to be integrated. A centrally directed clinical research programme helped provide practitioners robust evidence, with observational and interventional trials providing data for policy and testing treatments and vaccines. The scale of the emergency meant unavoidable tension between detailed work and speed, and between an integrated scientific view usable in decision-making and constructive challenge. While a final judgement of the UK scientific response will take time, everyone should be grateful to the thousands of scientists involved for the research, synthesis and advice, which improved outcomes for the public.
Staphylococcal Bacterial Persister Cells, Biofilms, and Intracellular Infection Are Disrupted by JD1, a Membrane-Damaging Small Molecule

Untreatable bacterial infections are a critical public health care issue. In addition to increasing antibiotic resistance, bacteria that are in slow-growing or nongrowing states, or that live inside mammalian cells, are typically insensitive to clinical antibiotics and therefore difficult to eradicate. ABSTRACT Rates of antibiotic and multidrug resistance are rapidly rising, leaving fewer options for successful treatment of bacterial infections. In addition to acquiring genetic resistance, many pathogens form persister cells, form biofilms, and/or cause intracellular infections that enable bacteria to withstand antibiotic treatment and serve as a source of recurring infections. JD1 is a small molecule previously shown to kill Gram-negative bacteria under conditions where the outer membrane and/or efflux pumps are disrupted. We show here that JD1 rapidly disrupts membrane potential and kills Gram-positive bacteria. Further investigation revealed that treatment with JD1 disrupts membrane barrier function and causes aberrant membranous structures to form. Additionally, exposure to JD1 reduced the number of Staphylococcus aureus and Staphylococcus epidermidis viable persister cells within broth culture by up to 1,000-fold and reduced the matrix and cell volume of biofilms that had been established for 24 h. Finally, we show that JD1 reduced the number of recoverable methicillin-resistant S. aureus organisms from infected cells. These observations indicate that JD1 inhibits staphylococcal cells in difficult-to-treat growth stages as well as, or better than, current clinical antibiotics. Thus, JD1 shows the importance of testing compounds under conditions that are relevant to infection, demonstrates the utility that membrane-targeting compounds have against multidrug-resistant bacteria, and indicates that small molecules that target bacterial cell membranes may serve as potent broad-spectrum antibacterials. IMPORTANCE Untreatable bacterial infections are a critical public health care issue. In addition to increasing antibiotic resistance, bacteria that are in slow-growing or nongrowing states, or that live inside mammalian cells, are typically insensitive to clinical antibiotics and therefore difficult to eradicate. Bacterial cell membranes have been proposed as potential novel antibiotic targets that may be vulnerable in these difficult to treat cell types because cell membranes are always present and performing essential functions. The small molecule JD1 was previously shown to disrupt Gram-negative bacterial cell membranes. Here, we show that it also disrupts the cell membrane of Gram-positive bacteria and reduces viable bacteria within persister populations, biofilms, and mammalian cells. These observations demonstrate the importance of testing novel compounds under infection-relevant conditions, because their potency against rapidly growing cells may not reveal their full potential.
Progress in Difluoroalkylation of Organic Substrates by Visible Light Photoredox Catalysis

Abstract The selective incorporation of fluorinated motifs, in particular CF2FG (FG=a functional group) and CF2H groups, into organic compounds has attrracted increasing attention since organofluorine molecules are of the utmost importance in the areas of nuclear imaging, pharmaceutical, agrochemical, and material sciences. A variety of synthetic approaches has been employed in late‐stage difluoroalkylation reactions. Visible light photoredox catalysis for the production of CF2FG and CF2H radicals has provided a more sustainable alternative to other conventional radical‐triggered reactions from the viewpoint of safety, cost, availability, and “green” chemistry. A wide range of difluoroalkylating reagents has been successfully implemented in these organic transformations in the presence of transition metal complexes or organic photocatalysts. In most cases, upon excitation via visible light irradiation with fluorescent light bulbs or blue light‐emitting diode (LED) lamps, these photocatalysts can act as both reductive and oxidative quenchers, thus enabling the application of electron‐donor or electron‐acceptor difluoroalkylating reagents for the generation of CF2FG and CF2H radicals. Subsequent radical addition to substrates and additional organic transformations afford the corresponding difluoroalkylated derivatives. The present review describes the distinct strategies for the transition metal‐ and organic‐photocatalyzed difluoroalkylation of a broad range of organic substrates by visible light irradiation reported in the literature since 2014.