Miracle-dz/UniFAR-FTUs huggingface dataset:
3 directional Inception-ResUNet: Deep spatial feature learning for multichannel singing voice separation with distortion

Singing voice separation on robots faces the problem of interpreting ambiguous auditory signals. The acoustic signal, which the humanoid robot perceives through its onboard microphones, is a mixture of singing voice, music, and noise, with distortion, attenuation, and reverberation. In this paper, we used the 3 directional Inception-Resnet structure in the U-shaped encoding and decoding network to improve the utilization of the spatial and spectral information of the spectrograms. Multi-objectives were used to train the model: magnitude consistency loss, phase consistency loss, and magnitude correlation consistency loss. We recorded the singing voice and accompaniment derived from the MIR-1k datasets with NAO robots and synthesized the 10-channel datasets for training the model. The experimental results show that the proposed model trained by multi-objective reaches an average NSDR of 11.55db on the test datasets, which outperforms the comparison model. Author summary The mixture in the real singing voice separation is always mixed with noise and distortion. In this paper, the acoustic signals with distortion and noise perceived by the robot are used to study the separation of singing voices in real scenes. This paper described how to synthesize the training datasets, proposed a 3 directional Inception-ResUNet structure for multichannel singing voice separation, and adopted multi-objectives including magnitude correlation consistency loss to train the model. The experimental results showed that the magnitude correlation consistency loss reduces distortions, the proposed model achieves better performance than the compared models.
Evolutionary reliable regional Kriging surrogate for expensive optimization

ABSTRACT This work proposes a sequential optimization algorithm, EORKS, combining a Kriging surrogate from an adaptive sampling and an iterative constrained search in the dynamic reliable regions to reduce the sampling size in expensive optimization. A surrogate established from small samples is liable to limited generality, which leads to a false prediction of optimum. EORKS applies Kriging variance to establish the reliable region neighbouring the learning samples to constrain the evolutionary searches of the surrogate. The verified quasi-optimum is used as an additional sample to dynamically update the regional model according to the prediction accuracy. A hybrid infilling strategy switches between the iterative quasi-optima and the maximum expected improvement from Kriging to prevent early convergence of local optimum. EORKS provides superior optima in several benchmark functions and an engineering design problem, using much smaller samples compared with the literature results, which demonstrates the sampling efficiency and searching robustness.
Functional analysis of cyclic diguanylate-modulating proteins in Vibrio fischeri

As bacterial symbionts transition from a motile free-living state to a sessile biofilm state, they must coordinate behavior changes suitable to each lifestyle. Cyclic diguanylate (c-di-GMP) is an intracellular signaling molecule that can regulate this transition, and it is synthesized by diguanylate cyclase (DGC) enzymes and degraded by phosphodiesterase (PDE) enzymes. Generally, c-di-GMP inhibits motility and promotes biofilm formation. While c-di-GMP and the enzymes that contribute to its metabolism have been well-studied in pathogens, considerably less focus has been placed on c-di-GMP regulation in beneficial symbionts. Vibrio fischeri is the sole beneficial symbiont of the Hawaiian bobtail squid (Euprymna scolopes) light organ, and the bacterium requires both motility and biofilm formation to efficiently colonize. C-di-GMP regulates swimming motility and cellulose exopolysaccharide production in V. fischeri. The genome encodes 50 DGCs and PDEs, and while a few of these proteins have been characterized, the majority have not undergone comprehensive characterization. In this study, we use protein overexpression to systematically characterize the functional potential of all 50 V. fischeri proteins. All 28 predicted DGCs and 14 predicted PDEs displayed at least one phenotype consistent with their predicted function, and a majority of each displayed multiple phenotypes. Finally, active site mutant analysis of proteins with the potential for both DGC and PDE activities revealed potential activities for these proteins. This work presents a systems-level functional analysis of a family of signaling proteins in a tractable animal symbiont and will inform future efforts to characterize the roles of individual proteins during lifestyle transitions. IMPORTANCE C-di-GMP is a critical second messenger that mediates bacterial behaviors, and V. fischeri colonization of its Hawaiian bobtail squid host presents a tractable model in which to interrogate the role of c-di-GMP during animal colonization. This work provides systems-level characterization of the 50 proteins predicted to modulate c-di-GMP levels. By combining multiple assays, we generated a rich understanding of which proteins have the capacity to influence c-di-GMP levels and behaviors. Our functional approach yielded insights into how proteins with domains to both synthesize and degrade c-di-GMP may impact bacterial behaviors. Finally, we integrated published data to provide a broader picture of each of the 50 proteins analyzed. This study will inform future work to define specific pathways by which c-di-GMP regulates symbiotic behaviors and transitions.
Functional Connectivity Development along the Sensorimotor-Association Axis Enhances the Cortical Hierarchy

Human cortical maturation has been posited to be organized along the sensorimotor-association (S-A) axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the S-A axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3,355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1,207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1,126). In each dataset, the development of functional connectivity systematically varied along the S-A axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These robust and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.
Neural filtering of physiological tremor oscillations to spinal motor neurons mediates short-term acquisition of a skill learning task

The acquisition of a motor skill involves adaptations of spinal and supraspinal pathways to alpha motoneurons. In this study, we estimated the shared synaptic contributions of these pathways to understand the neural mechanisms underlying the short-term acquisition of a new force-matching task. High-density surface electromyography (HDsEMG) was acquired from the first dorsal interosseous (FDI; 7 males and 6 females) and tibialis anterior (TA; 7 males and 4 females) during 15 trials of an isometric force-matching task. For two selected trials (pre- and post-skill acquisition), we decomposed the HDsEMG into motor unit spike trains, tracked motor units between trials, and calculated the mean discharge rate and the coefficient of variation of inter-spike interval (CoVISI). We also quantified the post/pre ratio of motor units’ coherence within delta, alpha, and beta bands. Improvements in force-matching were accompanied by a significant increase in the mean discharge rate and a decrease in CoVISI for both muscles. Moreover, the area under the curve within alpha band decreased by ∼22% and ∼13% for the TA and FDI muscles, respectively, with no changes in the delta or beta bands. These reductions correlated significantly with increased coupling between force/neural drive and target oscillations. These results suggest that the short-term acquisition of a new force-matching skill is mediated by the attenuation of tremor oscillations in the shared synaptic inputs. In other words, the central nervous system acts as a matched filter to modulate the synaptic weights of shared inputs and suppress neural components unrelated to the specific task. Supported by simulations, a plausible mechanism behind these alpha band reductions may involve spinal interneurons’ phase-cancelling descending oscillations.
Cortical and subcortical mapping of the allostatic-interoceptive system in the human brain: replication and extension with 7 Tesla fMRI

The brain continuously anticipates the energetic needs of the body and prepares to meet those needs before they arise, a process called allostasis. In support of allostasis, the brain continually models the internal state of the body, a process called interoception. Using published tract-tracing studies in non-human animals as a guide, we previously identified a large-scale system supporting allostasis and interoception in the human brain with functional magnetic resonance imaging (fMRI) at 3 Tesla. In the present study, we replicated and extended this system in humans using 7 Tesla fMRI (N = 91), improving the precision of subgenual and pregenual anterior cingulate topography as well as brainstem nuclei mapping. We verified over 90% of the anatomical connections in the hypothesized allostatic-interoceptive system observed in non-human animal research. We also identified functional connectivity hubs verified in tract-tracing studies but not previously detected using 3 Tesla fMRI. Finally, we demonstrated that individuals with stronger fMRI connectivity between system hubs self-reported greater interoceptive awareness, building on construct validity evidence from our earlier paper. Taken together, these results strengthen evidence for the existence of a whole-brain system supporting interoception in the service of allostasis and we consider the implications for mental and physical health. Significance Statement We used ultra-high field 7 Tesla fMRI to replicate and extend a large-scale brain system supporting interoception and allostasis, entwined processes crucial to the core brain function of coordinating and regulating the internal systems of the body. In particular, we mapped the subcortical extents of this system, several of which are small brainstem nuclei only recently delineated at 7 Tesla. Our findings suggest that investigations of distributed brain networks should not be restricted to the cerebral cortex. We emphasize bodily regulation as a whole-brain phenomenon and highlight its implications for mental and physical health.
Plasma potential in the sheaths of electron‐emitting surfaces in space

Sheaths are formed around the surfaces of airless bodies, which both collect and emit electrons. Depending on the ratio of the emitted to collected electron fluxes Γ, the sheath potential structure can be quite different. We present the first experimental measurements of all the three types of the sheath potentials: classical, space‐charge‐limited (SCL), and inverse. A solid surface immersed in plasmas emits secondary electrons. The potential structure changes from a monotonic classical to a nonmonotonic SCL sheath as Γ increases. At the critical electron emission with zero electric field at the surface, the sheath potential is determined by the plasma electron temperature, and Γ approaches but remains smaller than 1, in agreement with the theoretical expectation. The nonmonotonic SCL sheath persists steadily for Γ > 1. When the emitted electron density becomes larger than the plasma electron density, a monotonic inverse sheath forms with a positive surface potential relative to the ambient.
Causes and Effects of Coastal Dunes Erosion During Storm Surge Axel in January 2017 on the Southern Baltic Polish Coast

Abstract The study is dedicated to researching the storm surge Axel, the largest on the South Baltic coast in the 20th and 21st centuries. This unique event resulted in a very large erosion along the whole Polish Baltic Sea coast in January 2017 (max. HSL = 1.65 m, the average for the coast 1.36 m). Storm surge effects on the coast were followed based on field observations of dune retreat and analysis of hydrodynamic and meteorological parameters of the surge and its passage through the Baltic Sea. The material of dune erosion was collected based on cross-shore profiling of almost every 1 km, along the whole Polish sand barrier coast, before and after this storm. The work also studies the parameters of smaller storm surges from the end of 2016, which caused the lowering of beaches and dune erosion. A relationship was observed between erosion, and beach height and sea level (SL). The higher the beach, the lower the erosion that occurred. The average dune toe retreat was 5.1 m, and the largest exceeded 9–19 m (max. 42 m). The most important for dune erosion was the height of run-up, beach height and shore exposition for a surge. The largest dune erosion was observed during the heaviest SL with wave run-up higher than 3.8 m above mean sea level (AMSL). Each coast section was eroded, which also caused losses in infrastructure.
Thermocapillary modulation of self-rewetting films

Whereas surface tension decreases linearly with temperature for most fluids, here we consider those that exhibit a well-defined minimum. Specifically, our study is motivated by dilute aqueous mixtures of long-chain alcohols, for which surface tension is typically assumed to be a quadratic function of temperature. Utilization of these so-called ‘self-rewetting fluids’ has grown significantly in the past decade, due to observations that heat transfer is enhanced in applications such as film boiling and pulsating heat pipes. With similar applications in mind, we investigate the dynamics of a thin film with quadratic surface tension which is subjected to a temperature modulation in the bounding gas. A model is developed within the framework of the long-wave approximation, and a time-averaged thermocapillary driving force for destabilization is uncovered that results from the nonlinear surface tension. Linear stability analysis of the nonlinear partial differential equation for the film thickness is used to determine the critical conditions at which this driving force destabilizes the film and numerical investigation of this evolution equation reveals that linearly unstable perturbations saturate to regular periodic solutions (when the modulational frequency is set properly). Properties of these flows such as bifurcation at critical points and long-domain flows, where multiple unstable linear modes interact, are also discussed.
Optimal-order uniform and nonuniform bounds on the rate of convergence to normality for maximum likelihood estimators

: It is well known that, under general regularity conditions, the distribution of the maximum likelihood estimator (MLE) is asymptotically normal. Very recently, bounds of the optimal order O (1 / √ n ) on the close- ness of the distribution of the MLE to normality in the so-called bounded Wasserstein distance were obtained [2, 1], where n is the sample size. How- ever, the corresponding bounds on the Kolmogorov distance were only of the order O (1 /n 1 / 4 ). In this paper, bounds of the optimal order O (1 / √ n ) on the closeness of the distribution of the MLE to normality in the Kol- mogorov distance are given, as well as their nonuniform counterparts, which work better in tail zones of the distribution of the MLE. These results are based in part on previously obtained general optimal-order bounds on the rate of convergence to normality in the multivariate delta method. The cru- cial observation is that, under natural conditions, the MLE can be tightly enough bracketed between two smooth enough functions of the sum of in- dependent random vectors, which makes the delta method applicable. It appears that the nonuniform bounds for MLEs in general have no prece- dents in the existing literature; a special case was recently treated by Pinelis and Molzon [20]. The results can be extended to M -estimators.
A Structural Theory of Pitch,,

Melodies are composed of sounds that can be ordered on a musical scale. “Pitch” is the perceptual dimension on that scale. Abstract Musical notes can be ordered from low to high along a perceptual dimension called “pitch”. A characteristic property of these sounds is their periodic waveform, and periodicity generally correlates with pitch. Thus, pitch is often described as the perceptual correlate of the periodicity of the sound’s waveform. However, the existence and salience of pitch also depends in a complex way on other factors, in particular harmonic content. For example, periodic sounds made of high-order harmonics tend to have a weaker pitch than those made of low-order harmonics. Here we examine the theoretical proposition that pitch is the perceptual correlate of the regularity structure of the vibration pattern of the basilar membrane, across place and time—a generalization of the traditional view on pitch. While this proposition also attributes pitch to periodic sounds, we show that it predicts differences between resolved and unresolved harmonic complexes and a complex domain of existence of pitch, in agreement with psychophysical experiments. We also present a possible neural mechanism for pitch estimation based on coincidence detection, which does not require long delays, in contrast with standard temporal models of pitch.
Heating of the Lower Troposphere due to Absorption of the Visible Solar Radiation by Aerosols

The vertical distribution of the heating rate due to absorption of the visible solar radiation by aerosols is studied by solving the equation of radiative transfer in turbid atmospheres.Realistic model atmospheres with respect to size and vertical distributions of aerosols as well as surface reflectivity are assumed, and latitude-altitude variation of the heating rate is calculated as a function of atmospheric turbidity and complex refractive index of aerosols. It is shown that the heating rate increases in the lower atmosphere of the low latitude region, and becomes comparable to the heating rate due to absorption of near infrared solar radiation by gaseousconstituents there. It is shown, however, that the heating rate is greatly affected by the imaginary part of the refractive index of aerosols. A possivility of determing the imaginary part from field observations is suggested.
The vast potential of exoplanet satellites for high‐energy astrophysics

The photometric precision, monitoring baselines, and rapid, even sampling rates required by modern satellites designed for detecting the signal of transiting exoplanets are ideally suited to a large number of applications in high-energy astrophysics. I will exemplify this by discussing the results for active galactic nuclei from Kepler and summarizing other high-energy results from Kepler/K2. These precision instruments are currently underutilized for high-energy applications despite their great potential, due in part to complex systematics affecting the data. I will summarize these effects and mitigation approaches, and conclude by discussing how the recently launched Transiting Exoplanet Survey Satellite mission will differ from Kepler/K2 in ways significant to the high-energy community.
Language-based Photo Color Adjustment for Graphic Designs

Adjusting the photo color to associate with some design elements is an essential way for a graphic design to effectively deliver its message and make it aesthetically pleasing. However, existing tools and previous works face a dilemma between the ease of use and level of expressiveness. To this end, we introduce an interactive language-based approach for photo recoloring, which provides an intuitive system that can assist both experts and novices on graphic design. Given a graphic design containing a photo that needs to be recolored, our model can predict the source colors and the target regions, and then recolor the target regions with the source colors based on the given language-based instruction. The multi-granularity of the instruction allows diverse user intentions. The proposed novel task faces several unique challenges, including: 1) color accuracy for recoloring with exactly the same color from the target design element as specified by the user; 2) multi-granularity instructions for parsing instructions correctly to generate a specific result or multiple plausible ones; and 3) locality for recoloring in semantically meaningful local regions to preserve original image semantics. To address these challenges, we propose a model called LangRecol with two main components: the language-based source color prediction module and the semantic-palette-based photo recoloring module. We also introduce an approach for generating a synthetic graphic design dataset with instructions to enable model training. We evaluate our model via extensive experiments and user studies. We also discuss several practical applications, showing the effectiveness and practicality of our approach. Please find the code and data at https://zhenwwang.github. io/langrecol.
A Lightweight Model for Object Detection in Computer Motherboard Images

Directly benefiting from the deep neural network, object detection algorithm has played a key role in industrial automation in recent years. Computer motherboard component detection is essential in the industrial automation of computer assembly or disassembly and custom computer maintenance scenarios. Existing public datasets like MS COCO do not focus on particular detection scenarios. In addition, the popular datasets (e.g., PCB-Aol, micro-PCB, etc.) committed to electronic component detection are limited in the number of categories, and diversity. So, we construct a multi-category and diversity dataset called CMC-14. The dataset contains 14 categories, 2,145 images, and 25,718 labeled instances with a large variance in appearance. Moreover, there is no deep neural network model focused on computer motherboard detection before our work. Computer motherboard component detection exists two great challenges: (1) Some objects are small in scale and lack sufficient feature information; (2) Some objects are similar in appearance, but they do not belong to the same category. To cope with such challenges, we improved the YOLOv5n and designed a dense small object data augmentation algorithm. Extensive experiments and comprehensive evaluations on the CMC-14 benchmark dataset show the effectiveness of our work and methods. The dataset is available at https://github.com/CMC-14/CMC-14/releases/tag/CMC-14.