Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Two-Dimensional Dirac Semimetal Based on the Alkaline Earth Metal, CaP$_3$

Using an evolutionary algorithm in combination with first-principles density functional theory calculations, we identify two-dimensional (2D) CaP$_3$ monolayer as a new Dirac semimetal due to inversion and nonsymmorphic spatial symmetries of the structure. This new topological material, composed of light elements, exhibits high structural stability (higher than the phase known in the literature), which is confirmed by thermodynamic and kinetic stability analysis. Moreover, it satisfies the electron filling criteria, so that its Dirac state is located near the Fermi level. The existence of the Dirac state predicted by the theoretical symmetry analysis is also confirmed by first-principles electronic band structure calculations. We find that the energy position of the Dirac state can be tuned by strain, while the Dirac state is unstable against an external electric field since it breaks the spatial inversion symmetry. Our findings should be instrumental in the development of 2D Dirac fermions based on light elements for their application in nanoelectronic devices and topological electronics.
BogieCopter: A Multi-Modal Aerial-Ground Vehicle for Long-Endurance Inspection Applications

The use of Micro Aerial Vehicles (MAVs) for inspection and surveillance missions has proved to be extremely useful, however, their usability is negatively impacted by the large power requirements and the limited operating time. This work describes the design and development of a novel hybrid aerial-ground vehicle, enabling multi-modal mobility and long operating time, suitable for long-endurance inspection and monitoring applications. The design consists of a MAV with two tiltable axles and four independent passive wheels, allowing it to fly, approach, land and move on flat and inclined surfaces, while using the same set of actuators for all modes of locomotion. In comparison to existing multi-modal designs with passive wheels, the proposed design enables a higher ground locomotion efficiency, provides a higher payload capacity, and presents one of the lowest mass increases due to the ground actuation mechanism. The vehicle's performance is evaluated through a series of real experiments, demonstrating its flying, ground locomotion and wall-climbing capabilities, and the energy consumption for all modes of locomotion is evaluated.
Kittel law and domain formation mechanism in PbTiO$_3$/SrTiO$_3$ superlattices

We report second-principles simulations on the structural and energetic properties of domains in (PbTiO$_{3}$)$_{n}$/(SrTiO$_{3}$)$_{n}$ superlattices. For the explored layer thickness ($n$ ranging between 8 and 16 unit cells) and lateral sizes of the domains, the most stable configuration corresponds to polar domains separated by a sequence of counter-rotating vortices (clockwise/counterclockwise) perpendicular to the stacking direction and acting as domain walls. The balance between the domain wall energy and the electrostatic energy yields to an optimal domain period $\omega$ that is proportional to the square-root of the thickness of the PbTiO$_{3}$ layer, following the Kittel law. For a given lateral size of the simulation box, suboptimal domain structures (with a width larger than the one predicted by the Kittel law) can be obtained in a metastable form. However, at finite temperature, molecular dynamics simulations show the spontaneous change of periodicity, which implies the formation of new domains whose generation is initiated by the nucleation of vortices and antivortices at the interface between the SrTiO$_{3}$ and the PbTiO$_{3}$ layers. The vortices progressively elongate and eventually annihilate with the antivortices yielding the formation of new domains to comply the Kittel law via a topological phase transition.
Relaxation approach for learning neural network regularizers for a class of identification problems

The present paper deals with the data-driven design of regularizers in the form of artificial neural networks, for solving certain inverse problems formulated as optimal control problems. These regularizers aim at improving accuracy, wellposedness or compensating uncertainties for a given class of optimal control problems (inner-problems). Parameterized as neural networks, their weights are chosen in order to reduce a misfit between data and observations of the state solution of the inner- optimal control problems. Learning these weights constitutes the outer-problem. Based on necessary first-order optimality conditions for the inner-problems, a relaxation approach is proposed in order to implement efficient solving of these inner-problems, namely the forward operator of the outer-problem. Optimality conditions are derived for the latter, and are implemented in numerical illustrations dealing with the inverse conductivity problem. The numerical tests show the feasibility of the relaxation approach, first for rediscovering standard L 2-regularizers, and next for designing regularizers that compensate unknown noise on the observed state of the inner-problem.
Long gaps between turns are awkward for strangers but not for friends

When people feel connected they tend to respond quickly in conversation, creating short gaps between turns. But are long gaps always a sign that things have gone awry? We analysed the frequency and impact of long gaps (greater than 2 s) in conversations between strangers and between friends. As predicted, long gaps signalled disconnection between strangers. However, long gaps between friends marked moments of increased connection and friends tended to have more of them. These differences in connection were also perceived by independent raters: only the long gaps between strangers were rated as awkward, and increasingly so the longer they lasted. Finally, we show that, compared to strangers, long gaps between friends include more genuine laughter and are less likely to precede a topic change. This suggests that the gaps of friends may not function as ‘gaps’ at all, but instead allow space for enjoyment and mutual reflection. Together, these findings suggest that the turn-taking dynamics of friends are meaningfully different from those of strangers and may be less bound by social conventions. More broadly, this work illustrates that samples of convenience—pairs of strangers being the modal paradigm for interaction research—may not capture the social dynamics of more familiar relationships. This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.
Nonlinear Self-Modulation of Capillary-Gravity Waves on Liquid Layer

The derivative expansion method is applied to an investigation of the weakly nonlinear self-interactions of capillary-gravity waves on a liquid layer of uniform depth. The stability characteristics of a wave train are examined on the basis of the nonlinear Schrodinger equation. It is shown that the effect of capillarity is of critical importance to the modulational instability.
A Malicious Program Attack Identification Model Based on Risk Dependency Analysis

With the development of Internet technology, open networks and standardized protocols bring more potential security threats. How to propose effective identification and prevention methods for some typical malicious program attacks is an important issue to be considered for active defense. However, most methods do not analyze the risk in the attack behavior, so this paper proposes a malicious program attack identification model based on risk dependency analysis. The model takes the character text of the malicious program attack as input, and the attack behavior string is subjected to word splitting and embedding operations. Subsequently, the context vector obtained by concatenating all word vectors is used as the query vector of the attention mechanism, and the set of feature vectors obtained by concatenating the attribute feature word vectors in the attack behavior string is used as the key vector and value vector of the attention mechanism. Then, based on the context vector and feature vectors, the attention mechanism is used to mine the global and local dependency information in the character sequences, respectively. The attention weights corresponding to the local dependency information are regarded as the risk of malicious program attacks. The experimental results with real datasets show that the malicious program attack identification model based on risk dependency analysis can effectively identify different malicious attack.
Separating the art from the artist: The role of narratives on music enjoyment and appreciation

This study tests the effect to which narratives influence the enjoyment and appreciation of popular music. Two experiments tested how narratives about artists influenced listener perception of their songs. Experiment 1 found that narratives about artists influence how their music is appreciated and enjoyed. Experiment 2 found that this influence was due to narratives increasing how much listeners like the artist and desire to see them succeed. The increase in enjoyment and appreciation was stronger for songs that were not as well-liked to begin with. The results suggest that narratives about creators may influence the perception of their work.
Study of Thermal Fatigue of H13 Die Steel with Various Surface Treatments

Surfaces of die-casting dies are subjected to very severe conditions of cyclical thermal and mechanical load, and chemical and mechanical wear. Dies mostly fail due to a combination of heat checking, erosion, corrosion and soldering. It is conceivable that appropriate surface treatments and coatings have a favourable influence on the temperature dependant performance of the surface of the die. The objective of this study was to examine various surface treatments and coatings. including shot peening, nitriding, nitro-carburizing, laser hardening and remelting, electro-spark alloying (deposition) and plasma spraying, under thermal fatigue conditions. Thermal cycling tests were conducted by alternate dipping of treated samples in an LM24 melt and in water. Results and interpretation are presented in this paper. The best thermal fatigue resistance was shown for a double surface treatment of laser hardening plus electro-spark deposition.
Constrained smoothing of histograms and scatterplots with simulated annealing

Histogram and scatterplot models are often required for statistical inference. In the field of petroleum engineering, stochastic simulation algorithms require, among other statistics, a model for the histogram of the petrophysical attribute (porosity/permeability) being simulated. Often this model is taken to be the deciustered distribution of the sample data. When there are many data (say, greater than l,000), this histogram may be reasonably informed. Most often, however, the sample histogram shows multiple sawtoothlike spikes that are not representative of the entire population; the sample histogram must be smoothed. A simulated annealing-based procedure is proposed for smoothing one-variable (univariate) histograms and two-variable scatterplots (bivariate histograms). The smoothed histograms are constrained to the sample mean, variance, specified quantiles, and a measure of smoothness. In the bivariate case, the distribution must be consistent with both marginal histograms and can be additionally cons.
Fast Numerical Solution of Time-Periodic Parabolic Problems by a Multigrid Method

The discrete solutions of parabolic problems subject to the condition $y( \cdot ,T) = y( \cdot ,0)$ of time periodicity are solutions of large sparse systems. In this paper we propose a multigrid algorithm. It is a very fast iterative method. The algorithm can easily be generalized to nonlinear problems and to conditions of the type $y( \cdot ,0) = A(y( \cdot ,T))$ (A is a nonlinear mapping). The computational work for solving the periodic problem is of the same order as the work for solving an initial value problem ($y( \cdot ,0)$ given). Numerical results are reported for a linear and a nonlinear example.
VLA neutral hydrogen imaging of compact groups of galaxies. II - HCG 31, 44, and 79

Neutral hydrogen images of three compact groups of galaxies are presented: HCG 31, 44, and 79. The images were obtained with the very large array (VLA), an on-line Hanning smoothing was applied to the data, and the H I spectral channel was isolated. The images were made on the Pipeline, and were produced by means of a method described by Gorkon and Ekers (1988). The images of HCG 44 are compared with earlier Arecibo observations. The H I emission in HCG 44 is discovered within the galaxies, whereas the emission in 31 and 79 can be found throughout the group in clouds that are larger than the galaxies. Evidence of a relationship between the compact groups is found in the H I data, and the groups are considered to be merging into a single object. Some of the groups are theorized to be young amorphous galaxies where the H I is still bound to individual galaxies, and which have just begun to condense from the intergalactic medium. The kinematics of the gas are shown to vary, and a common gaseous envelope contains the dwarf galaxies. 42 refs.
Plasticity in parental behavior and vasopressin: responses to co-parenting, pup age, and an acute stressor are experience-dependent

The impact of variation in parental caregiving has lasting implications for the development of offspring. However, the ways in which parents impact each other in the context of caregiving is comparatively less understood, but can account for much of the variation observed in the postnatal environment. Prairie voles (Microtus ochrogaster) demonstrate a range of postnatal social groups, including biparental pairs and pups raised by their mothers alone. In addition to the challenges of providing parental care, prairie vole parents often experience acute natural stressors (e.g., predation, foraging demands, thermoregulation) that could alter the way co-parents interact. We investigated how variation in the experience of raising offspring impacts parental behavior and neurobiology by administering an acute handling stressor on prairie vole families of single mothers and biparental parents over the course of offspring postnatal development. Mothers and fathers exhibited robust behavioral plasticity in response to the age of their pups, but in sex-dependent ways. Pup-directed care from mothers did not vary as a function of their partner’s presence, but did covary with the number of hypothalamic vasopressin neurons in experience-dependent ways. The relationship between vasopressin neuron numbers and fathers’ behaviors was also contingent upon the stress handling manipulation, suggesting that brain-behavior associations exhibit stress-induced plasticity. These results demonstrate that the behavioral and neuroendocrine profiles of adults are sensitive to distinct and interacting experiences as a parent, and extend our knowledge of the neural mechanisms that may facilitate parental behavioral plasticity.
Functional host-specific adaptation of the intestinal microbiome in hominids

Fine-scale knowledge of the changes in composition and function of the human gut microbiome compared that of our closest relatives is critical for understanding the evolutionary processes underlying its developmental trajectory. To infer taxonomic and functional changes in the gut microbiome across hominids at different timescales, we performed high-resolution metagenomic-based analyses of the fecal microbiome from over two hundred samples including diverse human populations, as well as wild-living chimpanzees, bonobos, and gorillas. We find human-associated taxa depleted within non-human apes and patterns of host-specific gut microbiota, suggesting the widespread acquisition of novel microbial clades along the evolutionary divergence of hosts. In contrast, we reveal multiple lines of evidence for a pervasive loss of diversity in humans populations in correlation with a high Human Development Index, including evolutionarily conserved clades. Similarly, patterns of co-phylogeny are found to be disrupted in humans. Together with identifying individual microbial taxa and functional adaptations that correlate to host phylogeny, these findings offer new and exciting insights into specific candidates playing a role in the diverging trajectories of the gut microbiome of hominids, demonstrating that repeated horizontal gene transfer and gene loss, as well as the adaptation to transient microaerobic conditions appear to have played a role in the evolution of the human gut microbiome.
Inhibitory control of speech production in the human premotor frontal cortex

Natural speech is full of starts and stops. Here, we studied the neural mechanisms that underlie the inhibitory control of speech, specifically the ability to stop speaking on demand. We recorded direct cortical activity while participants made continuous speech production and were given a visual cue to stop speaking. Neural recordings revealed activity in the premotor frontal cortex associated with speech stopping. Cortical sites showing stop activity were largely distinct from sites involved in active speech production or, more specifically, encoding articulatory movements. Electrocortical stimulation mapping at many premotor sites with stop activity caused involuntary speech arrest, an immediate inability to speak or vocalize. Furthermore, many speech arrest sites did not co-localize with neural activity correlating with speech motor planning or execution, contrary to this long-assumed function in clinical brain mapping. Together, these results suggest a previously unknown premotor cortical network that underlies the inhibitory control of speech, which has significant implications for understanding the dynamics of normal and altered speech production, as well as clinical brain mapping.
Exploring the Interplay between BOLD Signal Variability, Complexity, and Static and Dynamic Functional Brain Network Features during Movie Viewing

As the brain is dynamic and complex, knowledge of brain signal variability and complexity is crucial in our understanding of brain function. Recent resting-fMRI studies revealed links between BOLD signal variability or complexity with static/dynamics features of functional brain networks (FBN). However, no study has examined the relationships between these brain metrics. The association between brain signal variability and complexity is still understudied. Here we investigated the association between movie naturalistic-fMRI BOLD signal variability/complexity and static/dynamic FBN features using graph theory analysis. We found that variability positively correlated with fine-scale complexity but negatively correlated with coarse-scale complexity. Hence, variability and coarse-scale complexity correlated with static FC oppositely. Specifically, regions with high centrality and clustering coefficient were related to less variable but more complex signal. Similar relationship persisted for dynamic FBN, but the associations with certain aspects of regional centrality dynamics became insignificant. Our findings demonstrate that the relationship between BOLD signal variability, static/dynamic FBN with BOLD signal complexity depends on the temporal scale of signal complexity. Additionally, altered correlation between variability and complexity with dynamic FBN features may indicate the complex, time-varying feature of FBN and reflect how BOLD signal variability and complexity co-evolve with dynamic FBN over time.
Computing of B-series by automatic differentiation

We present an algorithm based on Automatic Differentiation for computing general B-series of vector fields $f\colon \mathbb{R}^n\rightarrow \mathbb{R}^n$. The algorithm has a computational complexity depending linearly on $n$, and provides a practical way of computing B-series up to a moderately high order $d$. Compared to Automatic Differentiation for computing Taylor series solutions of differential equations, the proposed algorithm is more general, since it can compute any B-series. However the computational cost of the proposed algorithm grows much faster in $d$ than a Taylor series method, thus very high order B-series are not tractable by this approach.
Physics-Based Determination of Carrier Effective Mass Assumed in Density Gradient Model

In this paper, we describe the physics-based determination of the carrier effective mass assumed in a density gradient model (DGM). We show that the effective mass assumed in a conventional DGM corresponds to that along the quantization direction assumed in the Schrödinger–Poisson (SP) solver, and that the DGM produces a good quantum correction for a system with a smooth conduction band bottom profile. Assuming an abrupt jump in the conduction band bottom profile of a heterostructure system, the physics-based determination of parameters is required to obtain agreement with the SP solver result. Whether the physical meaning of the effective mass holds is examined for the two-fluid description of the DGM.
Multiple zeta functions: the double sine function and the signed double Poisson summation formula

We construct multiple zeta functions as absolute tensor products of usual zeta functions. The Euler product expression is established for the most basic case $\zeta(s,\mathbf{F}_p)\otimes\zeta(s,\mathbf{F}_q)$ by using the signed double Poisson summation formula and the theory of the double sine function.
Antenna excitation of drift wave in a toroidal plasma

In a magnetized toroidal plasma, an antenna tunable in vertical wave number is used to excite density perturbations. Coherent detection is performed by means of Langmuir probes to directly determine both the wave vector and the plasma response induced by the antenna. Comparison between the theoretical density response predicted by the generalized Hasegawa-Wakatani model, and the experimentally determined density response enables us the identification of one peak of the plasma response as a drift wave.
On the convegence of a sequential penalty function method for constrained minimization

The convergence behaviour of a class of iterative methods for solving the constrained minimization problem is analysed. The methods are based on the sequential minimization of a simple differentiable penalty function. They are sufficiently general to ensure global convergence of the iterates to the solution of the problem at an asymptotic (two-step Q-) superlinear rate.
Assessing population differentiation and isolation from single‐nucleotide polymorphism data

Summary. We introduce a new, hierarchical, model for single‐nucleotide polymorphism allele frequencies in a structured population, which is naturally fitted via Markov chain Monte Carlo methods. There is one parameter for each population, closely analogous to a population‐specific version of Wright's FST, which can be interpreted as measuring how isolated the relevant population has been. Our model includes the effects of single‐nucleotide polymorphism ascertainment and is motivated by population genetics considerations, explicitly in the transient setting after divergence of populations, rather than as the equilibrium of a stochastic model, as is traditionally the case. For the sizes of data set that we consider the method provides good parameter estimates and considerably outperforms estimation methods analogous to those currently used in practice. We apply the method to one new and one existing human data set, each with rather different characteristics—the first consisting of three rather close European populations; the second of four populations taken from across the globe. A novelty of our framework is that the fit of the underlying model can be assessed easily, and these results are encouraging for both data sets analysed. Our analysis suggests that Iceland is more differentiated than the other two European populations (France and Utah), a finding which is consistent with the historical record, but not obvious from comparisons of simple summary statistics.