Miracle-dz/UniFAR-FTUs huggingface dataset:
Tunable pseudocapacitance storage of MXene by cation pillaring for high performance sodium-ion capacitors

2D transition metal carbide materials called MXene have attracted significant interest in the field of electrochemical energy storage due to their high electrical conductivity and high volumetric capacity. However, the low capacity accompanied by sluggish sodiation kinetics of electrodes made from multi-layer MXene has limited their further application for sodium ion storage. The key challenge to overcome the abovementioned issue is to decrease the Na+ diffusion barrier and increase the active site concentration in MXene electrodes used for Na+ storage. In this study, a method to significantly improve the capacity and kinetics of Ti3C2 MXene for Na+ storage using facile alkali metal ion pillaring is reported. After Na+ pillaring, the MXene sheets (Na–Ti3C2) with incremental interlayer spacing exhibit a high reversible capacity of 175 mA h g−1 (∼170% of the original value) at 0.1 A g−1 and an excellent outstanding cycling stability for 2000 cycles at 2.0 A g−1 for sodium ion storage. By combining ex situ XPS with kinetics analysis, the increased number of active sites and lower Na+ diffusion barrier were confirmed after Na+ pillaring when compared with the cases of Ti3C2, Li–Ti3C2, and K–Ti3C2. The role of the terminal groups (–OH) in Na–Ti3C2 has also been confirmed by analysis of the electrochemical performance of the annealed Na–Ti3C2 samples (450 °C and 700 °C). The results show that the existence of –OH groups in Na–Ti3C2 can increase the number of Na+ storage active sites, but decrease the kinetics. By coupling the Na–Ti3C2 anode with an AC cathode, the assembled SIC device delivers a high energy density of 80.2 W h kg−1 and high power density (6172 W kg−1) with an ultra-long and stable cycling performance (capacity retention: ∼78.4 at 2 A g−1 after 15 000 cycles).
Matrices of seesaw type and the Courant-Fischer-Weyl theorem

To demonstrate that matrices of seesaw type lead to a hieararchy in the neutrino masses, i.e. that there is a large gap in the singular spectrum of these matrices, one generally uses an approximate block-diagonalization procedure. In this note we show that no approximation is required to prove this gap property if the Courant-Fisher-Weyl theorem is used instead. We do not claim originality for this observation which however does not seem to show up in the literature. We also sketch the proof of additional inequalities for the singular values of matrices of seesaw type.
Sync Toolbox: A Python Package for Efficient, Robust, and Accurate Music Synchronization

Music can be described and represented in many different ways, including as sheet music, symbolic representations, and audio recordings (Müller, 2015). For each of these representations, different versions (e.g., recordings performed by different orchestras and conductors) that correspond to the same musical work may exist. Music information retrieval (MIR) aims at developing techniques and tools for organizing, understanding, and searching this information in a robust, efficient, and intelligent manner. In this context, various alignment and synchronization procedures have been developed with the common goal of automatically linking several types of music representations, thus coordinating the multiple information sources related to a given musical work. In the design and implementation of synchronization algorithms, one has to deal with a delicate tradeoff between efficiency, robustness, and accuracy—requirements leading to various approaches with many design choices. In this contribution, we introduce a Python package called Sync Toolbox that provides open-source reference implementations for full-fledged music synchronization pipelines and yields state-of-the-art alignment results for a wide range of Western music. Using suitable feature representations and cost measures, the toolbox’s core technology is based on dynamic time warping (DTW), which brings the feature sequences into temporal correspondence. To account for efficiency, robustness and, accuracy, our toolbox integrates and combines techniques such as multiscale DTW (MsDTW) (Müller et al., 2006; Salvador & Chan, 2004), memory-restricted MsDTW (MrMsDTW) (Prätzlich et al., 2016), and high-resolution music synchronization (Ewert et al., 2009). While realizing a complete system with presets that allow users to reproduce research results from the literature, our toolbox also provides well-documented functions for all basic building blocks required for feature extraction and alignment. Furthermore, the toolbox contains example code for visualizing, sonifying, and evaluating synchronization results, thus deepening the understanding of the techniques and data.
Non-reproductive males as an evolutionary stable strategy: Insights for social species

We present an evolutionary account for the emergence of non reproducing males (NRM) in biological populations, potentially extensible to any range of non-monogamous social species. Accordingly, NRM is to be viewed as an evolutionary stable strategy (ESS) which works by improving inclusive fitness through the luring of more and better quality mates, directed by the NRM individual. Female preferences have been shown to be amenable to NRM associated behavior, which is also correlated to lower perceived threat by other males, resulting in decreased aggression and allowing for a longer actuation range. Using a mathematical model, we demonstrate that much like bonobos are matriarchal societies and chimpanzees are male centered, humans are a NRM-centered species, with NRM clustering working as female/male clustering in these species. Finally, we review how humankind history has been firmly steered by NRM, from the Greeks to the Church to Newton, and show how the modern occurrence of NRM can be rationalized if being triggered by diverse demographic access from early age, consistent with evolutionary scenarios.
Determinants of Demand for Microinsurance in Cameroon

This study aims at identifying the determinants of demand for microinsurance in Cameroon using a methodology based on the analytical framework proposed by Heckman (1979). These are counting models with double selection. First, a biprobit selection model is estimated to determine membership of an association on the one hand, and subscription to a microinsurance on the other. Second, interest models called counting models are estimated to identify and analyse the factors that affect the number of microinsurance policies. Data for the study are from the Fourth Cameroon Household Survey (ECAM4), a survey with national coverage conducted by the National Institute of Statistics (NIS) in 2014. The results make it possible to identify significant factors that are positively correlated with membership of an association and subscription to a microinsurance. These are mainly factors such as level of education, age squared and household size. Conversely, the results show that male gender and the age of the household head significantly and negatively influence membership and subscription. Furthermore, male gender, age squared, household size and insurance premium are positively related to the number of microinsurance policies purchased by household heads. Finally, age and level of education are negatively correlated with the number of microinsurance policies purchased. Furthermore, the inverse of the Mills ratio indicates that the number of microinsurance policies is negatively correlated with unobserved characteristics. In a context of poverty, these results call for a number of actions by public authorities to promote microinsurance as a way of achieving universal social security protection.
Experimental study of shear deformation of the medium formed by the massif of ribbed grains

The approach to the study of the seismically active zone as an open system that is in a state of self-organized criticality is becoming increasingly used in modern research. The models used in this approach should reflect the most characteristic features of such media: discreteness, nonequilibrium, nonlinear and nonlocal nature of interactions. In this paper, the medium is modeled by a granular massif with ribbed grains. Three different massifs are used in the research. Within one massif, all grains are identical, while grains from different massifs differ in shape (cubic or irregular ribbed) and characteristic size. A number of experiments of shear deformation of such granular massifs were carried out in order to study the influence of the shape and size of grains on the statistical characteristics of the process. The influence of the stress state on the deformation properties of these media was also studied. The experiments proved the qualitative similarity of the behavior of different granular media. Obtained experimentally force jumps, representing the reaction of the granular medium to shear deformation, obey the distributions in the form of power dependencies. However, the magnitudes of the forces arising in the massifs depend on the grain shape and size as well as the stress state in the massifs. The experiments with shear deformation of the granular media under the external action of small impulse stresses have shown that such action causes a smoother, devoid of sharp jumps, deformation. External perturbations shift the distribution of the force jumps towards smaller values, leaving their exponents unchanged. The analysis of experimental results using nonadditive statistical mechanics confirmed the presence of long-range correlations in the massif of ribbed granules during its shear deformation.
Design and application of facial expression analysis system in empathy ability of children with autism spectrum disorder

Empathy is an important social ability in the early childhood development. One of the significant characteristics of children with autism spectrum disorder (ASD) is their lack of empathy, which makes it difficult for them to feel and understand other people’s emotions and to judge other people’s behavioral intentions, leading to social disorders. This research designs and implements a facial expression analysis system that could obtain and analyze the real-time facial expressions of children when viewing stimulus materials, and then evaluate the differences of empathy ability between ASD children and typical development (TD) children. The results of this research provide new ideas for the evaluation of ASD children, and also help to develop empathy intervention plans for ASD children.
R ADA G RAD : Random Projections for Adaptive Stochastic Optimization

We present R ADA G RAD a simple and computationally efﬁcient approximation to full-matrix A DA G RAD based on dimensionality reduction using the subsampled randomized Hadamard transform. R ADA G RAD is able to capture correlations in the gradients and achieves a similar regret – in theory and empirically – to full-matrix A DA G RAD but at a computational cost comparable to the diagonal variant
Observation of the Absence of Ulnar Artery in a Patient Planned to Free Dorsoulnar Artery Perforator Flap for Finger Defect: Is Preoperative Doppler evaluation of Perforator Alone Sufficient?

Abstract Background Volar finger defects where critical structures exposed are always challenging for plastic surgeons. In these types of defects, local flaps, cross finger flaps, abdominal flaps, and free flaps are used. Free dorsoulnar artery perforator (DUAP) flaps and superficial palmar branch of radial artery (SPBRA) flaps are also used. In this case, we present a patient who was scheduled to receive a DUAP flap to address defect on the second finger of right hand; however, we repaired the defect with a SPBRA flap because intraoperative absence of the ulnar artery was observed. Materials and Methods A 34-year-old male patient was admitted with a wound that exposed the tendon and neurovascular bundle on the volar side of the second finger of the right hand. A free DUAP flap was planned for the patient. A perforator was detected during the preoperative Doppler ultrasound examination. While dissecting the perforator, we noted the absence of an ulnar artery proximal to the perforator vessel. The elevated SPBRA flap from same extremity and the defect were closed. Results Postoperative computer tomography showed an absence of the ulnar artery distal to the right antecubital region. No complications were seen in the donor and recipient areas. Long-term motor movements were natural, and the patient's quality of life was good. Conclusion Determining the perforator site using Doppler alone may not be sufficient in preoperative evaluation of patients scheduled to receive DUAP flaps. Performing an Allen test and using advanced imaging methods can prevent surgeons from encountering a bad surprise.
An Efficient Connected Swarm Deployment via Deep Learning

In this paper, an unmanned aerial vehicles (UAVs) deployment framework based on machine learning is studied. It aims to maximize the sum of the weights of the ground users covered by UAVs while UAVs forming a connected communication graph. We focus on the case where the number of UAVs is not necessarily enough to cover all ground users.We develop an UAV Deployment Deep Neural network (UD-DNNet) as a UAV’s deployment deep network method. Simulation results demonstrate that UDDNNet can serve as a computationally inexpensive replacement for traditionally expensive optimization algorithms in real-time tasks and outperform the state-of-the-art traditional algorithms.
This paper is included in the Proceedings of the Seventeenth Symposium on Usable Privacy and Security.

Phishing is a prevalent cyber threat, targeting individuals and organizations alike. Previous approaches on anti-phishing measures have started to recognize the role of the user, who, at the center of the target, builds the last line of defense. However, user-oriented phishing interventions are fragmented across a diverse research landscape, which has not been systematized to date. This makes it challenging to gain an overview of the various approaches taken by prior works. In this paper, we present a taxonomy of phishing interventions based on a systematic literature analysis. We shed light on the diversity of existing approaches by analyzing them with respect to the intervention type, the addressed phishing attack vector, the time at which the intervention takes place, and the required user interaction. Furthermore, we highlight shortcomings and challenges emerging from both our literature sample and prior meta-analyses, and discuss them in the light of current movements in the field of usable security. With this article, we hope to provide useful directions for future works on phishing interventions.
Detectability of COVID-19 global emissions reductions in local CO2 concentration measurements

It is estimated that global anthropogenic carbon dioxide (CO2) emissions reduced by up to 12% at the start of 2020 compared to recent years due to the COVID-19 related downturn in economic activity. Despite the large decrease in CO2 emissions, no reduction in the trend in background atmospheric CO2 concentrations has been detected. So, how long would it take for sustained COVID-19 CO2 emission reductions to be detected in daily and monthly averaged local CO2 concentration measurements? CO2 concentration measurements for five measurement sites in the UK and Ireland are combined with meteorological numerical weather prediction data to build statistical models that can predict future CO2 concentrations. It is found that 75% of the observed daily variability can be explained by these simple models. Emission reduction scenario experiments using these simple models illustrate that large daily and seasonal variability in local CO2 concentrations precludes the rapid emergence of a detectable signal. COVID-19 magnitude emissions reductions would only be detectable in the daily CO2 concentrations after at least 38 months and in monthly CO2 concentrations after 11 months of sustained reductions. For monthly CO2 concentrations the time of emergence is similar for all sites since the seasonal variability is largely driven by non-local fluxes of CO2 between the terrestrial biosphere and the atmosphere. The COVID-19 CO2 anthropogenic emissions reductions are similar in magnitude to those that are required to meet the Paris Agreement target of keeping global temperatures below 2° C. This study demonstrates that, using measurements alone, there will be a considerable lag between changes in global anthropogenic emissions and a detected signal in local CO2 concentration trends. Thus, there is likely to be a delay of several years between changes in policy designed to meet CO2 anthropogenic emissions targets and our ability to detect the impact of these policies on CO2 concentrations using atmospheric measurements alone.
A universal protocol for bidirectional controlled teleportation with network coding

We investigate bidirectional teleportation that works in a fair and efficient manner. Two explicit protocols are proposed to realize bidirectional teleportation with a controller. One is a symmetric protocol for two-qubit states. The other is an asymmetric protocol for single- and two-qubit states. We then devise a universal protocol for arbitrary n 1- and n 2-qubit states via a (2n 1 + 2n 2 + 1)-qubit entangled state, where n 1 ≤ n 2. The receiver only needs to perform the single-qubit recovery operation, which is derived by a general expression. Moreover, a (2n 1 + 1)-bit classical communication cost can be saved within the controller’s broadcast channel by the use of network coding technology.
Effects of a negative corona discharge on subsequent positive streamers

Positive streamers can be affected significantly by preceding discharges such as earlier streamers or a corona discharge. In this paper, we primarily discuss the effect of such a corona discharge on subsequent positive streamers with different intervals between them (0/500/990 ms) in air and pure nitrogen at 80 mbar with a repetition frequency of 1 Hz. We found that in air, when the interval time is 500 or 990 ms, a preceding discharge leads to shorter and weaker streamers or even prevents streamer inception altogether. This is likely due to negative ions which have converted to species that are harder to detach. The weaker streamers are also caused by more stable inception clouds which branch out later. When a corona discharge immediately precedes a streamer discharge in air, the streamers become longer, brighter while some branches develop from the side of the electrode instead of its tip. These effects are likely all related to plasma shielding caused by the corona discharge. In nitrogen, inception is primarily caused by electrons instead of negative ions. When the interval time is 500 or 990 ms in this gas, there is nearly no difference between streamers with and without a preceding corona discharge, because the inception time in nitrogen is the formative time that is not sensitive to the initial electron density. For near-zero intervals between corona and streamer discharges in nitrogen, streamers become smoother and thicker which can be attributed to a higher background ionization left by the corona discharge.
On the issue of using AutoCAD to assess flow rates (on the example of the Kama Reservoir)

A method of visualization and further analysis of the flow rate field is proposed. The method is based on using capabilities of the AutoCAD software package, which is currently widely used in research in the geosciences, in particular in hydrological researches. As a result of the work done, the flow rate field on the straight sections of the Kama River could be visualized. Information from speed profiles located close to each other was used. The applied method, which consists in extruding isotachs, turned out to be time-consuming, but representative; it cannot yet be used for section lines located around the turn or remote from each other, and in areas with tributaries. The considered method is planned to be tested on the data of hydrochemical survey in order to analyse the distribution of impurities.
Mycrocystin-LR degradation by indigenous bacterial community of Rybinsk Reservoir

The fate of microcystin-LR (MC-LR) in water samples of Rybinsk Reservoir taken before and during water bloom was assessed. The decrease in the MC-LR concentration in the both samples was due to its biodegradation by indigenous bacterial community. The highest biodegradation rate and shortest half-life of MC-LR in the water sample taken during water bloom were observed. A microcystin degrading bacterium was isolated and shown be 98.92% similar to Sphingopyxis sp. based on a fragment of 16S rRNA gene sequence. The rate of MC-LR biodegradation by Sphingopyxis sp. S7 was strongly dependent on initial concentration of toxin and temperature.
A random forest-based approach for survival curves comparison: principles, computational aspects, and asymptotic time complexity analysis

The log-rank test and Cox’s proportional hazard model can be used to compare survival curves but are limited by strict statistical assumptions. In this study, we introduce a novel, assumption-free method based on a random forest algorithm able to compare two or more survival curves. A proportion of the random forest’s trees with sufficient complexity is close to the test’s p-value estimate. The pruning of trees in the model modifies trees’ complexity and, thus, both the method’s robustness and statistical power. The discussed results are confirmed using a simulation study, varying the survival curves and the tree pruning level.
Deep Learning Augmented Data Assimilation: Reconstructing Missing Information With Convolutional Autoencoders

Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., sub-cloud area for satellite infrared radiance or no-precipitation region for radar reflectivity). Such data gaps impair the accuracy of initial conditions derived from data assimilation (DA), which has a negative impact on NWP. We use the barotropic vorticity equation to demonstrate the potential of deep learning augmented data assimilation (DDA), which involves reconstructing spatially complete pseudo-observation fields from incomplete observations and using them for DA. By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep-learning approximation of the “reconstruction operator,” which maps spatially incomplete observations to a model state with full spatial coverage and resolution. The CAE was applied to an incomplete stream function observation (∼30% missing) from a high-resolution benchmark simulation and demonstrated satisfactory reconstruction performance, even when only very sparse (1/16 of T63 grid density) observations were used as input. When only spatially incomplete observations are used, the analysis fields obtained from Ensemble Square Root Filter (EnSRF) assimilation exhibit significant error. However, in DDA, when EnSRF takes in the combined data from the incomplete observations and CAE reconstruction, analysis error reduces significantly. Such gains are more pronounced with sparse observation and small ensemble size because the DDA analysis is much less sensitive to observation density and ensemble size than the conventional DA analysis, which is based solely on incomplete observations.