Miracle-dz/UniFAR-FTUs huggingface dataset:
Response of benthic microbial mats to drifting green algal mats

Eutrophication has increased the amount of drifting ephemeral algae in shallow coastal bays. These floating algal mats can be expected to change the light climate of benthic primary producers and to induce oxygen deficiency, which may increase nutrient release from the sediment. The impact of healthy green algal mats, mainly Enteromorpha spp. , on benthic microbial mats was studied in five 2 to 6 wk outdoor experiments by incubating intact sediment below natural green algal mats from a microtidal bay on the west coast of Sweden. The microbial mats were cohesive and dominated by large motile diatoms, mainly Gyrosigma balticum. Estimates of response were based on biomass and con~position of microalgae and meiofauna, biomass of bacteria, chlorophyll a content, pigment ratios. primdrl; productivity, sediment oxygen microprofiles, and inorganic nutrient fluxes. Occasional held measurements of light, oxygen, and inorganic nutnents above and belo\\l green algal mats were made The results suggest that benthic diatom-dominated microbial mats need not be outcompeted by average amounts of healthy green algae during the growth season. Despite strongly reduced light below the green algae, no adverse effects on the biomass oi microorganisms were found. Either no quantitative effects were found at all, or an initial stimulus of rnicroalgal biomass was observed. The only consistent pattern reflecting treatment was that the sediment surface below the green algae became dark brown, while cores with no green algal cover became greylsh. This appeared to be caused by a different vertical position of diatoms within the sediment. Sediment oxygen profiles indicated a higher photosynthetic efficiency below the green algal mats, indicating light acclimation of the microalgae. It is suggested that shade adaptation and sediment nutrient supply enable the diatoms, and other microbiota, to coexist with moderate amounts of healthy drift algae. Both in sitir and in the experiments, algal photosynthesis kept oxygen concentrations above critical values, even at night, thereby preventing redox-related nutrient outflux from the sediment. The results hint at some not yet fully understood mechanisms of dlatom-dominated microbial mats for adaptlng to life below another algal mat. Although light-induced orientation and vertical movement of diatoms appear to play a major role in thls acclimation, the role of other mechanisms, such as hetel-otrophic nutrition, should be examined.
Single-ion addressing via trap potential modulation in global optical fields

To date, individual addressing of ion qubits has relied primarily on local Rabi or transition frequency differences between ions created via electromagnetic field spatial gradients or via ion transport operations. Alternatively, it is possible to synthesize arbitrary local one-qubit gates by leveraging local phase differences in a global driving field. Here we report individual addressing of 40Ca+ ions in a two-ion crystal using axial potential modulation in a global gate laser field. We characterize the resulting gate performance via one-qubit randomized benchmarking, applying different random sequences to each co-trapped ion. We identify the primary error sources and compare the results with single-ion experiments to better understand our experimental limitations. These experiments form a foundation for the universal control of two ions, confined in the same potential well, with a single gate laser beam.
Rational and p-local motivic homotopy theory

Let $F$ and $k$ be perfect fields. The main goal of this paper is to investigate algebraic models for the Morel-Voevodsky unstable motivic homotopy category $\mathrm{Ho}(F)$ after $\mathbf{H}^{\mathbb{A}^1}k$ localization. More specifically, we extend results of Goerss to the $\mathbb{A}^1$-algebraic topology setting: we study the homotopy theory of the category $s\mathrm{coCAlg}_k(Sm_F)$ of presheaves of simplicial coalgebras over a field $k$ and their $\tau$ and $\mathbb{A}^1$-localizations. For $k$ algebraically closed, we show that the unit of the adjunction $k^{\delta}[-]\dashv(-)^{gp}$ determines the $\mathbf{H}^{\mathbb{A}^1}k$ homotopy type, where $k^{\delta}[-]$ is the canonical coalgebra functor induced by the diagonal map $\Delta:\mathcal{X}\rightarrow \mathcal{X}\times \mathcal{X}$. We extend this result for the category of presheaves of coalgebras over a non-algebraically closed field $k$ and the category of discrete $G$-motivic spaces, for $G=Gal(\bar{k}/k)$. On the other hand, we show that the category of coalgebra objects in $\mathrm{PST}(Sm_F,k)$ is locally presentable, where $\mathrm{PST}(Sm_F,k)$ is the category of presheaves with Voevodsky transfers and the monoidal structure is given by a Day convolution product.
Plane shockwave generation in liquid water using irradiation of terahertz pulses

We demonstrate shockwave propagation with a plane wave front in liquid water using a terahertz (THz) laser pulse. The THz light can effectively generate the shockwave in water due to strong absorption via a stretching vibration mode of the hydrogen bonding network. This produces a plane shockwave from a large area excitation of the water surface irradiated by loosely focused THz light. This contrasts with conventional spherical shockwaves triggered by plasma generation via multi-photon absorption of near-infrared light. The shockwave generation and the plane wave propagation were observed using a system with a THz free electron laser and shadowgraph imaging. The plane shockwave was generated by small irradiation energy density of THz light, 0.3 GW/cm2, and delivered 100 times deeper than the penetration depth for water. The THz-light-induced plane shockwave offers great advantages compared to the spherical shockwave, and it will open non-thermal operations for industrial and biological applications.
Emerging Natural User Interfaces in Mobile Computing: A Bottoms-Up Survey

Mobile and wearable interfaces and interaction paradigms are highly constrained by the available screen real estate, and the computational and power resources. Although there exist many ways of displaying information to mobile users, inputting data to a mobile device is, usually, limited to a conventional touch based interaction, that distracts users from their ongoing activities. Furthermore, emerging applications, like augmented, mixed and virtual reality (AR/MR/VR), require new types of input methods in order to interact with complex virtual worlds, challenging the traditional techniques of Human-Computer Interaction (HCI). Leveraging of Natural User Interfaces (NUIs), as a paradigm of using natural intuitive actions to interact with computing systems, is one of many ways to meet these challenges in mobile computing and its modern applications. Brain-Machine Interfaces that enable thought-only hands-free interaction, Myoelectric input methods that track body gestures and gaze-tracking input interfaces - are the examples of NUIs applicable to mobile and wearable interactions. The wide adoption of wearable devices and the penetration of mobile technologies, alongside with the growing market of AR/MR/VR, motivates the exploration and implementation of new interaction paradigms. The concurrent development of bio-signal acquisition techniques and accompanying ecosystems offers a useful toolbox to address open challenges. In this survey, we present state-of-the-art bio-signal acquisition methods, summarize and evaluate recent developments in the area of NUIs and outline potential application in mobile scenarios. The survey will provide a bottoms-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) portable NUI hardware solutions, (iii) NUI-enabled applications, as well as (iv) research challenges and open problems.
Long-Term Ecological Records and Their Relevance to Climate Change Predictions for a Warmer World

This review focuses on biotic responses during intervals of time in the fossil record when the magnitude and rate of climate change exceeded or were comparable with those predicted to occur in the next century (Solomon et al. 2007). These include biotic responses during: (a) the Paleo-Eocene Thermal Maximum and early Eocene Climatic Optimum, (b) the mid-Pliocene warm interval, (c) the Eemian, and (d) the most recent glacial-interglacial transition into the Holocene. We argue that although the mechanisms responsible for these past changes in climate were different (i.e., natural processes rather than anthropogenic), the rate and magnitude of climate change were often similar to those predicted for the next century and therefore highly relevant to understanding future biotic responses. In all intervals we examine the fossil evidence for the three most commonly predicted future biotic scenarios, namely, extirpation, migration (in the form of a permanent range shift), or adaptation. Focusing predominantly on .
Grouping Capsules Based Different Types

Capsule network was introduced as a new architecture of neural networks, it encoding features as capsules to overcome the lacking of equivariant in the convolutional neural networks. It uses dynamic routing algorithm to train parameters in different capsule layers, but the dynamic routing algorithm need to be improved. In this paper, we propose a novel capsule network architecture and discussed the effect of initialization method of the coupling coefficient $c_{ij}$ on the model. First, we analyze the rate of change of the initial value of $c_{ij}$ when the dynamic routing algorithm iterates. The larger the initial value of $c_{ij}$, the better effect of the model. Then, we proposed improvement that training different types of capsules by grouping capsules based different types. And this improvement can adjust the initial value of $c_{ij}$ to make it more suitable. We experimented with our improvements on some computer vision datasets and achieved better results than the original capsule network
Fully Kinetic versus Reduced-kinetic Modeling of Collisionless Plasma Turbulence

We report the results of a direct comparison between different kinetic models of collisionless plasma turbulence in two spatial dimensions. The models considered include a first-principles fully kinetic (FK) description, two widely used reduced models (gyrokinetic (GK) and hybrid-kinetic (HK) with fluid electrons), and a novel reduced gyrokinetic approach (KREHM). Two different ion beta ( ) regimes are considered: 0.1 and 0.5. For , good agreement between the GK and FK models is found at scales ranging from the ion to the electron gyroradius, thus providing firm evidence for a kinetic Alfvén cascade scenario. In the same range, the HK model produces shallower spectral slopes, presumably due to the lack of electron Landau damping. For , a detailed analysis of spectral ratios reveals a slight disagreement between the GK and FK descriptions at kinetic scales, even though kinetic Alfvén fluctuations likely still play a significant role. The discrepancy can be traced back to scales above the ion gyroradius, where the FK and HK results seem to suggest the presence of fast magnetosonic and ion Bernstein modes in both plasma beta regimes, but with a more notable deviation from GK in the low-beta case. The identified practical limits and strengths of reduced-kinetic approximations, compared here against the FK model on a case-by-case basis, may provide valuable insight into the main kinetic effects at play in turbulent collisionless plasmas, such as the solar wind.
The Density Gradient Inside Molecular-gas Clumps as a Booster of Their Star Formation Activity

Star-forming regions presenting a density gradient experience a higher star formation rate than if they were of uniform density. We refer to the ratio between the star formation rate of a spherical centrally concentrated gas clump and the star formation rate that this clump would experience if it were of uniform density as the magnification factor ζ. We map ζ as a function of clump mass, radius, initial volume-density profile, and star formation timespan. For clumps with a steep density profile (i.e., a power-law slope ranging from −3 to −4, as observed in some high-density regions of Galactic molecular clouds), we find their star formation rate to be at least an order of magnitude higher than their top-hat equivalent. This implies that such clumps experience faster and more efficient star formation than expected based on their mean freefall time. This also implies that measurements of the star formation efficiency per freefall time of clumps based on their global properties, namely, mass, mean volume density, and star formation rate, present wide fluctuations. These reflect the diversity in the density profile of star-forming clumps, not necessarily variations in the physics of star formation. Steep density profiles inside star-cluster progenitors may be instrumental in the formation of multiple stellar populations, such as those routinely observed in old globular clusters.
Fractal Impedance for Passive Controllers

During the last decade, there has been increasing interest in new control frameworks to move robots from their industrial cages to unstructured environments where they may coexist with humans. Despite significant improvement in some specific applications (e.g., medical robotics), there is still the need of a general control framework to improve the robots' dynamics interaction performance without limiting system safety. The passive control framework has shown promising results in this direction; however, it relies on virtual energy tanks that can guarantee passivity as long as they do not run out of energy. In this paper, a fractal attractor is proposed to implement a variable impedance controller that is able to retain passivity without relying on the energy tank framework. The results prove that the proposed method can accurately track trajectories and apply forces at the end-effector. Furthermore, it can automatically deal with the extra energy introduced by changes in interaction conditions, null-space controller and environment. Therefore, These properties make the controller ideal for applications where the dynamic interaction at the end-effector is difficult to be characterized in advance, such as human-robot interaction and unknown dynamics.
Counting Small Permutation Patterns

A sample of n generic points in the xy-plane defines a permutation that relates their ranks along the two axes. Every subset of k points similarly defines a pattern, which occurs in that permutation. The number of occurrences of small patterns in a large permutation arises in many areas, including nonparametric statistics. It is therefore desirable to count them more efficiently than the straightforward ~O(n^k) time algorithm. This work proposes new algorithms for counting patterns. We show that all patterns of order 2 and 3, as well as eight patterns of order 4, can be counted in nearly linear time. To that end, we develop an algebraic framework that we call corner tree formulas. Our approach generalizes the existing methods and allows a systematic study of their scope. Using the machinery of corner trees, we find twenty-three independent linear combinations of order-4 patterns, that can be computed in time ~O(n). We also describe an algorithm that counts another 4-pattern, and hence all 4-patterns, in time ~O(n^(3/2)). As a practical application, we provide a nearly linear time computation of a statistic by Yanagimoto (1970), Bergsma and Dassios (2010). This statistic yields a natural and strongly consistent variant of Hoeffding's test for independence of X and Y, given a random sample as above. This improves upon the so far most efficient ~O(n^2) algorithm.
Switching Quantum Reference Frames for Quantum Measurement

Physical observation is made relative to a reference frame. A reference frame is essentially a quantum system given the universal validity of quantum mechanics. Thus, a quantum system must be described relative to a quantum reference frame (QRF). Further requirements on QRF include using only relational observables and not assuming the existence of external reference frame. To address these requirements, two approaches are proposed in the literature. The first one is an operational approach (F. Giacomini, et al, Nat. Comm. 10:494, 2019) which focuses on the quantization of transformation between QRFs. The second approach attempts to derive the quantum transformation between QRFs from first principles (A. Vanrietvelde, et al,Quantum4:225, 2020). Such first principle approach describes physical systems as symmetry induced constrained Hamiltonian systems. The Dirac quantization of such systems before removing redundancy is interpreted as perspective-neutral description. Then, a systematic redundancy reduction procedure is introduced to derive description from perspective of a QRF. The first principle approach recovers some of the results from the operational approach, but not yet include an important part of a quantum theory - the measurement theory. This paper is intended to bridge the gap. We show that the von Neumann quantum measurement theory can be embedded into the perspective-neutral framework. This allows us to successfully recover the results found in the operational approach, with the advantage that the transformation operator can be derived from the first principle. In addition, the formulation presented here reveals several interesting conceptual insights. For instance, the projection operation in measurement needs to be performed after redundancy reduction, and the projection operator must be transformed accordingly when switching QRFs. These results represent one step forward in understanding how quantum measurement should be formulated when the reference frame is also a quantum system.
Music Auto-tagging Using CNNs and Mel-spectrograms With Reduced Frequency and Time Resolution

Automatic tagging of music is an important research topic in Music Information Retrieval achieved improvements with advances in deep learning. In particular, many state-of-the-art systems use Convolutional Neural Networks and operate on mel-spectrogram representations of the audio. In this paper, we compare commonly used mel-spectrogram representations and evaluate model performances that can be achieved by reducing the input size in terms of both lesser amount of frequency bands and larger frame rates. We use the MagnaTagaTune dataset for comprehensive performance comparisons and then compare selected configurations on the larger Million Song Dataset. The results of this study can serve researchers and practitioners in their trade-off decision between accuracy of the models, data storage size and training and inference times.
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging Spider dataset this framework boosts the exact match accuracy to 57.2%, surpassing its best counterparts by 8.7% absolute improvement. Further augmented with BERT, it achieves the new state-of-the-art performance of 65.6% on the Spider leaderboard. In addition, we observe qualitative improvements in the model’s understanding of schema linking and alignment. Our implementation will be open-sourced at https://github.com/Microsoft/rat-sql.
Data-Free Point Cloud Network for 3D Face Recognition

Point clouds-based Networks have achieved great attention in 3D object classification, segmentation and indoor scene semantic parsing. In terms of face recognition, 3D face recognition method which directly consume point clouds as input is still under study. Two main factors account for this: One is how to get discriminative face representations from 3D point clouds using deep network; the other is the lack of large 3D training dataset. To address these problems, a data-free 3D face recognition method is proposed only using synthesized unreal data from statistical 3D Morphable Model to train a deep point cloud network. To ease the inconsistent distribution between model data and real faces, different point sampling methods are used in train and test phase. In this paper, we propose a curvature-aware point sampling(CPS) strategy replacing the original furthest point sampling(FPS) to hierarchically down-sample feature-sensitive points which are crucial to pass and aggregate features deeply. A PointNet++ like Network is used to extract face features directly from point clouds. The experimental results show that the network trained on generated data generalizes well for real 3D faces. Fine tuning on a small part of FRGCv2.0 and Bosphorus, which include real faces in different poses and expressions, further improves recognition accuracy.
Sanctioning, Clientelism and Politicization: The Impact of Ethnicity on Primary and Secondary Education in Africa

Ethnic diversity is widely assumed to negatively affect a country’s education outcomes. Yet, comprehensive explanations for this link are still missing. This paper combines the existing theory of the sanctioning ability of villages with general theories on clientelism and politicized ethnicity. The theories are tested using a novel dataset of district level school enrollment in 31 African countries in combination with newly coded indicators for district ethnic diversity, district shares of presidents’ co-ethnics and existing ethnic parties. Estimating a hierarchical model supports the sanctioning and clientelism theory and provides first evidence for a positive effect of ethnic parties on enrollment. Low-income countries with weak institutions benefit significantly from the existence of ethnic parties by an increase in enrollment of over 30 percent. Moreover, nation building policies targeted at uniting diverse populations are not found to provide a cure to the ethnic diversity problem.
'Warriors of the Word' - Deciphering Lyrical Topics in Music and Their Connection to Audio Feature Dimensions Based on a Corpus of Over 100, 000 Metal Songs

We look into the connection between the musical and lyrical content of metal music by combining automated extraction of high-level audio features and quantitative text analysis on a corpus of 124.288 song lyrics from this genre. Based on this text corpus, a topic model was first constructed using Latent Dirichlet Allocation (LDA). For a subsample of 503 songs, scores for predicting perceived musical hardness/heaviness and darkness/gloominess were extracted using audio feature models. By combining both audio feature and text analysis, we (1) offer a comprehensive overview of the lyrical topics present within the metal genre and (2) are able to establish whether or not levels of hardness and other music dimensions are associated with the occurrence of particularly harsh (and other) textual topics. Twenty typical topics were identified and projected into a topic space using multidimensional scaling (MDS). After Bonferroni correction, positive correlations were found between musical hardness and darkness and textual topics dealing with 'brutal death', 'dystopia', 'archaisms and occultism', 'religion and satanism', 'battle' and '(psychological) madness', while there is a negative associations with topics like 'personal life' and 'love and romance'.