Miracle-dz/UniFAR-FTUs huggingface dataset:
Dinitrogen-fixing endogenous rhythm in Synechococcus RF-1

Under continuous illumination this unicellular aerobic cyanobacterium fixes dinitrogen continuously at a variable and usually low rate. Exposure of the culture to diurnal light/dark cycles invariably results in the virtual restriction of nitrogenase activity to the dark periods. The rhythmic diurnal dinitrogen fixation pattern becomes a truely endogenous cycle which persists for at least 4 days with decreasing magnitude on exposing the culture to continuous illumination. The free running time of the rhythm appears to decrease from an initial 26 h to 22 h in the course of 4 days. This appears to be the first record of an endogenous rhythm in a prokaryote.
Efficient Receive Beamformers for Secure Spatial Modulation Against a Malicious Full-Duplex Attacker With Eavesdropping Ability

In this paper, we consider a new secure spatial modulation scenario with a full-duplex (FD) malicious attacker Mallory owning eavesdropping capacity, where Mallory works on FD model and transmits a malicious jamming such as artificial noise (AN) to interfere with legitimate user Bob. To suppress the malicious jamming on Bob from Mallory, a conventional maximum receive power (Max-RP) at Bob is presented firstly. Subsequently, to exploit the colored property of noise plus interference at Bob, a whitening-filter-based Max-RP (Max-WFRP) is proposed with an obvious performance enhancement over Max-RP. To completely remove the malicious jamming from Mallory, a Max-RP with a constraint of forcing the malicious jamming from Mallory to zero at Bob is proposed. To further improve secrecy rate (SR) by removing the zero-forcing contraint (ZFC), the maximum signal-to-jamming-plus-noise ratio (Max-SJNR) is proposed. Our proposed methods have closed-form expressions. Simulation results show that the four receive beamforming methods have an increasing order in performance: Max-RP, Max-RP with ZFC, and Max-SJNR $\approx$ Max-WFRP. Additionally, the latter two harvest a substantial performance gains over Max-RP and Max-RP with ZFC in the low and medium signal-to-noise ratio regions.
A Survey of Neural Networks and Formal Languages

This report is a survey of the relationships between various state-of-the-art neural network architectures and formal languages as, for example, structured by the Chomsky Language Hierarchy. Of particular interest are the abilities of a neural architecture to represent, recognize and generate words from a specific language by learning from samples of the language.
Dilated U-net based approach for multichannel speech enhancement from First-Order Ambisonics recordings

We present a CNN architecture for speech enhancement from multichannel first-order Ambisonics mixtures. The data-dependent spatial filters, deduced from a mask-based approach, are used to help an automatic speech recognition engine to face adverse conditions of reverberation and competitive speakers. The mask predictions are provided by a neural network, fed with rough estimations of speech and noise amplitude spectra, under the assumption of known directions of arrival. This study evaluates the replacing of the recurrent LSTM network previously investigated by a convolutive U-net under more stressing conditions with an additional second competitive speaker. We show that, due to more accurate short-term masks prediction, the U-net architecture brings some improvements in terms of word error rate. Moreover, results indicate that the use of dilated convolutive layers is beneficial in difficult situations with two interfering speakers, and/or where the target and interferences are close to each other in terms of the angular distance. Moreover, these results come with a two-fold reduction in the number of parameters.
SWAGGER: Sparsity Within and Across Groups for General Estimation and Recovery

Penalty functions or regularization terms that promote structured solutions to optimization problems are of great interest in many fields. Proposed in this work is a nonconvex structured sparsity penalty that promotes one-sparsity within arbitrary overlapping groups in a vector. This allows one to enforce mutual exclusivity between components within solutions to optimization problems. We show multiple example use cases (including a total variation variant), demonstrate synergy between it and other regularizers, and propose an algorithm to efficiently solve problems regularized or constrained by the proposed penalty.
A Thousand Words are Worth More Than One Recording: NLP Based Speaker Change Point Detection

Speaker Diarization (SD) consists of splitting or segmenting an input audio burst according to speaker identities. In this paper, we focus on the crucial task of the SD problem which is the audio segmenting process and suggest a solution for the Change Point Detection (CPD) problem. We empirically demonstrate the negative correlation between an increase in the number of speakers and the Recall and F1-Score measurements. This negative correlation is shown to be the outcome of a massive experimental evaluation process, which accounts its superiority to recently developed voice based solutions. In order to overcome the number of speakers issue, we suggest a robust solution based on a novel Natural Language Processing (NLP) technique, as well as a metadata features extraction process, rather than a vocal based alone. To the best of our knowledge, we are the first to propose an intelligent NLP based solution that (I) tackles the CPD problem with a dataset in Hebrew, and (II) solves the CPD variant of the SD problem. We empirically show, based on two distinct datasets, that our method is abled to accurately identify the CPDs in an audio burst with 82.12% and 89.02% of success in the Recall and F1-score measurements.
Chemical contamination of water in irrigated rice on Paraná State, Brazil

O objetivo deste trabalho foi avaliar a contaminacao da agua provocada pelo uso de agroquimicos na cultura de arroz irrigado em parte da Area de Protecao Ambiental das Ilhas e Varzeas do Rio Parana, no municipio de Querencia do Norte. Esta regiao e caracterizada por uma grande riqueza natural, bem como pela necessidade de producao agricola. Foram realizadas quatro secoes de coletas de agua, sendo duas no verao e duas no inverno, que ocorreram em pontos pre-estabelecidos, nos rios e canais de drenagem em torno das areas de cultivo de arroz, inclusive no rio Ivai que e receptor de aguas destes locais, utilizando-se galoes de 5 litros. Para determinacao da concentracao dos herbicidas, foi utilizada aliquota de 250 mL de cada amostra, com solucao hexano/diclorometano, concentracao do extrato e afericao do volume em acetonitrila. A medicao foi realizada por cromatografia liquida de alta eficiencia com espectrometria de massa sequencial. Os resultados demonstraram presenca de Organofosforados, Metilcarbamatos, Cloroacetamidas, Triazois, Piretroides, Triazinas, Acetamidas e Pirazois. O composto Carbofurano e o mais abundante na area de estudos. A Atrazina e encontrada e determinada como originaria de regioes proximas a area de estudo. Alem da Atrazina, outros compostos nao recomendados para arroz irrigado sao usados nesta regiao, sendo passiveis de acoes legais para punicao.
Coalescing and branching simple symmetric exclusion process

Motivated by kinetically constrained interacting particle systems (KCM), we consider a reversible coalescing and branching simple exclusion process on a general finite graph $G=(V,E)$ dual to the biased voter model on $G$. Our main goal are tight bounds on its logarithmic Sobolev constant and relaxation time, with particular focus on the delicate slightly supercritical regime in which the equilibrium density of particles tends to zero as $|V|\rightarrow \infty$. Our results allow us to recover very directly and improve to $\ell^p$-mixing, $p\ge 2$, and to more general graphs, the mixing time results of Pillai and Smith for the FA-$1$f (or $1$-neighbour) KCM on the discrete $d$-dimensional torus. In view of applications to the more complex FA-$j$f KCM, $j>1$, we also extend part of the analysis to an analogous process with a more general product state space.
Learning optimal environments using projected stochastic gradient ascent

In this work, we generalize the direct policy search algorithms to an algorithm we call Direct Environment Search with (projected stochastic) Gradient Ascent (DESGA). The latter can be used to jointly learn a reinforcement learning (RL) environment and a policy with maximal expected return over a joint hypothesis space of environments and policies. We illustrate the performance of DESGA on two benchmarks. First, we consider a parametrized space of Mass-Spring-Damper (MSD) environments. Then, we use our algorithm for optimizing the size of the components and the operation of a small-scale and autonomous energy system, i.e. a solar off-grid microgrid, composed of photovoltaic panels, batteries, etc. The results highlight the excellent performances of the DESGA algorithm.
Workspace monitoring and planning for safe mobile manipulation

In order to enable physical human-robot interaction where humans and (mobile) manipulators share their workspace and work together, robots have to be equipped with important capabilities to guarantee human safety. The robots have to recognize possible collisions with the human co-worker and react anticipatorily by adapting their motion to avert dangerous situations while they are executing their task. Therefore, methods have been developed that allow to monitor the workspace of mobile manipulators using multiple depth sensors to gather information about the robot environment. This encompasses both 3D information about obstacles in the close robot surroundings and the prediction of obstacle motions in the entire monitored space. Based on this information, a collision-free robot motion is planned and during the execution the robot continuously reacts to unforeseen dangerous situations by adapting its planned motion, slowing down or stopping. For the demonstration of a manufacturing scenario, the developed methods have been implemented on a prototypical mobile manipulator. The algorithms handle both robot platform and manipulator in a uniform manner so that an overall optimization of the path and of the collision avoidance behavior is possible. By integrating the monitoring, planning, and interaction control components, the task of grasping, placing and delivering objects to humans in a shared workspace is demonstrated.
Adaptive convolutional neural networks for k-space data interpolation in fast magnetic resonance imaging

Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing deep learning-based image reconstruction methods typically apply weight-sharing convolutional neural networks (CNNs) to k-space data without taking into consideration the k-space data's spatial frequency properties, leading to ineffective learning of the image reconstruction models. Moreover, complementary information of spatially adjacent slices is often ignored in existing deep learning methods. To overcome such limitations, we develop a deep learning algorithm, referred to as adaptive convolutional neural networks for k-space data interpolation (ACNN-k-Space), which adopts a residual Encoder-Decoder network architecture to interpolate the undersampled k-space data by integrating spatially contiguous slices as multi-channel input, along with k-space data from multiple coils if available. The network is enhanced by self-attention layers to adaptively focus on k-space data at different spatial frequencies and channels. We have evaluated our method on two public datasets and compared it with state-of-the-art existing methods. Ablation studies and experimental results demonstrate that our method effectively reconstructs images from undersampled k-space data and achieves significantly better image reconstruction performance than current state-of-the-art techniques.
Floristic compositions of Inner Mongolian grasslands under different land-use conditions

The severe degradation of grasslands caused by overgrazing and other diverse human operations has become widespread in Inner Mongolia, China. In this study, the vegetations of several grasslands under different land-use conditions were compared to examine the relationships between the vegetation and the disturbance of the grassland. Floristic and life-form compositions of the stands representing the six different sites were researched by the quadrat method: a non-grazed grassland (G1), a lightly grazed grassland (G2), a path (P), an abandoned field (AF) and two artificial meadows (M1 and M2). P, AF, M1 and M2 were located in a part of the grazed grassland. AF, M1 and M2 had been tilled when planted. The dominant species found in G1 and G2 were Potentilla fragarioides, Poa annua, and Cleistogenes squarrosa. Artemisia annua, Chenopodium album, Cannabis sativa f. ruderalis and Calystegia hederacea were found in P and AF, being scarce in the grassland areas. Amaranthus retroflexus, Kochia scoparia and Setaria viridis, which are cosmopolitan weeds, were dominant only in AF. Leymus chinensis, Agropyron cristatum, Potentilla bifurca, and Potentilla tanacetifolia were widely observed across all the surveyed sites. The land-use change from grassland to path caused a decrease of perennial species and an invasion of annual species by heavy trampling. A similar vegetational change occurred with the land-use change from grassland to abandoned field due to the destruction of original vegetation by tillage. Human disturbance such as tillage and trampling also resulted in a decrease in the species with the tufted- and branched-form and an increase in the species with the erect-form. Tillage could produce a more serious impact on the grassland vegetation as compared to grazing.
Optimizing Ultra-Reliable and Low-Latency Communication Systems with Unsupervised Learning

Supervised learning has been introduced to wireless communications to solve complex problems due to its wide applicability. However, generating labels for supervision could be expensive or even unavailable in wireless communications, and constraints cannot be explicitly guaranteed in the supervised manner. In this work, we introduced an unsupervised learning framework, which exploits mathematic models and the knowledge of optimization to search an unknown policy without supervision. Such a framework is applicable to both variable and functional optimization problems with instantaneous and long-term constraints. We take two resource allocation problems in ultra-reliable and low-latency communications as examples, which involve one and two timescales, respectively. Unsupervised learning is adopted to find the approximated optimal solutions of the problems. Simulation results show that the learned solution can achieve the same bandwidth efficiency as the optimal solution in the symmetric scenarios. By comparing the learned solution with the existing policies, our results illustrate the benefit of exploiting frequency diversity and multi-user diversity in improving the bandwidth efficiency in both symmetric and asymmetric scenarios. We further illustrate that, with pre-training, the unsupervised learning algorithm converges rapidly.
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition

In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person). However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction. In this work, we propose to integrate label component information as embeddings into models. Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels.
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization

Advanced data augmentation strategies have widely been studied to improve the generalization ability of deep learning models. Regional dropout is one of the popular solutions that guides the model to focus on less discriminative parts by randomly removing image regions, resulting in improved regularization. However, such information removal is undesirable. On the other hand, recent strategies suggest to randomly cut and mix patches and their labels among training images, to enjoy the advantages of regional dropout without having any pointless pixel in the augmented images. We argue that the random selection of the patch may not necessarily represent any information about the corresponding object and thereby mixing the labels according to that uninformative patch enables the model to learn unexpected feature representation. Therefore, we propose SaliencyMix that carefully selects a representative image patch with the help of a saliency map and mixes this indicative patch with the target image that leads the model to learn more appropriate feature representation. SaliencyMix achieves a new state-of-the-art top-1 error of 20.09% on ImageNet classification using ResNet-101 architecture and also improves the model robustness against adversarial perturbations. Furthermore, SaliencyMix trained model helps to improve the object detection performance.
Supplementary File: Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining

where red and blue are still the same but changed to Y = X ↓s, that are the down-scaled features by by scaling factor s. The naı̈ve cross-scale attention is build upon the correlation between features in different scales but summarises down-scaled features. The down-scaling operation will eliminate high-frequency details and lead performance regression in super-resolution tasks. The proposed cross-scale non-local attention summaries corresponding features in target scale without down-scaling operation, and can be formalized as
Infinite Horizon Optimal Control Framework for Goal Directed Movements

Reaching is the simplest and the most studied type of discrete movements, i.e. movements that have finite durations. For obvious reasons, most optimal control models of reaching were formulated in the finitehorizon framework with a pre-specified terminal time. However, we have discovered significant limitations of such formulations, and investigated the alternative, an infinite-horizon formulation.
TGF‐β1 induces bone closure of skull defects: Temporal dynamics of bone formation in defects exposed to rhTGF‐β1

The temporal dynamics of bone repair in a skull defect in rabbits was examined to characterize the in vivo cellular events occurring following a single local application of recombinant human TGF‐β1 (rhTGF‐β1). Rabbits received vehicle or 0.4, 1, 2, or 5 μg rhTGF‐β1 applied to 12 mm defects at the time of surgery. The defect sites were subsequently evaluated by radiography and qualitative and quantitative histology at time points ranging from 1 to 180 days. Based on radiographic assessment, the defect area decreased rapidly in a dose‐dependent manner through 35 days after surgery in the rhTGF‐β1‐treated groups. Minimal closure occurred in sites administered vehicle control at all time points examined. Sites treated with rhTGF‐β1 were characterized histologically by an increase in parameters of active bone formation through 49 days, including percentage osteoid surface, percentage osteoblast/total surface, and an increase in the trabecular bone volume. Bone resorption parameters were increased at 16 and 49 days with histologic evidence of remodeling from woven to lamellar bone. By 70 days, no differences were observed among the groups for parameters of either bone formation or resorption. Bone formation rate was not altered with rhTGF‐β1 treatment at any time point. These results indicate that exogenously applied rhTGF‐β1 stimulated the recruitment and proliferation of osteoblasts at the defect site, resulting in a rapid deposition of bony matrix, with normal remodeling processes occurring thereafter. This study supports the hypothesis that TGF‐β1 is a potent osteoinductive growth factor in vivo and may have potential application as a therapeutic aid to nonhealing bony defects.
Error tolerant parent identification from a finite set of individuals

We consider using microsatellites for paternity checking and parent identification in different population structures, and allowing for possible typing errors or mutations. Statistical rules derived from the Bayesian and the sampling approaches are discussed in the case involving the choice of the true father–mother pair among a finite set of possible parental pairs. General situations are investigated by means of random simulations, in order to characterize the joint influences of the number and polymorphism of typed loci, the population structure and size, and error rates. Approximate expressions are provided that give the efficiency of a set of markers for identifying the parents in various mating schemes. The importance of a non-zero value for the typing error rate in the likelihood is highlighted.
Influence of undersowing annual forage legumes in early-maturing maize on grain yield and quality of their forage mixtures

An experiment was carried out to investigate the effects of date of undersowing different legume species on maize grain yield and forage quality. Early-maturing maize (Zea mays L. cv. Suwan1) and Tephrosia bracteolata Guill. and Perr. (tephrosia), Lablab purpureus (L.) Sweet (lablab) and Mucuna pruriens (L.) DC. (mucuna) monocultures as well as their mixture at three planting times—same day, 2 weeks after planting (WAP) and 4 WAP of maize—were arranged in a randomised complete block design with three replications. After harvesting fresh maize cobs, herbage samples (maize stover and legumes) from each plot were mixed together in ratios of 50:50 and 70:30 (maize:legume) for chemical analysis. Legume type and time of undersowing significantly affected (P < 0.05) the height and spread of maize from 4 to 6 WAP maize. Legume type and time of undersowing influenced fresh cob weight with the highest (P < 0.05) cob weight and grain yield recorded from maize undersown with tephrosia 2 WAP maize. Undersowing affected dry matter yield, crude protein, neutral detergent fiber and acid detergent fiber concentrations. It is concluded that intercropping of early-maturing maize and legumes can substantially increase forage quantity and quality compared with maize monocultures.