Miracle-dz/UniFAR-FTUs huggingface dataset:
Homeologue differential expression in the flavonoid biosynthetic pathway underlies flower colour variation in natural and synthetic polyploids of Nicotiana tabacum (Solanaceae)

Homeologue expression bias occurs when one progenitor copy of a gene is expressed at a higher level than the other in allopolyploids. Morphological variation, including differences in flower colour, exists between natural and synthetic allopolyploids of Nicotiana tabacum and their progenitors. In this study, we use a comparative transcriptomic approach to investigate gene expression differences as well as homeologue bias in the flavonoid biosynthetic pathway (FBP) in these accessions. We do not observe reciprocal homeologue bias between dark and light pink allopolyploids, but the production of light pink flowers is correlated with high FLAVONOL SYNTHASE:DIHYDROFLAVONOL-4-REDUCTASE (FLS:DFR) ratio at 60% of anthesis length due to delayed activation of DFR in these accessions. We do find that natural allopolyploids have stronger homeologue bias than synthetic allopolyploids in both FBP genes and across the transcriptome. While there is no overall subgenome dominance, there is a bias towards expression of N. tomentosiformis homeologues in FBP genes; however, the magnitude of this bias is reduced in allopolyploids compared to the progenitors, suggesting that N. sylvestris homeologues play an active role in the development of flower colour in N. tabacum allopolyploids. In addition, synthetic allopolyploids tend to exhibit trans regulation of homeologues whereas natural allopolyploids often have evolved cis-regulatory differences between homeologues since their origin.
A Current-Reused Ultralow-Power IoT LNA With a Robust Linearization Technique

This letter presents an ultralow-power (ULP) low-noise amplifier (LNA) with a robust linearization technique. A replica of the LNA's amplification transistor is integrated into the negative feedback path as an auxiliary to generate postdistortion (PD) current, where accurate process-voltage–temperature (PVT)-independent third-order distortion cancellation is achieved through constant-gain bias circuits. Furthermore, the noise figure of the proposed LNA is optimized by local-positive feedback (LPF) techniques. The increase in power due to feedback paths is minimized using a current-reused technique. Manufactured in the TSMC 65-nm process, the LNA core exhibits a gain of 18.3 dB at 2.4 GHz and power consumption of 500 <inline-formula> <tex-math notation="LaTeX">$\mu \text {W}$ </tex-math></inline-formula> at a voltage supply of 0.9 V. The proposed linearization technique improves the IIP3 by more than 5.8 dB over a wide temperature range (<inline-formula> <tex-math notation="LaTeX">$-40^{\circ }\text{C}$ </tex-math></inline-formula> to 100 °C). At room temperature, the measured IIP3 is −0.6 dBm, and the noise figure is 3.5 dB at 2.4 GHz.
A Neural Network and Model Predictive Control Based Resilient Architecture for Constrained Cyber-Physical Systems

In this paper, the resilient control problem for constrained cyber-physical systems subject to stealthy data intrusions on the communication channels is considered. The key idea consists in designing a neural network to act as the anomaly detector during the on-line operations. Accordingly the controller unit, based on model predictive control arguments, is developed to take advantage of the resulting detection capabilities. As its main merits are concerned, the overall control architecture has a two-fold merit with respect to the existing literature: it is avoided the need of modifying the detector structure whenever a different class of attacks is considered, and the occurrence of false positive events is significantly mitigated. Finally, a numerical example is provided to show the effectiveness and peculiarities of the proposed approach.
Chemical analysis of Eucalyptus and Rosemary essential oils using gas chromatography-mass spectrometry (GC-MS) and evaluation of their toxicity against the most common urban pest

Using plant essential oils (EOs) as a pesticide alternative has gained increasing interest as a promising strategy to reduce the harmful effects of chemical pesticides. This study aims to investigate the chemical composition of Eucalyptus globulus Labill and Rosmarinus officinalis L. essential oils and evaluate their impacts against Blattella germanica L. under laboratory conditions. The essential oils were prepared from dried leaves using hydro-distillation (HD) as a chemical extraction method. The gas chromatography-mass spectrometry (GC-MS) was employed to analyze and identify their chemical compounds. Bioassays were conducted using the standard contact method recommended by the World Health Organization, and the data were analyzed using the probit regression model. By GC-MS analysis, the major components included 1,8-cineole (50.67%), alpha-pinene (17.48%), limonene (4.26%) for eucalyptus and alpha-pinene (20.67%), camphor (10.69%), 1,8-cineole (9.38%), Borneol (9.02%), comphene (7.15%), and limonene (4.88%) for rosemary. The LD50 values were 9. 27, 10.54, and 3.23 %, and LD95 values increased to 27.2, 22.3 %, and 14.3% for rosemary, eucalyptus, and their mixture. The EOs mixture had a higher repellent effect with a repellency rate of 98.9% at a concentration of 3% compared to 93.3% and 90% at a concentration of 5% for rosemary and eucalyptus alone.
Comparing subsistence strategies in different landscapes of North China 10,000 years ago

A recent switch in the study of the beginning of agriculture is to demonstrate the increasingly closer interaction between people and the landscape and how this would have played a crucial role in the transition to agriculture. Understanding the palaeo-ecology of the local environments at key sites and its relationship with subsistence strategies is critical to an improved appreciation of such interactions. This article examines macro- and micro-plant remains discovered at two important sites in North China, both dated to around 10,000 cal. yr BP. These two sites, Zhuannian and Nanzhuangtou, are located on the terrace of the Bai River in the Yan Mountains and next to Baiyangdian Lake on the piedmont of the Taihang Mountains, respectively. The floral remains at these two sites provide a great opportunity to examine (1) post-Pleistocene subsistence strategies, (2) the increasing consumption of millets and grassy plants and its significance and (3) the intra-regional diversity in food exploitation and its relationships with local environments. While the macro-plant remains at Nanzhuangtou indicate the importance of aquatic plants in the palaeo-diet, those at Zhuannian suggest a tendency of exploiting tree plants. This significance of these diversified plant food exploitation strategies by the last hunter-gatherers should be paid more attention in future research. Our data also once again confirm the importance of millet consumption to these hunter-gatherers on the eve of millet domestication.
Technical Report Series Center for Data and Simulation Science

Binary trees are fundamental objects in models of evolutionary biology and population genetics. Here, we discuss some of their combinatorial and structural properties as they depend on the tree class considered. Furthermore, the process by which trees are generated determines the probability distribution in tree space. Yule trees , for instance, are generated by a pure birth process. When considered as unordered, they have neither a closed-form enumeration nor a simple probability distribution. But their ordered siblings have both. They present the object of choice when studying tree structure in the framework of evolving genealogies.
A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks

We demonstrate how to use generative adversarial networks to improve the small data problem when training brain-computer-interfaces. The new approach is based on finely graded frequency bands, which are extracted from motor imagery electroencephalography data by using power spectral density method to synthetically generate electroencephalography data using generative adversarial networks. We evaluate our approach using one of the currently largest publicly available electroencephalography datasets, by first checking the synthetic and real data for statistical and visual similarity, and secondly, by training a random forest classifier, once using only the real data and then using the real data augmented with the synthetic data. With similarity scores of 95.72 % in the subject-dependent case and 83.51 % in the subject-independent case, and a predictive gain of 17.53 % in the subject-dependent case, and 7.51 % in the subject-independent case, we were able to achieve promising results. The results show that our approach can make it possible to research rare diseases for which there is too little patient data. Also, synthetic data can be a way for many electroencephalography-based brain-computer interface applications to obtain the required data more cost- and time-efficiently.
12. CARBONATE PRODUCTION AND DISSOLUTION IN THE WESTERN EQUATORIAL ATLANTIC DURING THE LAST 1 M.Y. 1

Variations in calcium carbonate sedimentation on Ceara Rise have been dominated by dilution of noncarbonate material derived from the Amazon. There has been a continuous increase in the input of this material during the last 1 m.y. , which may be related to a long-term increase in erosion in the Amazon drainage basin. In addition, large glacial-interglacial differences in carbonate concentration are caused mainly by increased dilution by noncarbonate material during low sea levels. In the shallowest sites, long-term average carbonate accumulation has been relatively constant, suggesting that average carbonate productivity has been constant. But there are indications that accumulation has varied on shorter time scales: a prominent maximum at about 410 ka, for instance, exhibits accumulation rates that are twice the average value. Bathymetric differences in carbonate accumulation imply that dissolution was more severe in the deepest site prior to about 433 ka. Throughout the last 1 m.y. , carbonate accumulation rates and concentrations have been converging toward similar values at all water depths, implying that the bathymetric gradient in [CO 3 Ð2 ], which marks the transition from North Atlantic Deep Water to Circumpolar Deep Water, has been reducing. Therefore, the calcite saturation levels of the two water masses have become more similar over the last 1 m.y.
Effects of Artists' Death and Trade Frequency on Art Forgery

This paper empirically investigated the factors that may affect art forgery, using 5,130 authentication request data drawn from Korean Art Appraisal Board (KAAB) during 2003 and 2012. Living status of artists and trade frequency of artworks of the artists were considered as explanatory variables that may affect authentication requests and forgery ratio. We found that artists' death has significantly positive impacts on both authentication requests and forgery ratio, while trade frequency of artworks of the artists has the impact only on authentication requests. These results imply that art forgery may have multidimensional characteristics.
Machine Learning-Assisted Codebook Design for MMSE Channel Estimation

In order to realize the high spectral efficiency promised by the new radio, the channel estimation algorithm shall be carefully designed to achieve high accuracy. Conventional optimal estimation techniques require the knowledge of secondorder statistics, which is difficult to compute in real-time systems. In this work, we propose a machine learning-assisted codebook design for minimum mean square error (MMSE) channel estimation, where ML techniques are applied to cluster the channels that minimize the sum normalized mean square error (MSE). Specifically, we use k-medoids as the clustering method and we propose the normalized mismatched weight error performance as the clustering dissimilarity measure. Simulation results demonstrate the close-to-optimal channel estimation performance with reasonable codebook size, and robustness to signal-to-noise-ratio (SNR) changes and truncated codeword length.
Balancing Requests for Multi-Agent Distributed Servers in Augmented Reality Services

Multi-agent management strategies rely either on the classic client-server architecture or complex inter-agent communications. Distributed control algorithms allow you to balance the load. In this paper, an innovative technology for organizing a distributed structure of servers is considered in such a way as to reduce the computational load on the nearest servers while maintaining an adequate response time to client requests, regardless of the geographical location of both clients and servers in the network. The proposed model allows to reduce the impact of random interference on short-term tasks in the system and decrease the summary duration of all long-term tasks, improving the quality of the augmented reality service provided in general.
Use of imagery to indicate landscape features important when assessing environmental risk caused by olive farming and olive oil production

Low vegetation and linear features, such as terraces, paths, hedges and fences can contribute to estimation of management factor in soil erosion models, while recognition, allocation, and identification of olive mills waste tanks can support selection indicators and setup of criteria for pollution risk assessment. In this work, the olive oil production area of Kolymvari in the island of Crete, Greece was selected in order to indicate the aforementioned features. An image of very high spatial resolution, namely a QuickBird image was used and visual photo-interpretation as well as object-oriented analysis was applied. Use of a DEM, the cooperation with local experts, and a field survey assisted the interpretation of the image. Eleven out of 15 waste tanks recognised in the image were validated. Linear features and low vegetation were successfully identified after support by object-oriented analysis.
Spatio-Temporal Variation and Monsoon Effect on the Temperature Lapse Rate of a Subtropical Island

Temperature lapse rate (TLR) has been widely used in the prediction of mountain climate and vegetation and in many ecological models. The aims of this paper are to explore the spatio-temporal variations and monsoon effects on the TLR in the subtropical island of Taiwan with its steep Central Mountain Region (CMR). A TLR analysis using the 32-year monthly mean air temperatures and elevations from 219 weather stations (sea level to 3852 m a.s.l.) was performed based on different geographical regions and monsoon exposures. The results revealed that the average TLR for all of Taiwan is -5.17°C km^(-1), with a general tendency to be steeper in summer and shallower in winter. The results are also shallower than the typical or global average TLR of -6.5°C km^(-1). During the prevailing northeast monsoon season (winter), the TLR exhibits a contrast between the windward side (steeper, -5.97°C km^(-1)) and the leeward side (shallower, -4.51°C km^(-1)). From the diagnosis on spatial characteristics of monthly cloud amount and vertical atmospheric profiles, this contrasting phenomenon may be explained by the warming effect of onshore stratus clouds (500 - 2500 m depth) on cold and dry Siberian monsoon air on the windward side of the CMR. On the southwestern leeward side of the CMR, the low-level (1500 m), the weak ventilation atmosphere and temperature inversion make the TLR shallower than on the windward side.
A Tensor-Based High Resolution Millimeter Wave Massive MIMO Channel Parameters Estimation Scheme

Channel estimation is vital for wireless communication and channel sounding. In this paper, we propose a tensor-based high-resolution estimation method for a millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) based channel sounding system with hybrid beamforming (HBF) adopted. At the transmitter (Tx), the OFDM pilot signal is sent with the help of a precoder with random phases. At the receiver (Rx) configured with a random-phased combiner, the received signal is concatenated into a third-order tensor which follows a CANDECOMP/PARAFAC (CP) decomposition to prospect implicit channel information. The CP decomposition is solved by leveraging the Vandermonde structure of the factor matrices, from which the channel parameters are estimated by correlationbased searching. Simulation results with the synthesized channel data illustrate that the proposed tensor-based algorithm can obtain high stability and accuracy. The influence of the pilot sequence length on the accuracy of the proposed algorithm is also discussed. Furthermore, we find that the proposed algorithm is not affected by the quantization bit of the precoder and combiner.
Hippocampal atrophy is associated with hearing loss in cognitively normal adults

Objectives A growing body of evidence suggests that age-related hearing loss (HL) is associated with morphological changes of the cerebral cortex, but the results have been drawn from a small amount of data in most studies. The aim of this study is to investigate the correlation between HL and gray matter volume (GMV) in a large number of subjects, strictly controlling for an extensive set of possible biases. Methods Medical records of 576 subjects who underwent pure tone audiometry, brain magnetic resonance imaging (MRI), and the Korean Mini-Mental State Exam (K-MMSE) were reviewed. Among them, subjects with normal cognitive function and free of central nervous system disorders or coronary artery disease were included. Outliers were excluded after a sample homogeneity check. In the end, 405 subjects were enrolled. Pure tone hearing thresholds were determined at 0.5, 1, 2, and 4 kHz in the better ear. Enrolled subjects were divided into 3 groups according to pure tone average: normal hearing (NH), mild HL (MHL), and moderate-to-severe HL (MSHL) groups. Using voxel-based morphometry, we evaluated GMV changes that may be associated with HL. Sex, age, total intracranial volume, type of MRI scanner, education level, K-MMSE score, smoking status, and presence of hypertension, diabetes mellitus and dyslipidemia were used as covariates. Results A statistically significant negative correlation between the hearing thresholds and GMV of the hippocampus was elucidated. Additionally, in group comparisons, the left hippocampal GMV of the MSHL group was significantly smaller than that of the NH and MHL groups. Conclusion Based on the negative correlation between hearing thresholds and hippocampal GMV in cognitively normal old adults, the current study indicates that peripheral deafferentation could be a potential contributing factor to hippocampal atrophy.
Twenty Thousand Aftershocks of a Very Small (M 2) Earthquake and Their Relation to the Mainshock Rupture and Geological Structures

We have determined the locations of more than 20,000 aftershocks (as small as moment magnitude Mw 4:4 or even smaller) following an M 2 event in a South African gold mine, using manually picked arrival times. Spatial clustering into fivegroupswasclearlydiscerned.Amajorityoftheaftershocksformedaplanarcluster (∼4 m in apparent thickness, ∼100 × 80 m in areal extent). This cluster is thought to delineate the rupture area of the mainshock because its orientation and spatial extent wereconsistent with thenodalplaneofthecentroid moment tensor (CMT)solution and withthecornerfrequencyofthemainshock,respectively.Thecluster'sattitudesuggests that the mainshock was a Mohr-Coulomb failure (or formation of a shear rupture sur- faceinintactrockatananglethatobeystheCoulombfailurecriterion)thattookplacein a vertical compression stress field that is indicated by borehole breakout patterns. The aftershock distribution also shows that the mainshock rupture was largely confined to the interior of a 25-m-thick vertical dike, although there are indications of interactions taking place between the rupture and the dike's material boundary with the host rock.
Experimental Study on Output Robust Control of a Surface Vessel with Uncertainties and Exogenous Disturbances

In this paper experimental research on the output control problem of a surface vessel with parametric uncertainties and external disturbances is shown. We propose application of the robust control framework based on the extended observer design with subsequent augmenting with the internal model to reject external disturbances affected the robotic boat in laboratory basin. Dynamic Positioning (DP) mode includes task of a station-keeping which can can be effectively solved with proposed algorithm.
An Efficient Multi-Agent Optimization Approach for Coordinated Massive MIMO Beamforming

Beamforming plays an important role in 5G Massive Multiple-Input Multiple-Output (MMIMO) communications. Optimizing beamforming configurations for 5G base stations (BSs) can substantially improve the quality of service for mobile users, and thus has great practical value. However, identifying the optimal beamforming configurations has proven to be a complex task, and is even more challenging when accounting for the unavoidable coupled influence among multiple densely deployed 5G BSs. In this paper, we propose a highly efficient deep multi-agent Bayesian optimization framework for the coordinated beamforming optimization problem that involves multiple BSs. Its core algorithm is built upon a deep ensemble of neural networks and a sample-efficient upper confidence bound (UCB) based exploration strategy. Our numerical results show that the proposed approach is highly effective in searching for optimally coordinated beamforming vectors under extremely large search space, and beats strong multi-agent reinforcement learning baselines in terms of optimization quality and sample efficiency.
Harnessing Natural Language Processing for Mental Health Detection in Malay Text: A Review

This paper comprehensively examines mental health detection in the Malay language using natural language processing (NLP) techniques. With global implications, mental health holds significant importance in Malay-speaking regions. NLP, a specialised branch of artificial intelligence, shows promise in deciphering mental health issues from text data. The review begins by exploring traditional NLP approaches like word frequencies, illustrating their role in mental health research. It then focuses on advanced techniques such as embeddings, neural networks, and transformer-based language models. The paper discusses prevalent mental health disorders in Malay-speaking communities and the challenges in their detection. It also highlights distinctive features of Malay mental health datasets crucial for NLP model development. The review delves into studies utilising NLP to analyse mental health content across social media and online forums in Malay contexts. These studies' methods, findings, and limitations are detailed, demonstrating NLP's potential in identifying mental health problems on a larger scale. The review emphasises NLP's role in Malay mental health detection and underscores the need for ongoing research. Leveraging NLP techniques can provide deep insights into the Malay-speaking mental health scenario, facilitating effective interventions and support for individuals with such challenges.