Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization potential of biomass supply chains with torrefaction technology

This study compared the economic and environmental impacts of torrefaction on bioenergy supply chains against conventional pellets for scenarios where biomass is produced in Mozambique, and undergoes pre‐processing before shipment to Rotterdam for conversion to power and Fischer‐Tropsch (FT) fuels. We also compared the impacts of using different land quality (productive and marginal) for feedstock production, feedstocks (eucalyptus and switchgrass), final conversion technologies (XtY and CXtY) and markets (the Netherlands and Mozambique). At current conditions, the torrefied pellets (TOPs) are delivered in Rotterdam at higher cost (7.3–7.5 $/GJ) than pellets (5.1–5.3 $/GJ). In the long term, TOPs costs could decline (4.7–5.8 $/GJ) and converge with pellets. TOPs supply chains also incur 20% lower greenhouse gas (GHG) emissions than pellets. Due to improved logistics and lower conversion investment, fuel production costs from TOPs are lower (12.8–16.9 $/GJFT) than from pellets (12.9–18.7 $/GJFT). Co‐firing scenarios (CXtY) result in lower cost fuel (but a higher environmental penalty) than 100% biomass fired scenarios (XtY). In most cases, switchgrass and the productive region of Nampula provide the lowest fuel production cost compared to eucalyptus and the marginally productive Gaza region. Both FT and ion in Mozambique are more costly than in Rotterdam. For the Netherlands, both FT and power production are competitive against average energy costs in Western Europe. The analysis shows that large‐scale bioenergy production can become competitive against fossil fuels. While the benefits of TOPs are apparent in logistics and conversion, the current higher torrefaction costs contribute to higher biofuel costs. Improvements in torrefaction technology can result in significant performance improvements over the future chain. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd
Genetic Identity of Walleye in the Cumberland River

Abstract Walleye from the upper Cumberland River drainage were examined by means of mitochondrial DNA restriction fragment length polymorphism (RFLP) analysis, microsatellite analysis of nuclear DNA, and sequencing of the mitochondrial DNA control region. The objectives of this study were to identify native Eastern Highland and introduced Great Lakes walleye and determine if introgression occurs between the two stocks. Additional collections were made from rivers of the Eastern Highlands to establish evolutionary relationships. Native and introduced walleye appear reproductively partitioned in the upper Cumberland River drainage, with natives persisting in the tributary Rockcastle River and introduced Great Lakes walleye reproducing in the Big South Fork of the Cumberland River. Additional partitioning between native and introduced stocks may occur in the Kanawha River, in the Ohio River drainage. Native walleye from the Cumberland River share a common ancestor with others from Eastern Highland rivers but are distantly related to populations from the southern Mobile River basin and the northern Great Lakes.
Visual GUI testing in practice: An extended industrial case study

Context: Visual GUI testing (VGT) is referred to as the latest generation GUI-based testing. It is a tool-driven technique, which uses image recognition for interacting with and asserting the behavior of the system under test. Motivated by the industrial need of a large Turkish software and systems company providing solutions in the areas of defense and IT sector, an action-research project was recently initiated to implement VGT in several teams and projects in the company. Objective: To address the above needs, we planned and carried out an empirical investigation with the goal of assessing VGT using two tools (Sikuli and JAutomate). The purpose was to determine a suitable approach and tool for VGT of a given project (software product) in the company, increase the know-how in the company's test teams. Method: Using an action-research case-study design, we investigated the use of VGT in the studied organization. Specifically, using the two selected VGT tools, we conducted a quantitative and a qualitative evaluation of VGT. Results: By assessing the list of Challenges, Problems and Limitations (CPL), proposed in previous work, in the context of our empirical study, we found that test-tool- and SUT-related CPLs were quite comparable to a previous empirical study, e.g., the synchronization between SUT and test tools were not always robust and there were failures in test tools' image recognition features. When assessing the types of test maintenance activities, when executing the automated test cases on next versions of the SUTs, for the case of the two test tools, we found that about half of the test cases (59.1% and 47.8%) failed in the next version. Conclusion: By our results, we confirm some of the previously-reported issues when conducting VGT. Further, we highlight some additional challenges in test maintenance when using VGT.
Normalized Attention Without Probability Cage

Attention architectures are widely used; they recently gained renewed popularity with Transformers yielding a streak of state of the art results. Yet, the geometrical implications of softmax-attention remain largely unexplored. In this work we highlight the limitations of constraining attention weights to the probability simplex and the resulting convex hull of value vectors. We show that Transformers are sequence length dependent biased towards token isolation at initialization and contrast Transformers to simple max- and sum-pooling - two strong baselines rarely reported. We propose to replace the softmax in self-attention with normalization, yielding a hyperparameter and data-bias robust, generally applicable architecture. We support our insights with empirical results from more than 25,000 trained models. All results and implementations are made available.
Mixture Models and Networks -- Overview of Stochastic Blockmodelling

Mixture models are probabilistic models aimed at uncovering and representing latent subgroups within a population. In the realm of network data analysis, the latent subgroups of nodes are typically identified by their connectivity behaviour, with nodes behaving similarly belonging to the same community. In this context, mixture modelling is pursued through stochastic blockmodelling. We consider stochastic blockmodels and some of their variants and extensions from a mixture modelling perspective. We also survey some of the main classes of estimation methods available, and propose an alternative approach. In addition to the discussion of inferential properties and estimating procedures, we focus on the application of the models to several real-world network datasets, showcasing the advantages and pitfalls of different approaches.
Hybrid star HD 81817 accompanied by brown dwarf or substellar companion

HD 81817 is known as a hybrid star. Hybrid stars have both cool stellar wind properties and Ultraviolet (UV) or even X-ray emission features of highly ionized atoms in their spectra. A white dwarf companion has been suggested as the source of UV or X-ray features. HD 81817 has been observed since 2004 as a part of a radial velocity (RV) survey program to search for exoplanets around K giant stars using the Bohyunsan Observatory Echelle Spectrograph at the 1.8 m telescope of Bohyunsan Optical Astronomy Observatory in Korea. We obtained 85 RV measurements between 2004 and 2019 for HD 81817 and found two periodic RV variations. The amplitudes of RV variations are around 200 m s−1, which are significantly lower than that expected from a closely orbiting white dwarf companion. Photometric data and relevant spectral lines were also analyzed to help determine the origin of the periodic RV variations. We conclude that 627.4-day RV variations are caused by intrinsic stellar activities such as long-term pulsations or rotational modulations of surface activities based on Hα equivalent width variations of a similar period. On the other hand, 1047.1-day periodic RV variations are likely to be caused by a brown dwarf or substellar companion, which is corroborated by a recent Gaia proper motion anomaly for HD 81817. The Keplerian fit yields a minimum mass of 27.1 MJup, a semimajor axis of 3.3 AU, and an eccentricity of 0.17 for the stellar mass of 4.3 M⊙ for HD 81817. The inferred mass puts HD 81817 b in the brown dwarf desert.
Atss-Net: Target Speaker Separation via Attention-based Neural Network

Recently, Convolutional Neural Network (CNN) and Long short-term memory (LSTM) based models have been introduced to deep learning-based target speaker separation. In this paper, we propose an Attention-based neural network (Atss-Net) in the spectrogram domain for the task. It allows the network to compute the correlation between each feature parallelly, and using shallower layers to extract more features, compared with the CNN-LSTM architecture. Experimental results show that our Atss-Net yields better performance than the VoiceFilter, although it only contains half of the parameters. Furthermore, our proposed model also demonstrates promising performance in speech enhancement.
Algorithmic Bias and Regularisation in Machine Learning

Often, what is termed algorithmic bias in machine learning will be due to historic bias in the training data. But sometimes the bias may be introduced (or at least exacerbated) by the algorithm itself. The ways in which algorithms can actually accentuate bias has not received a lot of attention with researchers focusing directly on methods to eliminate bias - no matter the source. In this paper we report on initial research to understand the factors that contribute to bias in classification algorithms. We believe this is important because underestimation bias is inextricably tied to regularization, i.e. measures to address overfitting can accentuate bias.
Holistic Parameteric Reconstruction of Building Models from Point Clouds

Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.
Robust Hypergraph Clustering via Convex Relaxation of Truncated MLE

We study hypergraph clustering in the weighted <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-uniform hypergraph stochastic block model (<inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula><italic>-WHSBM</italic>), where each edge consisting of <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> nodes from the same community has higher expected weight than the edges consisting of nodes from different communities. We propose a new hypergraph clustering algorithm, called <italic>CRTMLE</italic>, and provide its performance guarantee under the <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula><italic>-WHSBM</italic> for general parameter regimes. We show that the proposed method achieves the order-wise optimal or the best existing results for approximately balanced community sizes. Moreover, our results settle the first recovery guarantees for growing number of clusters of unbalanced sizes. Involving theoretical analysis and empirical results, we demonstrate the robustness of our algorithm against the unbalancedness of community sizes or the presence of outlier nodes.
Cooperative orbital moments and edge magnetoresistance in monolayer 
WTe2

We argue that edge electrons in monolayer $\mathrm{W}{\mathrm{Te}}_{2}$ can possess a ``cooperative'' orbital moment (COM) that critically impacts its edge magnetoresistance behavior. Arising from the cooperative action of both Rashba and Ising spin-orbit coupling, COM quickly achieves large magnitudes (of the order of a few Bohr magnetons) even for relatively small spin-orbit coupling strengths. As we explain, such large COM magnitudes arise from an unconventional cooperative spin canting of edge spins when Rashba and Ising spin-orbit coupling act together. Strikingly, COM can compete with spin moments to produce an unusual anisotropic edge magnetoresistance oriented at an oblique angle. In particular, this competition produces a direction along which $\mathbf{B}$ is ineffective at gapping out the edge spectrum, leaving it nearly gapless. As a result, large contrasts in gap sizes appear as $\mathbf{B}$ is rotated, granting a giant anisotropic magnetoresistance as large as several million percent at 10 T and low temperature.
Histological profile of breast and leg muscles of Silkies chickens and of slow-growing Hubbard JA 957 broilers.

: Histological pro (cid:191) le of breast and leg muscles of Silkies chickens and of slow-growing Hubbard JA 957 broilers. The experiment was conducted with 60 slow-growing Hubbard JA 957 broilers and with 60 Silkies chickens reared until the 63 rd day of life, in three replications, 20 birds each, in a closed building on litter. A three--stage feeding program was applied in the rearing period. Feed mixtures used in the experiment did not differ in contents of nutrients. In the (cid:191) rst stage of rearing (till 21 days of life) the chickens were fed a standard starter feed mixture contain-ing: 20.98% of total protein, 2,845 kcal/kg of metabolizable energy, 1.17% of lysine and 0.98% of met. + cys. In the period from 22 to 56 days of life they were receiving a grower type mixture containing: 20.0% of total protein, 2,900 kcal/kg of energy, 1.04% of lysine and 0.88% of met. + + cys. , whereas contents of these components in the (cid:191) nisher type mixture administered till the end of the rearing period were as follows: 18% of protein, 2,965 kcal/kg of EM, 0.90% of lysine and 0.78% of met. + cys. On day 63 of rearing, 12 cockerels and 12 hens were selected random from each group. The birds were slaughtered in a poultry slaughter house, and specimens of their major breast muscle ( m. pectoralis super (cid:191) cialis ) and thigh muscle ( biceps femoris ) were sampled for analyses. The breast and leg muscles of the birds were characterized by diversi (cid:191) ed diameters of muscle (cid:191) bers depending on bird genotype. The greatest diameters were in breast and leg muscles of the slow-growing Hubbard JA 957 broilers, which was con (cid:191) rmed statistically (P (cid:148) 0.01). It was also demonstrated that the mean diameter of muscle (cid:191) bers of Silkies chickens was more than two times smaller compared to the Hubbard JA 957 chickens.
Describing roosts used by forest bats: the importance of microclimate

ABSTRACT Adequate descriptions of roosting habitat are vital to the management and conservation of bats. However, most studies on bat roosting preference report only structural characteristics of roosts and surrounding habitat, and ignore potentially important factors in roost selection. I argue that the current methods for describing the roosting habitat of tree-roosting bats can be improved, and that more emphasis should be placed on designing studies to determine why bats choose particular roosts. Herein, I focus on measuring microclimate in roosts because it universally influences habitat selection. Specifically, roost temperature is easily measured and is likely an important microclimate variable used by bats in roost selection. Variation in structural characteristics of roosts is often assumed to correlate with variation in microclimate of the roost; however, empirical data are too scarce to verify this assumption. I suggest improvements to the current methods of describing roost characteristics and suggest the inclusion of new methods to describe microclimate. In summation, I argue that there are methods of measuring roost characteristics that may be beneficial to use in conjunction with the methods currently being used, and that microclimate should be considered when designing future studies.
Sketch-BERT: Learning Sketch Bidirectional Encoder Representation From Transformers by Self-Supervised Learning of Sketch Gestalt

Previous researches of sketches often considered sketches in pixel format and leveraged CNN based models in the sketch understanding. Fundamentally, a sketch is stored as a sequence of data points, a vector format representation, rather than the photo-realistic image of pixels. SketchRNN studied a generative neural representation for sketches of vector format by Long Short Term Memory networks (LSTM). Unfortunately, the representation learned by SketchRNN is primarily for the generation tasks, rather than the other tasks of recognition and retrieval of sketches. To this end and inspired by the recent BERT model, we present a model of learning Sketch Bidirectional Encoder Representation from Transformer (Sketch-BERT). We generalize BERT to sketch domain, with the novel proposed components and pre-training algorithms, including the newly designed sketch embedding networks, and the self-supervised learning of sketch gestalt. Particularly, towards the pre-training task, we present a novel Sketch Gestalt Model (SGM) to help train the Sketch-BERT. Experimentally, we show that the learned representation of Sketch-BERT can help and improve the performance of the downstream tasks of sketch recognition, sketch retrieval, and sketch gestalt.
On the Choice of Auxiliary Languages for Improved Sequence Tagging

Recent work showed that embeddings from related languages can improve the performance of sequence tagging, even for monolingual models. In this analysis paper, we investigate whether the best auxiliary language can be predicted based on language distances and show that the most related language is not always the best auxiliary language. Further, we show that attention-based meta-embeddings can effectively combine pre-trained embeddings from different languages for sequence tagging and set new state-of-the-art results for part-of-speech tagging in five languages.
A free boundary problem arising from branching Brownian motion with selection

We study a free boundary problem for a parabolic partial differential equation in which the solution is coupled to the moving boundary through an integral constraint. The problem arises as the hydrodynamic limit of an interacting particle system involving branching Brownian motion with selection, the so-called Brownian bees model which is studied in a companion paper. In this paper we prove existence and uniqueness of the solution to the free boundary problem, and we characterise the behaviour of the solution in the large time limit.
Scalable near-infrared graphene plasmonic resonators exhibiting strong non-local and electron quantization effects

Graphene plasmonic resonators have been broadly studied in the terahertz and mid-infrared ranges because of their electrical tunability and large confinement factors which can enable dramatic enhancement of light-matter coupling. In this work, we demonstrate that the characteristic scaling laws of graphene plasmons change for smaller (< 40 nm) plasmonic wavelengths, expanding the operational frequencies of graphene plasmonic resonators into the near-infrared (NIR) and modifying their optical confinement properties. We utilize a novel bottom-up block copolymer lithography method that substantially improves upon top-down methods to create resonators as narrow as 12 nm over centimeter-scale areas. Measurements of these structures reveal that their plasmonic resonances are strongly influenced by non-local and quantum effects, which push their resonant frequency into the NIR (2.2 um), almost double the frequency of previous experimental works. The confinement factors of these resonators, meanwhile, reach 137 +/- 25, amongst the largest reported in literature for an optical cavity. While our findings indicate that the enhancement of some 'forbidden' transitions are an order of magnitude weaker than predicted, the combined NIR response and large confinement of these structures make them an attractive platform to explore ultra-strongly enhanced spontaneous emission.
Development and Quantitative Genetics of Correlation Structure Among Body Parts of Drosophila melanogaster

The relationships between developmental components and correlation structure are explored for 17 head and thorax traits of Drosophila melanogaster. A developmental basis for the patterns of genetic and phenotypic correlation is described and related to information on the growth of various imaginal discs. In general, correlations between traits are positive, reflecting the similarity of cell doubling times of the various imaginal discs and the overlapping times of their growth during the larval instars. Positive correlations between traits provide a mechanism that can constrain radical evolutionary divergence from the fundamental body plan recognized as a fly. Phenotypic and genetic correlation structures are generally consistent with a hypothesis that traits derived from the same imaginal disc are more highly correlated than those derived from different discs. This structure is consistent with observed patterns of morphological divergence within the order Diptera. Furthermore, higher within-disc correlations provide a mechanism for evolutionary fine-tuning of the phenotype of a population of organisms to variable environmental conditions.
Multiscale modelling of replicated nonstationary time series

Within the neurosciences, to observe variability across time in the dynamics of an underlying brain process is neither new nor unexpected. Wavelets are essential in analyzing brain signals because, even within a single trial, brain signals exhibit nonstationary behaviour. However, neurological signals generated within an experiment may also potentially exhibit evolution across trials (replicates). As neurologists consider localised spectra of brain signals to be most informative, here we develop a novel wavelet-based tool capable to formally represent process nonstationarities across both time and replicate dimensions. Specifically, we propose the Replicate Locally Stationary Wavelet (RLSW) process, that captures the potential nonstationary behaviour within and across trials. Estimation using wavelets gives a natural desired time- and replicate-localisation of the process dynamics. We develop the associated spectral estimation framework and establish its asymptotic properties. By means of thorough simulation studies, we demonstrate the theoretical estimator properties hold in practice. A real data investigation into the evolutionary dynamics of the hippocampus and nucleus accumbens during an associative learning experiment, demonstrate the applicability of our proposed methodology, as well as the new insights it provides.