Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpretable Textual Neuron Representations for NLP

Input optimization methods, such as Google Deep Dream, create interpretable representations of neurons for computer vision DNNs. We propose and evaluate ways of transferring this technology to NLP. Our results suggest that gradient ascent with a gumbel softmax layer produces n-gram representations that outperform naive corpus search in terms of target neuron activation. The representations highlight differences in syntax awareness between the language and visual models of the Imaginet architecture.
Functional anatomy of human extraocular muscles during fusional divergence.

We employed magnetic resonance imaging to quantify human extraocular muscle contractility during centered target fusion and fusional divergence repeated with each eye viewing monocularly at 20 cm through 8Δ and at 400 cm through 4Δ base in prism. Contractility, indicated by posterior partial volume (PPV) change, was analyzed in transverse rectus and in medial and lateral superior oblique (SO) muscle compartments and by cross-sectional area change in the inferior oblique (IO). At 20 cm, 3.1 ± 0.5° (SE) diverging eye abduction in 10 subjects was associated with 4.2 ± 1.5% whole lateral rectus (LR) PPV increase ( P < 0.05) and 1.7 ± 1.1% overall medial rectus (MR) PPV decrease attributable to 3.1 ± 1.8% reduction in the superior compartment ( P < 0.025), without change in its inferior compartment or in muscles of the aligned eye. At 400 cm, 2.2 ± 0.5° diverging eye abduction in nine subjects was associated with 6.1 ± 1.3% whole LR PPV increase ( P < 10-5) but no change in MR, with compartmentally similar relaxation in the LR and MR of the aligned eye. Unlike convergence, there were no IO or SO contractile changes for divergence to either target nor any change in rectus pulley positions. Results confirm and extend to proximal divergence the unique role of the superior MR compartment, yet no MR role for far divergence. Corelaxation of aligned eye LR and MR combined with failure of MR relaxation during divergence is consistent with the limited behavioral range of divergence. NEW & NOTEWORTHY Magnetic resonance imaging shows that the lateral rectus muscle must overcome continued contraction by its opponent the medial rectus when humans diverge their visual axes to achieve single, binocular vision. While the upper but not lower compartment of the medial rectus assists by relaxing for near targets, it does not do so when targets are far away. This behavior violates Sherrington's law of reciprocal action of antagonists and conventional assumptions about the ocular motor system.
In silico Prediction and Exploration of Potential Bacteriocin Gene Clusters Within the Bacterial Genus Geobacillus

The thermophilic, endospore-forming genus of Geobacillus has historically been associated with spoilage of canned food. However, in recent years it has become the subject of much attention due its biotechnological potential in areas such as enzyme and biofuel applications. One aspect of this genus that has not been fully explored or realized is its use as a source of novel forms of the ribosomally synthesized antimicrobial peptides known as bacteriocins. To date only two bacteriocins have been fully characterized within this genus, i.e., Geobacillin I and II, with only a small number of others partially characterized. Here we bioinformatically investigate the potential of this genus as a source of novel bacteriocins through the use of the in silico screening software BAGEL3, which scans publically available genomes for potential bacteriocin gene clusters. In this study we examined the association of bacteriocin gene presence with niche and phylogenetic position within the genus. We also identified a number of candidates from multiple bacteriocin classes which may be promising antimicrobial candidates when investigated in vitro in future studies.
Acceleration Speed Optimization of Intelligent EVs in Consideration of Battery Aging

A speed optimization framework is presented to minimize both the battery aging and the energy consumption for intelligent electric vehicles (EVs) during acceleration process. To describe quantitatively the battery capacity loss and to optimize in real-life driving conditions, a control-oriented battery life model is derived. Then, the formulation of the speed optimization during an acceleration process is established and solved by sequential quadratic programming (SQP) algorithm. Several simulations are conducted in the case that an intelligent EV speeds up to the specified speed considering different road slopes. The results during an acceleration process from 0 to 100 km/h within 14 s show that the loss of battery capacity can be reduced by 9.6% while the battery energy consumption is merely increased by 1.6% compared with the strategy that only considers the optimal energy consumption.
Dense Convolutional Binary-Tree Networks for Lung Nodule Classification

This paper investigates the problem of benign or malignant diagnosis of pulmonary nodule with original thoracic computed tomography images, and presents a novel end-to-end deep learning architecture named dense convolutional binary-tree network (DenseBTNet). Besides introducing center-crop operation into the DenseNet, the DenseBTNet splits isolated transition layers of the DenseNet and merges them with dense blocks, then adjusts feature-maps transition mode to compact the model. The DenseBTNet has several compelling advantages: 1) the DenseBTNet not only preserves densely connected mechanism of the DenseNet to extract features of lung nodules in different level, but also further reinforces this mechanism to a level of dense blocks and enriches multi-scale features and 2) The DenseBTNet owns high parameter-efficiency and is lightweight in the scale of parameters as well. Experimental results show that the DenseBTNet largely boosts the performance of the DenseNet and achieves higher accuracies on the task of lung nodule classification in comparison with state-of-the-art approaches.
Design and Implementation of Transformer-Based Multilevel Inverter Topology With Reduced Components

This paper presents a nine-level transformer-based inverter requiring only eight switches. The envisaged structure consists of two standard H-bridges fed from a single dc source. Besides, a single-phase transformer is employed to aid the process of intermediate voltage level generation. An ad-hoc pulsewidth modulation scheme based on boolean logic form equations is developed to derive the gating pulses. An effortless extension of the proposed inverter to a higher number of voltage levels is also achieved by generalizing the switching functions. Furthermore, the superior performance of the proposed topology is demonstrated through a comprehensive cost-based analysis. Finally, the validation of the proposed topology is accomplished through experiments on a down-scale prototype, and the measurement results are included.
A Hybrid Wide Drain Poly-Si FinTFT for RF Application

In this paper, a hybrid wide drain polycrystalline silicon (poly-Si) fin-like thin-film transistor (FinTFT) for radio frequency (RF) applications is fabricated. A high <inline-formula> <tex-math notation="LaTeX">$I_{\mathrm {ON}}/I_{\mathrm {OFF}}$ </tex-math></inline-formula> current ratio (>10<sup>8</sup>) and favorable gate control (SS = 198 mV/dec) are observed, and a high cutoff frequency (<inline-formula> <tex-math notation="LaTeX">$f_{T} =9.55$ </tex-math></inline-formula> GHz @ <inline-formula> <tex-math notation="LaTeX">$V_{\text {DS}}=1$ </tex-math></inline-formula> V and 12.45 GHz @ <inline-formula> <tex-math notation="LaTeX">$V_{\text {DS}}=2$ </tex-math></inline-formula> V) is achieved. The hybrid wide drain poly-Si FinTFT developed in this paper exhibits excellent gate controllability and good high-frequency characteristics as compared with the conventional poly-Si TFT. The effect of the extended length of wide drain on the high-frequency characteristics of poly-Si FinTFT is investigated and analyzed by using the extracted small-signal parameters. Increasing the extended length not only can improve the high-frequency performance which degrade due to additional fringing capacitance in 3-D multigate structure but also can increase the breakdown voltage. This result suggest the possible use of the poly-Si TFT technology for RF integrated circuits and show a great potential to the future system on panel or system on glass.
Enhanced Carrier Density in a MoS2/Si Heterojunction-Based Photodetector by Inverse Auger Process

When exploring the charge transport in the MoS<sub>2</sub>/Si heterojunction, it is imperative to understand the contribution of different carrier interactions and scattering mechanisms in the conduction process. Here, we have demonstrated an ultrasensitive photodetector based on MoS<sub>2</sub>/Si (for both p-type and n-type Si) van der Walls (vdW) heterojunction, which provides a high photoresponsivity of greater than 10<sup>3</sup> A/W along with a large detectivity of ≈ 10<sup>12</sup> Jones (Jones = <inline-formula> <tex-math notation="LaTeX">$\text{cm} \cdot \text{Hz}^{ {1/2}} \cdot \text{W}^{-1}$ </tex-math></inline-formula>). This high photoresponsivity is attributed to the high absorption rate of incident photons and large current carrying capacity of a few layer MoS<sub>2</sub>. The tuning of barrier height at the MoS<sub>2</sub>/n-Si interface with photoexcitation of different wavelengths was interpreted by using Bardeen’s model. The increase in carrier density due to the photon energy results in a reduction of barrier height at the heterojunction. Because of increase in carrier density with wavelength, it was observed that the inverse Auger process prevails at higher wavelength for the relaxation of photoinduced charge carriers at the MoS<sub>2</sub>/Si interface over other scattering mechanisms. Our obtained results unveil the great potential of the MoS<sub>2</sub>/Si vdW heterojunction that may pave the way for new generation photodetectors.
Modeling of Electron Devices Based on 2-D Materials

The advent of graphene and related 2-D materials has attracted the interest of the electron device research community in the past 14 years. The possibility to boost the transistor performance and the prospects to build novel device concepts with 2-D materials and their heterostructures has awakened a strong experimental interest that requires continuous support from modeling. In this paper, we review the state of the art in the simulation of electron devices based on 2-D materials. We outline the main methods to model the electronic bandstructure and to study electron transport, classifying them in terms of accuracy and computational cost. We briefly discuss, how they can be combined in a multiscale approach to provide a quantitative understanding of the mechanisms determining the operation of electron devices, and we examine the application of these methods to different families of 2-D materials. Finally, we shortly analyze the main open challenges of modeling 2-D-based electron devices.
Improved methods to capture the total societal benefits of zoonotic disease control: Demonstrating the cost-effectiveness of an integrated control programme for Taenia solium, soil transmitted helminths and classical swine fever in northern Lao PDR

Background Control and elimination of zoonotic diseases requires robust information about their effect on both human and livestock health in order to enable policy formulation and the allocation of resources. This study aimed to evaluate the cost-effectiveness of controlling Taenia solium taeniasis/cysticercosis in both humans and pigs, and soil-transmitted helminths (STH) in humans by integrating their control to on-going human and animal health control programmes in northern Lao People’s Democratic Republic. Method A cross-sectional study was carried out in 49 households, focusing on the prevalence of T. solium taenias/cysticercosis and soil transmitted helminths before and after a twelve month intervention. The village data was collected using a semi-structured questionnaire through a door-to-door survey. The village data was then projected to the wider northern Lao PDR population using stochastic modelling and cost-effectiveness ratio (after aggregating the net cost to capture both human and animal health parameters) and GDP per capita as a threshold, to determine the cost-effectiveness of the integrated control of T. solium taeniasis/ cysticercosis and STH, assuming linear scaling out of the intervention. The zoonotic DALY (zDALY) approach was also used as an alternative method of estimating the cost-effectiveness ratio of controlling T. solium taeniasis/cysticercosis in humans and pigs. Findings Using cost-effectiveness analysis after aggregating the net cost and control of T. solium taeniasis/cysticercosis alone as the base case, the study found that simultaneous control of T. solium taeniasis/cysticercosis in humans and pigs, STH in humans and Classical Swine Fever (CSF) in pigs was USD 14 per DALY averted and USD 234 per zDALY averted using zDALY method hence considered highly cost-effective whereas controlling T. solium taeniasis/cysticercosis without incorporating STH and CSF was the least cost-effective (USD 3,672 per DALY averted). Additionally, the cost-effectiveness of controlling T. solium taeniasis/cysticercosis in people and pigs using zDALY as an alternative method was USD 3,662 per zDALY averted which was quite close to our findings using the aggregate net cost method. Conclusion The study showed that control of T. solium taeniasis/cysticercosis alone in humans and pigs is not cost-effective in northern Lao PDR whereas control of STH is. Consequently, integrating T. solium taeniasis/cysticercosis control with other cost-effective programmes such as STH and CSF markedly improved the cost-effectiveness of the intervention. This is especially important in low resource countries where control of zoonotic neglected tropical diseases could be integrated with the human and animal health sectors to optimize use of the limited resources. Trial registration Australia New Zealand Clinical Trials Registry (ANZCTR) ACTRN12614001067662.
Ground Plane Alterations for Design of High-Isolation Compact Wideband MIMO Antenna

In this paper, a simple technique for improving element isolation in wideband multiple-input multiple-output (MIMO) antenna is investigated. We consider n-section rectangular slits below the feed line and analyze the effects of the number of sections on achievable isolation. The effect of the slits is indirect by improving the impedance matching, which creates a room for isolation enhancement through rigorous optimization of all antenna parameters. The results obtained for an exemplary antenna structure indicate the advantages of increasing the slit complexity as well as a saturation effect beyond <inline-formula> <tex-math notation="LaTeX">$n= 3$ </tex-math></inline-formula>. Using these considerations, a compact MIMO antenna is developed and optimized to operate in the ultrawideband (UWB) (3.1–10.6 GHz) frequency range. The final design features <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{11}\vert \le -10$ </tex-math></inline-formula> dB and isolation <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{21}\vert \le -20$ </tex-math></inline-formula> dB in the entire UWB range, as well as small dimensions of only <inline-formula> <tex-math notation="LaTeX">$25\times32$ </tex-math></inline-formula> mm<sup>2</sup>. Excellent performance figures, including envelop coefficient correlation (< 0.005), diversity gain (> 9.99 dB), and total efficiency (>80% by average), demonstrate that the considered structure is suitable for practical applications and competitive to the state-of-the-art antennas reported in the literature. Experimental validation of the design is also provided.
Sublinear Time Low-Rank Approximation of Distance Matrices

Let Ƥ = {p1, p2, . pn} and Q = {q1, q2 . qm} be two point sets in an arbitrary metric space. Let A represent the m × n pairwise distance matrix with Ai.j = d(pi, qj). Such distance matrices are commonly computed in software packages and have applications to learning image manifolds, handwriting recognition, and multi-dimensional unfolding, among other things. In an attempt to reduce their description size, we study low rank approximation of such matrices. Our main result is to show that for any underlying distance metric d, it is possible to achieve an additive error low rank approximation in sublinear time. We note that it is provably impossible to achieve such a guarantee in sublinear time for arbitrary matrices A, and our proof exploits special properties of distance matrices. We develop a recursive algorithm based on additive projection-cost preserving sampling. We then show that in general, relative error approximation in sublinear time is impossible for distance matrices, even if one allows for bicriteria solutions. Additionally, we show that if Ƥ = Q and d is the squared Euclidean distance, which is not a metric but rather the square of a metric, then a relative error bicriteria solution can be found in sublinear time. Finally, we empirically compare our algorithm with the singular value decomposition (SVD) and input sparsity time algorithms. Our algorithm is several hundred times faster than the SVD, and about 8-20 times faster than input sparsity methods on real-world and and synthetic datasets of size 108. Accuracy-wise, our algorithm is only slightly worse than that of the SVD (optimal) and input-sparsity time algorithms.
A Fast and Simple Model in Practice for Ranking

Recent work has shown success in using neural network as ranking model in question answering(QA). While the precision of these models has increased significantly over time, most models need long training time, which leads to it cannot be used effectively in practical applications. In this paper, we propose a simple model to accelerate training speed with noise-contrastive estimation. This model mainly inspired by an architecture based entirely on convolutional neural networks and noise-contrastive estimation. We investigate the behavior of the model on the financial dataset which we use in practice and show that it reduces the training times by more than an order of magnitude without affecting the quality of the resulting models. The model is also more efficient and more stable than importance sampling because it requires fewer noise samples to perform well.
Novel Methods Generated by Genetic Programming for the Guillotine-Cutting Problem

New constructive algorithms for the two-dimensional guillotine-cutting problem are presented. The algorithms were produced from elemental algorithmic components using evolutionary computation. A subset of the components was selected from a previously existing constructive algorithm. The algorithms’ evolution and testing process used a set of 46 instances from the literature. The structure of three new algorithms is described, and the results are compared with those of an existing constructive algorithm for the problem. Several of the new algorithms are competitive with respect to a state-of-the-art constructive algorithm. A subset of novel instructions, which are responsible for the majority of the new algorithms’ good performances, has also been found.
Rank‐based inference for covariate and group effects in clustered data in presence of informative intra‐cluster group size

There have been numerous attempts to extend the Wilcoxon rank‐sum test to clustered data. Recently, one such rank‐sum test (Dutta & Datta, 2016, Biometrics 72, 432‐440) was developed to compare the group‐specific marginal distributions of outcomes in clustered data where the conditional distributions of outcomes depend on the number of observations from that group in a given cluster, a phenomenon referred to as informative intra‐cluster group (ICG) size. However, comparison of group‐specific marginal distributions may not be sufficient in presence of some potentially useful covariables that are observed in the study. In addition, not accounting for the effect of these covariates can lead to biased and misleading inference for the group comparisons. Thus, the purpose of this article is twofold. First, we develop a method to estimate the covariate effects using rank‐based weighted estimating equations that are appropriate when the ICG size is informative. Second, we construct an aligned rank‐sum test based on the covariate adjusted outcomes. Asymptotic distributions of the R‐estimators and the test statistic are provided. Through simulation studies, we show the importance of selecting proper weights in constructing the estimating equations when informativeness is present through the cluster or ICG sizes. We also demonstrate the superiority and the robustness of our method in comparison to regular parametric linear mixed models in clustered data. We apply our method to analyze different real‐life data sets including a data on birthweights of rat pups in different litters and a dental data on tooth attachment loss.
Optimized ESS Operation for Peak Shaving based on Probabilistic Load Prediction

With an increase in the mount of photovoltaic (PV) generators in a distribution network, the network requires flexible energy storage systems (ESSs) to accept additional PVs and prevent power overflow. This paper aims to find the best ESS operation schedule to minimize the peak load on the feeder. The load on the feeder needs to be predicted to determine the optimized ESS schedule; however, one deterministically predicted scenario is not reliable because the prediction contains certain error. In this paper, the forecasting error is reflected when the ESS schedule is optimized by using probabilistic load prediction. The proposed method doesn’t provide only one scenario but shows various scenarios with probability of peak load to distributed network operators (DNOs) as a result of ESS operation. The DNOs can choose the best scenario based on their principles. In the simulation, based on the probabilistic load prediction, the peak load is shaved and three scenarios—optimistic, realistic, and pessimistic—are shown.
Fake Accounts Detection on Twitter Using Blacklist

Social networking sites such as Twitter, Facebook, Weibo etc. are extremely mainstream today. Also, the greater part of the malicious users utilize these sites to persuade legitimate users for different purposes, for example, to promote their products item, to enter their spam links, to stigmatize other persons and so forth. An ever increasing number of users are utilized these social networking sites and fake accounts on these destinations are turned into a major issue. In this paper, fake accounts are detected using blacklist instead of traditional spam words list. Blacklist is created by using topic modeling approach and keyword extraction approach. We conduct an evaluation experiment with not only 1KS - 10KN dataset but also Social Honeypot dataset. The accuracy of the traditional spam words list based approach and our blacklist based approach are compared. Decorate, a meta-learner classifier is applied for classifying fake accounts on Twitter from legitimate accounts. Our approach achieves 95.4% accuracy and true positive rate is 0.95.