Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of the flow rate of the shielding gas on the species fluxes in the coaxial double-tube helium atmospheric pressure plasma jet

The effect of ambient air as an unavoidable problem for atmospheric pressure plasma jet (APPJ) applications has attracted a lot of interest, especially when the specific scenarios are highly sensitive to ambient species such as the biomedical process. The coaxial double-tube device is a promising method for controlling the ambient species into the jet effluent and thus the chemical properties of the jet effluent. In this work, the discharge characteristics and plasma chemistry of the coaxial double-tube helium APPJ at different shielding gas (SG) flow rates are studied numerically. An experiment on optical images of the discharge is conducted to illustratively validate the variation of the (main) discharge channel widths in the model as the SG flow rate varies. The results illustrate that the discharge is enhanced at the high flow rate, while it shows the weaker discharge behavior at the low flow rate as well as that without SG. The analysis of the dielectric plate surface indicates that the species fluxes to the dielectric plate significantly increase with the increases in the flow rate, which can be attributed to the wider (main) discharge channel. Moreover, to further explore the impact of the SG on the effluent chemistry, the ions fluxes on the surfaces of the main discharge channel and the discharge channel are distinguished and discussed. The analysis shows the great difference in the ions fluxes affected by the flow rate between the two discharge channels. In summary, advancing the knowledge that the flow rate of the SG has an impact on the discharge behavior, this study further reveals that different discharge positions greatly influence the production of nitrogen/oxygen species. This work enables the previously elusive account of the effect of SG and may open new opportunities for the further application of coaxial double-tube APPJ.
Tree Species Diversity and Conservation Status of Keniam Forest, Taman Negara, Pahang

This study was conducted to quantify the community structure with regards to tree species composition and species diversity of tree communities at Kuala Keniam. Five study plots of 25 m x 20 m each were established randomly covering a total area of 0.25 ha. In each plot, all trees with diameters at breast height of 5 cm and above were tagged, measured and identified. Voucher specimens were then taken for further verification purposes. The floristic composition of Kuala Keniam consists of 244 individuals represented by 142 species, 95 genera and 36 families. Euphorbiaceae was the most speciose family with 20 species and nine genera. Elateriospermum tapos (Euphorbiaceae) was recorded as the most dominant species with 16 trees. Kuala Keniam recorded Shannon-Weiner Diversity index of H’=3.01 and Margalef’s Richness Index with DMG=22.78. Low evenness index of J’ = 0.14 portrayed the unequal distribution of the tree species in the forest. Total tree biomass was estimated at 955.02 t/ha, represented by 815.43 t/ha of above ground biomass and 139.59 t/ha of below ground biomass. Carbon stock was estimated at 448.86 t/ha, contributed by, 383.25 t/ha of above ground carbon and 65.61 t/ha of below ground carbon.
A novel MEMS inertial switch with frictional electrode

This paper presents a frictional MEMS inertial switch with aiming to solve the problems of short contact time and bounce. According to contact principle in MEMS inertial switch, a new enhanced contact method by friction is proposed and a frictional switch is designed. To compare the frictional switch with common rigid switch and flexible switch, three switches were fabricated by micro electroforming technology and tested by a standard dropping hammer system. The tested results show that the contact time of rigid switch, flexible switch and frictional switch is 13 μs, 85 μs and 620 μs, respectively. Both rigid switch and flexible switch have bounce problem, while frictional switch is closed stably without bounce phenomenon. Frictional MEMS inertial switch has great advantages in enhancing contact.
A Contribution to Trees and Shrubs Checklist in Kuala Keniam at Taman Negara, Pahang, Malaysia

The forested area around Kuala Keniam is a part of Taman Negara (Pahang) and is categorized as a primary lowland dipterocarp, riparian fringe and limestone forests. A study of flora that focuses on the trees and shrubs was conducted during the scientific expedition in September 2020, organised by Universiti Teknologi MARA (UiTM). The results identified as many as 187 taxa of trees and shrubs consisting of 124 genera and 49 plant families from the lowland dipterocarp forest. A total of nine endemic taxa to Peninsular Malaysia were also recorded namely Aporosa globifera, Baccaurea pyriformis, Cinnamomum mollissimum, Enicosanthum fuscum, Lithocarpus curtisii, Mallotus griffithianus, M. penangensis, Ryparosa fasciculata and Schoutenia kunstleri. Another three taxa viz. Elaeocarpus obtusatus ssp. apiculatus, Popowia tomentosa and Sterculia rubiginosa var. setistipula were considered as the new additional record for Pahang state. It is hoped that the information from this study can be used as references to help other researchers as well as stakeholders in ecotourism management and forest conservation plan.
Specific Targeted Research Projects SOLDER Spectrum OverLay through aggregation of heterogeneous DispERsed Bands – Carrier Aggregation over HetNets and h-RATs: and

This document presents a state-of-the-art survey on aggregation technologies, defines the application scenarios and use cases that the SOLDER project is considering, and discusses some of the challenges and opportunities for novel research that the project aims to address through those application scenarios and use cases. It is noted that the application scenarios and use cases may be adjusted somewhat throughout the duration of the project, if it transpires that it is beneficial to do so.
Energy efficiency initiatives in a campus building

Higher Learning Institution (HLI)is a place for ademicians, students and its administration staff in a campus. HLI is among the favoured targeted locations to embrace for sustainability and energy conservation solutions due to the campus operations and activities that have significant energy consumption depending on the size of the campus including its buildings and infrastructures. Basically, there are several alternatives for which HLI is able to offer huge opportunities in retrofitting initiatives to concern on energy efficiency. This paper presents a study leads to the identification of possible retrofit initiatives and the criteria affecting the retrofitting of M50 building in Faculty of Civil Engineering towards zero energy. A questionnaire survey was distributed to occupants of M50 building in order to identify the preferred energy efficiency technologies to be implemented. The result was analyzed with Relative Importance Index (RII) and it shows that solarphotovoltaic, green roof and variable air volume were among the most preferable technologies. The relevant criteria for each energy efficiency initiatives being prioritized and ranked based on Factor Analysis (FA. The purpose of using the selected approach and ranking is to investigate the significance of each criteria. The performing result assists as a step for energy efficiency initiatives in a campus building, thus minimizing the energy impact to environment from its operations and activities. The study outcome is also indirectly fulfilling an example case for sustainable teaching, learning and research process.
A Computational Acquisition Model for Multimodal Word Categorization

Recent advances in self-supervised modeling of text and images open new opportunities for computational models of child language acquisition, which is believed to rely heavily on cross-modal signals. However, prior studies has been limited by their reliance on vision models trained on large image datasets annotated with a pre-defined set of depicted object categories. This is (a) not faithful to the information children receive and (b) prohibits the evaluation of such models with respect to category learning tasks, due to the pre-imposed category structure. We address this gap, and present a cognitively-inspired, multimodal acquisition model, trained from image-caption pairs on naturalistic data using cross-modal self-supervision. We show that the model learns word categories and object recognition abilities, and presents trends reminiscent of ones reported in the developmental literature.
Mass and Age Determination of the LAMOST Data with Different Machine-learning Methods

We present a catalog of 948,216 stars with mass labels and a catalog of 163,105 red clump (RC) stars with mass and age labels simultaneously. The training data set is crossmatched from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR5, and high-resolution asteroseismology data, mass, and age are predicted by the random forest (RF) method or a convex-hull algorithm. The stellar parameters with a high correlation with mass and age are extracted and the test data set shows that the median relative error of the prediction model for the mass of the large sample is 3%, and for the mass and age of RC stars is 4% and 7%. We also compare the predicted age of RC stars with recent works and find that the final uncertainty of the RC sample could reach 18% for age and 9% for mass; meanwhile, the final precision of the mass for the large sample with different types of stars could reach 13% without considering systematics. All of this implies that this method could be widely used in the future. Moreover, we explore the performance of different machine-learning methods for our sample, including Bayesian linear regression and the gradient-boosting decision tree (GBDT), multilayer perceptron, multiple linear regression, RF, and support vector regression methods. Finally, we find that the performance of a nonlinear model is generally better than that of a linear model, and the GBDT and RF methods are relatively better.
Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers

Parkinson's disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including number and speed of room to room transitions, provides a proxy outcome which represents mobility and could be used as a digital biomarker to quantify how mobility changes as this disease progresses. We use data collected from 10 people with Parkinson's, and 10 controls, each of whom lived for five days in a smart home with various sensors. In order to more effectively localise them indoors, we propose a transformer-based approach utilizing two data modalities, Received Signal Strength Indicator (RSSI) and accelerometer data from wearable devices, which provide complementary views of movement. Our approach makes asymmetric and dynamic correlations by a) learning temporal correlations at different scales and levels, and b) utilizing various gating mechanisms to select relevant features within modality and suppress unnecessary modalities. On a dataset with real patients, we demonstrate that our proposed method gives an average accuracy of 89.9%, outperforming competitors. We also show that our model is able to better predict in-home mobility for people with Parkinson's with an average offset of 1.13 seconds to ground truth.
3-Qubit Circular Quantum Convolution Computation using Fourier Transform with Illustrative Examples

In this work, we describe examples for calculating the 1-D circular convolution of signals represented by 3-qubit superpositions. The case is considered, when the discrete Fourier transform of one of the signals is known and calculated in advance and only the QFT of another signal is calculated. The frequency characteristics of many linear time-invariant systems and filters are well known. Therefore, the considered method of convolution can be used for these systems in quantum computation. The ideal low pass and high pass filters are considered and quantum schemes for convolution are presented. The method of the Fourier transform is used with one addition qubit to prepare the quantum superposition for the inverse quantum Fourier transform.
TaDeR: A New Task Dependency Recommendation for Project Management Platform

Many startups and companies worldwide have been using project management software and tools to monitor, track and manage their projects. For software projects, the number of tasks from the beginning to the end is quite a large number that sometimes takes a lot of time and effort to search and link the current task to a group of previous ones for further references. This paper proposes an efficient task dependency recommendation algorithm to suggest tasks dependent on a given task that the user has just created. We present an efficient feature engineering step and construct a deep neural network to this aim. We performed extensive experiments on two different large projects (MDLSITE from moodle.org and FLUME from apache.org) to find the best features in 28 combinations of features and the best performance model using two embedding methods (GloVe and FastText). We consider three types of models (GRU, CNN, LSTM) using Accuracy@K, MRR@K, and Recall@K (where K = 1, 2, 3, and 5) and baseline models using traditional methods: TF-IDF with various matching score calculating such as cosine similarity, Euclidean distance, Manhattan distance, and Chebyshev distance. After many experiments, the GloVe Embedding and CNN model reached the best result in our dataset, so we chose this model as our proposed method. In addition, adding the time filter in the post-processing step can significantly improve the recommendation system's performance. The experimental results show that our proposed method can reach 0.2335 in Accuracy@1 and MRR@1 and 0.2011 in Recall@1 of dataset FLUME. With the MDLSITE dataset, we obtained 0.1258 in Accuracy@1 and MRR@1 and 0.1141 in Recall@1. In the top 5, our model reached 0.3040 in Accuracy@5, 0.2563 MRR@5, and 0.2651 Recall@5 in FLUME. In the MDLSITE dataset, our model got 0.5270 Accuracy@5, 0.2689 MRR@5, and 0.2651 Recall@5.
Ensemble Clustering via Co-association Matrix Self-enhancement

Ensemble clustering integrates a set of base clustering results to generate a stronger one. Existing methods usually rely on a co-association (CA) matrix that measures how many times two samples are grouped into the same cluster according to the base clusterings to achieve ensemble clustering. However, when the constructed CA matrix is of low quality, the performance will degrade. In this article, we propose a simple, yet effective CA matrix self-enhancement framework that can improve the CA matrix to achieve better clustering performance. Specifically, we first extract the high-confidence (HC) information from the base clusterings to form a sparse HC matrix. By propagating the highly reliable information of the HC matrix to the CA matrix and complementing the HC matrix according to the CA matrix simultaneously, the proposed method generates an enhanced CA matrix for better clustering. Technically, the proposed model is formulated as a symmetric constrained convex optimization problem, which is efficiently solved by an alternating iterative algorithm with convergence and global optimum theoretically guaranteed. Extensive experimental comparisons with 12 state-of-the-art methods on ten benchmark datasets substantiate the effectiveness, flexibility, and efficiency of the proposed model in ensemble clustering. The codes and datasets can be downloaded at https://github.com/Siritao/EC-CMS.
Distinction Maximization Loss: Efficiently Improving Classification Accuracy, Uncertainty Estimation, and Out-of-Distribution Detection Simply Replacing the Loss and Calibrating

Building robust deterministic neural networks remains a challenge. On the one hand, some approaches improve out-of-distribution detection at the cost of reducing classiﬁcation accuracy in some situations. On the other hand, some methods simultaneously increase classiﬁcation accuracy, uncertainty estimation, and out-of-distribution detection at the expense of reducing the inference efﬁciency and requiring training the same model many times to tune hyperparameters. In this paper, we propose training deterministic neural networks using our DisMax loss, which works as a drop-in replacement for the usual SoftMax loss (i.e., the combination of the linear output layer, the SoftMax activation, and the cross-entropy loss). Starting from the IsoMax+ loss, we create each logit based on the distances to all prototypes rather than just the one associated with the correct class. We also introduce a mechanism to combine images to construct what we call fractional probability regularization. Moreover, we present a fast way to calibrate the network after training. Finally, we propose a composite score to perform out-of-distribution detection. Our experiments show that DisMax usually outperforms current approaches simultaneously in classiﬁcation accuracy, uncertainty estimation, and out-of-distribution detection while maintaining deterministic neural network inference efﬁciency and avoiding training the same model repetitively for hyperparameter tuning. The code to reproduce the results is available. 1
Deep morphological recognition of kidney stones using intra-operative endoscopic digital videos

Objective. To assess the performance and added value of processing complete digital endoscopic video sequences for the automatic recognition of stone morphological features during a standard-of-care intra-operative session.Approach. A computer-aided video classifier was developed to predict in-situ the morphology of stone using an intra-operative digital endoscopic video acquired in a clinical setting. Using dedicated artificial intelligence (AI) networks, the proposed pipeline selects adequate frames in steady sequences of the video, ensures the presence of (potentially fragmented) stones and predicts the stone morphologies on a frame-by-frame basis. The automatic endoscopic stone recognition (A-ESR) is subsequently carried out by mixing all collected morphological observations.Main results. The proposed technique was evaluated on pure (i.e. include one morphology) and mixed (i.e. include at least two morphologies) stones involving ‘Ia/Calcium Oxalate Monohydrate’ (COM), ‘IIb/Calcium Oxalate Dihydrate’ (COD) and ‘IIIb/Uric Acid’ (UA) morphologies. The gold standard ESR was provided by a trained endo-urologist and confirmed by microscopy and infra-red spectroscopy. For the AI-training, 585 static images were collected (349 and 236 observations of stone surface and section, respectively) and used. Using the proposed video classifier, 71 digital endoscopic videos were analyzed: 50 exhibited only one morphological type and 21 displayed two. Taken together, both pure and mixed stone types yielded a mean diagnostic performances as follows: balanced accuracy = [88 ± 6] (min = 81)%, sensitivity = [80 ± 13] (min = 69)%, specificity = [95 ± 2] (min = 92)%, precision = [78 ± 12] (min = 62)% and F1-score = [78 ± 7] (min = 69)%. Significance. These results demonstrate that AI applied on digital endoscopic video sequences is a promising tool for collecting morphological information during the time-course of the stone fragmentation process without resorting to any human intervention for stone delineation or the selection of adequate steady frames.
High frequency tunable grounded and floating incremental/ decremental meminductor emulator and its application as Amplitude Modulator

In this paper, a new design has been proposed for the realization of grounded and floating meminductor emulators built with two OTAs and two second-generation current conveyors. The proposed emulators can be configured in both incremental and decremental topology. This paper also proposes the application of meminductor as Amplitude Modulator (AM). The proposed circuits and its application claim that the circuit is much simpler in design and can be utilized in both topologies. The performance of all the proposed circuits has been verified with Cadence Virtuoso Spectre CMOS 180 nm. Furthermore, post-layout simulations and its comparison along with non-ideal and Monte Carlo analysis have been carried out in detail.
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue

Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue understanding encompasses many diverse tasks, yet task transfer has not been thoroughly studied in conversational AI. This work explores conversational task transfer by introducing FETA: a benchmark for FEw-sample TAsk transfer in open-domain dialogue.FETA contains two underlying sets of conversations upon which there are 10 and 7 tasks annotated, enabling the study of intra-dataset task transfer; task transfer without domain adaptation. We utilize three popular language models and three learning algorithms to analyze the transferability between 132 source-target task pairs and create a baseline for future work.We run experiments in the single- and multi-source settings and report valuable findings, e.g., most performance trends are model-specific, and span extraction and multiple-choice tasks benefit the most from task transfer.In addition to task transfer, FETA can be a valuable resource for future research into the efficiency and generalizability of pre-training datasets and model architectures, as well as for learning settings such as continual and multitask learning.
Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language

To solve difficult tasks, humans ask questions to acquire knowledge from external sources. In contrast, classical reinforcement learning agents lack such an ability and often resort to exploratory behavior. This is exacerbated as few present-day environments support querying for knowledge. In order to study how agents can be taught to query external knowledge via language, we first introduce two new environments: the grid-world-based Q-BabyAI and the text-based Q-TextWorld. In addition to physical interactions, an agent can query an external knowledge source specialized for these environments to gather information. Second, we propose the"Asking for Knowledge"(AFK) agent, which learns to generate language commands to query for meaningful knowledge that helps solve the tasks. AFK leverages a non-parametric memory, a pointer mechanism and an episodic exploration bonus to tackle (1) irrelevant information, (2) a large query language space, (3) delayed reward for making meaningful queries. Extensive experiments demonstrate that the AFK agent outperforms recent baselines on the challenging Q-BabyAI and Q-TextWorld environments.
Beyond Hard-Core Bosons in Transmon Arrays

Arrays of transmons have proven to be a viable medium for quantum information science and quantum simulations. Despite their widespread popularity as qubit arrays, there remains yet untapped potential beyond the two-level approximation or, equivalently, the hard-core boson model. With the higher excited levels included, coupled transmons naturally realize the attractive Bose-Hubbard model. The dynamics of the model has been difficult to study due to unfavorable scaling of the dimensionality of the Hilbert space with the system size. In this work, we present a framework for describing the effective unitary dynamics of highly-excited states of coupled transmons based on high-order degenerate perturbation theory. This allows us to describe various collective phenomena -- such as bosons stacked onto a single site behaving as a single particle, edge-localization, and effective longer-range interactions -- in a unified, compact and accurate manner. While our examples deal with one-dimensional chains of transmons for the sake of clarity, the theory can be readily applied to more general geometries.