Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometry Wall: An Embodied Gesture-based Game for Supporting Spatial Ability

Research has shown that interaction design based on embodied cognition has a significant effect on the understanding of abstract concepts. Spatial ability as one of the core literacies in mathematics education can be difficult to train. On this basis, we have designed and developed a gesture-based spatial skills game training system, which allows students to manipulate objects synchronously with gestures to complete game tasks, thereby practicing spatial skills. We then invited 20 students to conduct a pre-experiment and recorded the changes in spatial ability, operational data during the learning process, and the acceptance of the technology through questionnaires and system logs. The results showed significant improvements in all dimensions of spatial ability through this game training system, with two processual characteristic sequences reflecting the status of problem-solving strategies used by participants with different styles. In the technology acceptance survey, there were some issues with ease of use, but overall satisfaction with the game system as a whole was high.
Analysis of Self-esteem on Students' Performance in Online Programming Competition

This paper will look at the effect of self-esteem on students' performance in online programming competitions. The competition was conducted in a hackerrank platform with 4 rounds in total. On each round, there was a set of general questions such as age, gender, etc., but also self-esteem related questions to evaluate how much self-esteem a person has. Correlation between self-esteem and results of each round on average was calculated. The average obtained correlation was around -0.11. Based on the results, the conclusion was that the correlation is slightly low and negative which claims that too high self-esteem can result in low results in online programming competitions across students.
The Perfect Match: Selecting Approximate Multipliers for Energy-Efficient Neural Network Inference

Reconfigurable approximate multipliers have been proposed as a way to improve the energy efficiency of neural network inference. However, selecting the optimal combination of approximate modes is a challenging problem due to the tradeoff between energy savings and accuracy loss. In this paper, we propose a methodology for selecting the best triad of approximate multipliers to form a reconfigurable approximate multiplier that can satisfy a maximum accuracy drop threshold and achieve the highest possible energy savings. We use formal methods to produce a Pareto-front of solutions that satisfy the accuracy constraint and maximize energy savings. Experimental results show that our methodology can achieve significant gains in energy with negligible drops in accuracy when compared to the baseline.
Estimating the Associated Burden of Illness and Healthcare Utilization of Newly Diagnosed Patients Aged ≥65 with Mantle Cell Lymphoma (MCL) in Ontario, Canada

Background: With the emergence of therapies for mantle cell lymphoma (MCL), understanding the treatment patterns and burden of illness among older patients with MCL in Canada is essential to inform decision making. Methods: A retrospective study using administrative data matched individuals aged ≥65 who were newly diagnosed with MCL between 1 January 2013 and 31 December 2016 with general population controls. Cases were followed for up to 3 years in order to assess healthcare resource utilization (HCRU), healthcare costs, time to next treatment or death (TTNTD), and overall survival (OS); all were stratified according to first-line treatment. Results: This study matched 159 MCL patients to 636 controls. Direct healthcare costs were highest among MCL patients in the first year following diagnosis (Y1: CAD 77,555 ± 40,789), decreased subsequently (Y2: CAD 40,093 ± 28,720; Y3: CAD 36,059 ± 36,303), and were consistently higher than the costs for controls. The 3-year OS after MCL diagnosis was 68.6%, with patients receiving bendamustine + rituximab (BR) experiencing a significantly higher OS compared to patients treated with other regimens (72.4% vs. 55.6%, p = 0.041). Approximately 40.9% of MCL patients initiated a second-line therapy or died within 3 years. Conclusion: Newly diagnosed MCL presents a substantial burden to the healthcare system, with almost half of all patients progressing to a second-line therapy or death within 3 years.
Relative performance of AUSGeoid09 in mountainous terrain

Abstract The Australian Height Datum (AHD) is the national vertical datum for Australia, and AUSGeoid09 is the latest quasigeoid model used to compute (normalorthometric) AHD heights from Global Navigation Satellite System (GNSS) derived ellipsoidal heights. While previous studies have evaluated the AUSGeoid09 model across Australia, such studies have generally not focused on mountainous terrain. This paper investigates the performance of AUSGeoid09 in a relative sense in the MidHunter and Snowy Mountains regions of New South Wales, from a user’s perspective. Relative (i.e. height difference between two points) comparisons were undertaken between AUSGeoid09-derived heights and officialAHDheights. The performance of AUSGeoid09 was compared to its predecessor AUSGeoid98. In both study areas, an overall improvement was evident when applying AUSGeoid09 to compute AHD height differences. AUSGeoid09 generally provided AHD height differences at the ±0.05mto ±0.09m level (1 sigma) and substantially increased the percentage of GNSS-derived height differences meeting 3rd order differential levelling specifications. This is a very encouraging result, considering the difficulties of spirit levelling in mountainous terrain and the increasing popularity of GNSS-based height transfer in practice.
Measurement of area by counting

Summary Through the use of a zone mapping transformation, geometrical probability is applied to the problem of measuring the area of a planar figure by counting the points covered when the figure is placed at random on a regular point lattice. An expression for the variance σ 2 is obtained as a function of radius for the case of a circle. This expression is equivalent to a more general formula for σ 2 derived earlier by Kendall and Rankin using analytical methods. Computer calculations of σ are given for two special cases and the results are compared with those predicted by alternative asymptotic formulae also due to Kendall and Rankin. Some examples of the use of zone mapping to treat asymmetric figures, not subject to analytical methods, are described.
Cold Calls to Enhance Class Participation and Student Engagement

The question whether cold calls increase student engagement in the classroom has not been conclusively answered in the literature. This study describes the automated system to implement unbiased, randomized cold calling by posing a question, allowing all students to think first and then calling on a particular student to respond. Since we already have a measure of the level of student engagement as the self-reported class-participation entries from the students, its correlation to cold calling is also further studied. The results show that there is a statistically significant increase in the class participation reported, and therefore in student engagement, in the course for which the cold-calling system was implemented. Finally, along with the conclusions, the future plans and the limitations of this study are explained.
Cramer-Rao Lower Bounds for the Task of Joint Estimation of Signal Initial Phase and AoA in Multi-Element Antenna Arrays

In this paper, we thoroughly investigate the task of joint estimation of signal initial phase and angle-of-arrive (AoA) in multi-element antenna arrays (AA). The accurate estimation of the initial phase of the signal with simultaneous estimation of the AoA is very important and highly relevant for solving mobile objects precise positioning in 5G systems. Analytical expressions for Cramer-Rao lower bounds for the estimated signal parameters under various a priori assumptions are found and effective estimation algorithm is proposed. It is shown that the accuracy of the initial phase estimate, if it is unknown, strongly depends on the choice of the antenna element in which it is estimated, and vice versa, when the initial phase of the signal is known a priori, the choice of the base element of the AA has a significant impact on the accuracy of estimating the signal AoA. The simulation results have shown that the mean square errors of the obtained estimates achieve the Cramer-Rao bounds when SNR exceeding defined threshold values.
A Study of Zooming, Interactive Lenses and Overview+Detail Techniques in Collaborative Map-based Tasks

The support for multi-focus data exploration is vital in collaborative visualization. In these scenarios, which often involve multiple devices and large displays, users may focus on specific information on their individual screens while also sharing contextual views with others. While many visualization techniques developed for single-user applications can be adapted for use in collaborative settings, little research has been done on how to design adaptive versions of these techniques or how they may impact collaborative tasks involving large datasets. In this work, we perform a comparative study of three collaborative visualization techniques (Zooming, Interactive lenses and Overview+Detail) on large displays in three map-based visualization tasks (Exploration, Comparison and Spatial Memorizing). These three collaborative techniques draw on three different classical visualization techniques in a single-user setting. Our results show that these techniques have different impacts on users’ task performance and preferences. The collaborative Overview+Detail technique benefits users most in supporting Spatial Memorizing. Closely coupled groups prefer collaborative Zooming in Target Exploration. Based on these results, we further discuss the design of collaborative visualization techniques and propose suggestions for adapting classical single-user visualization techniques to a collaborative setting.
Multi-S3P: Protein Secondary Structure Prediction With Specialized Multi-Network and Self-Attention-Based Deep Learning Model

Protein structure prediction (PSP) is a vital challenge in bioinformatics, structural biology and drug discovery. Protein secondary structure (SS) prediction is critical since three-dimensional (3D) structures are primarily made up of secondary structures. With the advancement of deep learning approaches, SS classification accuracy has been significantly improved. Many existing methods use an ensemble of complex neural networks to improve SS prediction. Because of the high dimensionality of the hyperparameter space, deep neural networks with complex architectures are typically challenging to train effectively. Also, predicting secondary structures in the boundary regions between different types of SS is challenging. This study presents Multi-S3P, which employs bidirectional Long-Short-Term-Memory (BILSTM) and Convolutional Neural Networks (CNN) with a self-attention mechanism to improve the secondary structure prediction using an effective training strategy to capture the unique characteristics of each type of secondary structure and combine them more effectively. The ensemble of CNN and BILSTM can learn both contextual information and long-range interactions between the residues. In addition, using a self-attention mechanism allows the model to focus on the most important features for improving performance. We used the SPOT-1D dataset for the training and validation of our model using a set of four input features derived from amino acid sequences. Further, the model was tested on four popular independent test datasets and compared with various state-of-the-art predictors. The presented results show that Multi-S3P outperformed the other methods in terms of Q3, Q8 accuracy and other performance metrics, achieving the highest Q3 accuracy of 87.57% and a Q8 accuracy of 77.56% on the TEST2016 test set. More importantly, Multi-S3P demonstrates high performance in SS boundary regions. Our experiment also demonstrates that the combination of different input features and a multi-network-based training strategy significantly improved the performance.
Research on Multi-classification of TCM Tongue Symptoms Based on Self-supervised Learning

The advancement of computer technology has been progressing rapidly and the need for social life development, “Internet + health care” has become a necessary trend in human life, and the intelligent research of TCM (Traditional Chinese Medicine) tongue diagnosis is also one of the development directions. In recent years, neural networks have made significant progress in the field of medical image classification. However, neural networks often need to be built based on an extensive amount of data instances to achieve more precise results. In the medical field, collecting large and labeled datasets is costly. Considering this situation, we introduce a self-supervised Transformer framework, named SViT-T5, and build a masked image modeling task based on the Vision Transformer (ViT) architecture. The method treats restoring pixels as a proxy task for self-supervised learning, and image classification as a downstream task. Pre-train the model by using a great deal of unlabeled data and fine-tuning with the TongueNet dataset we created, five categories of tongue images are studied: cold syndrome, heat syndrome, deficiency syndrome, excess syndrome, and normal tongue image. And compare the classification results with the basic models without pre-training (ConvNeXt, ResNeXt, ViT, Swin Transformer) and pre-training on the ImageNet dataset. According to the results, the classification accuracy of our proposed SViT-T5 framework based on self-supervised learning reaches 85.938%. In contrast to other methods, our approach exhibits superior classification accuracy and generalization ability.
Factors Affecting ERP Systems Adoption in Organizations in the Face of Economic Crisis: A Systematic Literature Review

When a country is on the verge of an economic collapse, most enterprises struggle to decide what technological innovation activities to engage in. With the development of the IT industry and its value creation, adopting ERP systems has become one of the popular strategic decisions businesses have made. However, during an economic crisis, the propensity to adopt new technological solutions differs according to the organization's perception. Therefore, it's crucial to identify the factors that affect ERP adoption and the impact of the economic crisis on it. This study presents a comprehensive overview of the past literature on technological adoption models, ERP adoption and adoption determinants, and economic dynamics' impact on technological innovation activities in organizations. According to the study, a limited number of publications were found on ERP adoption in the face of an economic crisis. This study will help future researchers to understand the importance of the factors that affect ERP adoption and the influence of the country's financial condition on it.
5 A/1.17 kV NiO/$\beta$-Ga2O3 heterojunction power rectifier with high-temperature operation capability up to 548 K

Industrial power devices are required to conduct at least several amperes current in the on-state while blocking at least hundreds of volts in the off-state. In this work, high-temperature operational $\text{NiO}/\beta-\text{Ga}_{2}\mathrm{O}_{3}$ vertical p-n heterojunction diodes (HJDs) with ampere-level forward current and kV -level reverse breakdown voltage $(V_{b})$ have been demonstrated. The temperature-dependent current-voltage characteristics reveal that trap-assisted tunneling (TAT) current dominates the forward conduction mechanism of HJDs, while the leakage current is dominated by variable range hopping (VRH) mechanism under the high reverse bias. The resultant large-area (1×1 mm2) HJD rectifiers exhibit a superior forward on-state current of 5 A, a nearly-unity ideality factor and a large $V_{b}$ of 1.17 kV operated at a high temperature up to 548 K. The low deterioration rate of forward on-state current (1.24 mA/K at 4 V) and $V_{b}$ (0.95 V/K) with temperature implies high reliability of HJD, evidencing the promising potential of Ga2O3-based power diodes in harsh-environment power systems.
A Structured Krylov Subspace Projection Framework for Fast Power Integrity Verification

This paper presents a model order reduction approach, specifically designed for the generation of compact and efficient transient simulation models of system-level power distribution networks (PDN) of multicore processor systems. The proposed approach applies a Krylov subspace projection, with a structure that is adapted to a block-coupled state-space description of individual PDN subsystems. The latter include board-package, averaged models of integrated voltage regulators switching circuitry, and individual models of all cores including regulator inductors and capacitors. Numerical results from pro-posed reduced-order models provide major speedup with respect to SPICE with negligible loss of accuracy.
Power Cycling of Sintered SiC Power MOSFET Baseplate-less Modules with Aluminum Oxide and Silicon Nitride Substrates

This paper shows a comparison of power cycling (PC) lifetime between SiC power MOSFET baseplate-less modules with Si3N4 and Al2O3 DBC substrates at heating on/off-times of 2 s. As the modules were designed with soft Si-gel encapsulation and silver sinter die attach, the dominant failure mechanism was bond wire lift-off. Electro-thermal-mechanical (ETM) simulations are performed to support the lifetime analysis on a small set of statistical data. The results point out that the PC capability of the Al wires is not significantly affected by changing the semiconductor material from Si to SiC. Furthermore, the developed ETM simulations show that Si3N4 substrates will cause a faster temperature rise time, but will not lead to a significantly higher thermo-mechanical stress of the bond wire foot.
ERRATUM: “THE HERSCHEL AND IRAM CHESS SPECTRAL SURVEYS OF THE PROTOSTELLAR SHOCK L1157-B1: FOSSIL DEUTERATION” (2012, ApJ, 757, L9)

C. Codella1,2, C. Ceccarelli2, B. Lefloch2,3, F. Fontani1, G. Busquet4, P. Caselli5, C. Kahane2, D. Lis6, V. Taquet2, M. Vasta1, S. Viti7, and L. Wiesenfeld2 1 INAF, Osservatorio Astrofisico di Arcetri, Largo Enrico Fermi 5, I-50125 Firenze, Italy; codella@rcetri.astro. it 2 UJF-Grenoble 1/CNRS-INSU, Institut de Planétologie et d’Astrophysique de Grenoble (IPAG) UMR 5274, Grenoble, F-38041, France 3 Centro de Astrobiologia, CSIC-INTA, Carretera de Ajalvir, Km 4, Torrejon de Ardoz, E-28850, Madrid, Spain 4 INAF-Istituto di Astrofisica e Planetologia Spaziali, via Fosso del Cavaliere 100, I-00133 Roma, Italy 5 School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, UK 6 California Institute of Technology, Cahill Center for Astronomy and Astrophysics 301-17, Pasadena, CA 91125, USA 7 Department of Physics and Astronomy, University College London, London, UK Received 2012 October 9; published 2012 October 26
Cancer Cell-Intrinsic Alterations Associated with an Immunosuppressive Tumor Microenvironment and Resistance to Immunotherapy in Lung Cancer

Simple Summary Immunotherapy for non-small cell lung cancer (NSCLC) is a clinical reality with impressive efficacy for some patients. However, less than half of them will benefit from this therapeutic regime, and finding indicators of response is necessary for selecting the patients likely to respond. So far, detection of PD-L1 in tumors by immunohistochemistry is the only validated predictive biomarker. In recent years, certain changes in the tumor cells (intrinsic alterations), including mutations, epigenetic changes and metabolic rewiring, have been shown to modify the type of tumor microenvironment (TME) where such a tumor grows. This TME may determine response or refractoriness to immunotherapy. Examples of key mutations are KRAS, SKT11(LKB1), KEAP1 and TP53, as well as co-mutations of these genes. Reshaping the TME with novel strategies in these particular TMEs could increase the efficacy of immunotherapy in lung cancer patients. Abstract Despite the great clinical success of immunotherapy in lung cancer patients, only a small percentage of them (<40%) will benefit from this therapy alone or combined with other strategies. Cancer cell-intrinsic and cell-extrinsic mechanisms have been associated with a lack of response to immunotherapy. The present study is focused on cancer cell-intrinsic genetic, epigenetic, transcriptomic and metabolic alterations that reshape the tumor microenvironment (TME) and determine response or refractoriness to immune checkpoint inhibitors (ICIs). Mutations in KRAS, SKT11(LKB1), KEAP1 and TP53 and co-mutations of these genes are the main determinants of ICI response in non-small-cell lung cancer (NSCLC) patients. Recent insights into metabolic changes in cancer cells that impose restrictions on cytotoxic T cells and the efficacy of ICIs indicate that targeting such metabolic restrictions may favor therapeutic responses. Other emerging pathways for therapeutic interventions include epigenetic modulators and DNA damage repair (DDR) pathways, especially in small-cell lung cancer (SCLC). Therefore, the many potential pathways for enhancing the effect of ICIs suggest that, in a few years, we will have much more personalized medicine for lung cancer patients treated with immunotherapy. Such strategies could include vaccines and chimeric antigen receptor (CAR) cells.
Hashing to elliptic curves over highly 2-adic fields $\mathbb{F}_{\!q}$ with O(log(q)) operations in $\mathbb{F}_{\!q}$

The current article provides a new deterministic hash function H to almost any elliptic curve E over a finite field F q , having an F q - isogeny of degree 3. Since H just has to compute a certain Lucas sequence element, its complexity always equals O (log( q )) operations in F q with a small constant hidden in O . In comparison, whenever q ≡ 1 (mod 3), almost all previous hash functions need to extract at least one square root in F q . Over the field F q of 2-adicity ν this amounts to O (log( q )+ ν 2 ) operations in F q for the Tonelli–Shanks algorithm and O (log( q ) + ν 3 / 2 ) ones for the recent Sarkar algorithm. A detailed analysis shows that H is several times faster than earlier state-of-the-art hash functions to the curve NIST P-224 (for which ν = 96) from the American standard NIST SP 800-186.
Outside options and worker motivation∗

We study the relationship between outside options and workers’ incentives to exert effort. We first set up a relational contracting model where effort is constrained by the future value of an employment relationship. To test the predictions from this model, we evaluate changes in outside options arising from age and experience cutoffs in the Austrian unemployment insurance (UI) system. Results indicate that a 9-week UI benefit extension increases worker absenteeism at the intensive margin by 0.5 days per half-year, on average. Consistent with our model predicting that these effort reductions are more pronounced if the perceived relationship value is small, we find that our effects are stronger for workers with higher potential cost of unemployment, for older workers, in declining rather than in growing firms, in low-wage firms, and for women as well as workers with children. JEL Classification: D21, D22, J22, J53, M52