Miracle-dz/UniFAR-FTUs huggingface dataset:
Tunable Plasmons in Large-Area 
WTe2
 Thin Films

The observation of electrically tunable and highly confined plasmons in graphene has stimulated the exploration of interesting properties of plasmons in other two-dimensional materials. Recently, hyperbolic plasmon resonance modes have been observed in exfoliated ${\mathrm{W}\mathrm{Te}}_{2}$ films, a type-II Weyl semimetal with layered structure, providing a platform for the assembly of plasmons with hyperbolicity and exotic topological properties. However, the plasmon modes were observed in relatively thick and small-area films, which restrict the tunability and application for plasmons. Here, large-area (approximately cm) ${\mathrm{W}\mathrm{Te}}_{2}$ films with different thicknesses are grown by the chemical vapor deposition method, in which plasmon resonance modes are observed in films with different thicknesses down to about 8 nm. Hybridization of plasmon and surface polar phonons of the substrate is revealed by mapping the plasmon dispersion. The plasmon frequency is demonstrated to be tunable by changing the temperature and film thickness. Our results facilitate the development of a tunable and scalable ${\mathrm{W}\mathrm{Te}}_{2}$ plasmonic system for revealing topological properties and towards various applications in sensing, imaging, and light modulation.
Do we need non-ideal magnetohydrodynamic to model protostellar discs?

We investigate and discuss protostellar discs in terms of where the various non-ideal magnetohydrodynamics (MHD) processes are important. We find that the traditional picture of a magnetized disc (where Ohmic resistivity is dominant near the mid-plane, surrounded by a region dominated by the Hall effect, with the remainder of the disc dominated by ambipolar diffusion) is a great oversimplification. In simple parametrized discs, we find that the Hall effect is typically the dominant term throughout the majority of the disc. More importantly, we find that in much of our parametrized discs, at least two non-ideal processes have coefficients within a factor of 10 of one another, indicating that both are important and that naming a dominant term underplays the importance of the other terms. Discs that were self-consistently formed in our previous studies are also dominated by the Hall effect, and the ratio of ambipolar diffusion and Hall coefficients is typically less than 10, suggesting that both terms are equally important and listing a dominant term is misleading. These conclusions become more robust once the magnetic field geometry is taken into account. In agreement with the literature we review, we conclude that non-ideal MHD processes are important for the formation and evolution of protostellar discs. Ignoring any of the non-ideal processes, especially ambipolar diffusion and the Hall effect, yields an incorrect description of disc evolution.
PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search

Neural network (NN) models are increasingly used in scientific simulations, AI, and other high performance computing (HPC) fields to extract knowledge from datasets. Each dataset requires tailored NN model architecture, but designing structures by hand is a time-consuming and error-prone process. Neural architecture search (NAS) automates the design of NN architectures. NAS attempts to find well-performing NN models for specialized datsets, where performance is measured by key metrics that capture the NN capabilities (e.g., accuracy of classification of samples in a dataset). Existing NAS methods are resource intensive, especially when searching for highly accurate models for larger and larger datasets. To address this problem, we propose a performance estimation strategy that reduces the resources for training NNs and increases NAS throughput without jeopardizing accuracy. We implement our strategy via an engine called PEng4NN that plugs into existing NAS methods; in doing so, PEng4NN predicts the final accuracy of NNs early in the training process, informs the NAS of NN performance, and thus enables the NAS to terminate training NNs early. We assess our engine on three diverse datasets (i.e., CIFAR-100, Fashion MNIST, and SVHN). By reducing the training epochs needed, our engine achieves substantial throughput gain; on average, our engine saves 61% to 82% of training epochs, increasing throughput by a factor of 2.5 to 5 compared to a state-of-the-art NAS method. We achieve this gain without compromising accuracy, as we demonstrate with two key outcomes. First, across all our tests, between 74% and 97% of the ground truth best models lie in our set of predicted best models. Second, the accuracy distributions of the ground truth best models and our predicted best models are comparable, with the mean accuracy values differing by at most . 7 percentage points across all tests.
A minimal timescale for the continuum in 4U 1608-52 and Aql X-1

Similar to black-hole X-ray binary (BHXRB) transients, hysteresis-like state transitions are also seen in some neutronstar X-ray binaries (NSXRBs). Using a method based on wavelets and lightcurves constructed from archival RXTE observations, we extract a minimal timescale over the complete range of transitions for 4U 1608-52 during the 2002 and 2007 outbursts and the 1999 and 2000 outbursts for Aql X-1. We present evidence for a strong positive correlation between this minimal timescale and a similar timescale extracted from the corresponding power spectra of these sources.
Gravitational wave templates from Extreme Mass Ratio Inspirals

An extreme mass ratio inspiral takes place when a compact stellar object is inspiraling into a supermassive black hole due to gravitational radiation reaction. Gravitational waves (GWs) from this system can be calculated using the Teukolsky equation (TE). In our case, we compute the asymptotic GW fluxes of a spinning body orbiting a Kerr black hole by solving numerically the TE both in time and frequency domain. Our ultimate goal is to produce GW templates for space-based detectors such as LISA.
Toward the endoscopic classification of unipotent representations of $p$-adic $G_2$

In this paper, we review the Langlands correspondence for unipotent representations of the exceptional group of type G2 over a p-adic field F and present it in an explicit form. Then we compute all ABV-packets, as introduced in [CFM+21] following ideas from [Vog93], for these representations and prove that these packets satisfy properties derived from the expectation that they are generalized A-packets. We attach distributions to ABVpackets for G2 and its endoscopic groups and study a geometric endoscopic transfer of the distributions. This paper builds on [CFZ].
Polarization enhanced multi-grain-boundary dendritic micro–nano structure α-Fe for electromagnetic absorption applications: synthesis and characterization

In this paper, multi-grain-boundary hierarchical dendritic micro–nano structure α-Fe was successfully synthesized in electrolyte containing rare earth (RE) ions by the electric field-induced and electrochemical reduction method. The results show that the size of the dendritic morphology samples decreases from about 10 μm to about 6 μm with increasing RE ion concentration, and plentiful nano-sized particles are generated on their surface due to the adsorption suppression effect of the RE ions, which facilitates a great increase in the BET surface area and the formation of abundant grain boundaries. All of these RE-added dendritic α-Fe exhibit excellently enhanced electromagnetic absorption performance. The minimum reflection loss (RL) value (around −60 dB) of the RE-added samples is twice that of the samples without added RE ions, and the absorption peak moves to higher frequency range as the RE ion concentration is increased. The widest absorption band (in which RL < −20 dB) of the La-added samples grows to 2 GHz. We mainly ascribe the great enhancement of the EMA performance to the unique structural characteristics, such as the great surface area and abundant grain boundaries, which could affect the dielectric loss and magnetic loss of the absorbers. Not only has this work directly confirmed the influence of the interface and grain boundaries on the electromagnetic absorption performance, but it has also provided a new way to control the morphology and microstructure using RE ions.
Intersection of Solvable Hall subgroups in finite groups

Throughout the paper the term “group” we always use in the meaning “finite group”. We use symbols A ≤ G and A ✂ G if A is a subgroup of G and A is a normal subgroup of G respectively. Given H ≤ G by HG = ∩g∈GH g we denote the kernel of H. Assume that G acts on Ω. An element x ∈ Ω is called a G-regular point, if |xG| = |G|, i.e., if the stabilizer of x is trivial. We define the action of G on Ωk by
The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention

A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behavior and activities. While these algorithms work well in a controlled environment, naturalistic driving conditions add new challenges such as illumination variations, occlusions, and extreme head poses. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks to effectively monitor driver actions and behaviors. Toward building the required infrastructure, this paper presents the multimodal driver monitoring (MDM) dataset, which was collected with 59 subjects that were recorded performing various tasks. We use the Fi-Cap device that continuously tracks the head movement of the driver using fiducial markers, providing frame-based annotations to train head pose algorithms in naturalistic driving conditions. We ask the driver to look at predetermined gaze locations to obtain accurate correlation between the driver’s facial image and visual attention. We also collect data when the driver performs common secondary activities such as navigation using a smart phone and operating the in-car infotainment system. All of the driver’s activities are recorded with high definition RGB cameras and a time-of-flight depth camera. We also record the controller area network-bus (CAN-Bus), extracting important information. These high quality recordings serve as the ideal resource to train various efficient algorithms for monitoring the driver, providing further advancements in the field of in-vehicle safety systems.
Analysis and Design of a PMUT-based transducer for Powering Brain Implants

This paper presents an analytical design of an ultrasonic power transfer system based on piezoelectric micro-machined ultrasonic transducer (PMUT) for fully wireless brain implants in mice. The key steps like the material selection of each layer and the top electrode radius to maximize the coupling factor are well-detailed. This approach results in the design of a single cell with a high effective coupling coefficient. Furthermore, compact models are used to make the design process less time-consuming for designers. These models are based on the equivalent circuit theory for the PMUT. A cell of 107 um in radius, 5 um in thickness of Lead Zirconate Titanium (PZT), and 10 um in thickness of silicon (Si) is found to have a 4% of effective coupling coefficient among the highest values for a clamped edge boundary conditions. Simulation results show a frequency of 2.84 MHz as resonance. In case of an array, mutual impedance and numerical modeling are used to estimate the distance between the adjacent cells. In addition, the area of the proposed transducer and the number of cells are computed with the Rayleigh distance and neglecting the cross-talk among cells, respectively. The designed transducer consists of 7x7 cells in an area of 3.24 mm2. The transducer is able to deliver an acoustic intensity of 7.185 mW/mm2 for a voltage of 19.5 V for powering brain implants seated in the motor cortex and striatum of the mice's brain. The maximum acoustic intensity occurs at a distance of 2.5 mm in the near field which was estimated with the Rayleigh length equation.
Flexible Validity Conditions for the Multivariate Mat\'ern Covariance in any Spatial Dimension and for any Number of Components

aDepartment of Mining Engineering, University of Chile, Avenida Beauchef 850, Santiago, Chile. bAdvanced Mining Technology Center, University of Chile, Avenida Beauchef 850, Santiago, Chile. cDepartment of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, The United Arab Emirates. dSchool of Computer Science and Statistics, Trinity College, Dublin, Ireland. eDepartment of Statistics, Brigham Young University, Provo, UT, USA.
Yang-Lee edge singularity triggered entanglement transition

Shao-Kai Jian, ∗ Zhi-Cheng Yang, 3, ∗ Zhen Bi, and Xiao Chen Condensed Matter Theory Center, Department of Physics, University of Maryland, College Park, Maryland 20742, USA Joint Quantum Institute, University of Maryland, College Park, Maryland 20742, USA Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, Maryland 20742, USA Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA Department of Physics, Boston College, Chestnut Hill, MA 02467, USA (Dated: January 13, 2021)
Project ThaiPASS: international outreach blending astronomy and Python

We present our outreach program, the Thailand–UK Python+Astronomy Summer School (ThaiPASS), a collaborative project comprising UK and Thai institutions and assess its impact and possible application to schools in the United Kingdom. Since its inception in 2018, the annual ThaiPASS has trained around 60 Thai high-school students in basic data handling skills using Python in the context of various astronomy topics, using current research from the teaching team. Our impact assessment of the 5 day summer schools shows an overwhelmingly positive response from students in both years, with over 80% of students scoring the activities above average in all activities but one. We use this data to suggest possible future improvements. We also discuss how ThaiPASS may inspire further outreach and engagement activities within the UK and beyond.
On complex surfaces with definite intersection form

A compact complex surface with positive definite intersection lattice is either the projective plane or a false projective plane. If the intersection lattice is negative definite, the surface is either a non-minimal secondary Kodaira surface, a non-minimal elliptic surface with b1 = 1, or a class VII surface with b2 > 0. In all cases the lattice is odd and diagonalizable over the integers.
Fine-grained Semantic Constraint in Image Synthesis

In this paper, we propose a multi-stage and high-resolution model for image synthesis that uses fine-grained attributes and masks as input. With a fine-grained attribute, the proposed model can detailedly constrain the features of the generated image through rich and fine-grained semantic information in the attribute. With mask as prior, the model in this paper is constrained so that the generated images conform to visual senses, which will reduce the unexpected diversity of samples generated from the generative adversarial network. This paper also proposes a scheme to improve the discriminator of the generative adversarial network by simultaneously discriminating the total image and sub-regions of the image. In addition, we propose a method for optimizing the labeled attribute in datasets, which reduces the manual labeling noise. Extensive quantitative results show that our image synthesis model generates more realistic images.
AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of coronavirus research. We report here on a project that leverages both human and artificial intelligence to detect references to drug-like molecules in free text. We engage non-expert humans to create a corpus of labeled text, use this labeled corpus to train a named entity recognition model, and employ the trained model to extract 10912 drug-like molecules from the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198875 papers. Performance analyses show that our automated extraction model can achieve performance on par with that of non-expert humans.
Non-Orthogonal Multiple Access and Network Slicing: Scalable Coexistence of eMBB and URLLC

The 5G systems feature three generic services: enhanced Mobile BroadBand (eMBB), massive Machine-Type Communications (mMTC) and Ultra-Reliable and Low-Latency Communications (URLLC). The diverse requirements of these services in terms of data-rates, number of connected devices, latency and reliability can lead to a sub-optimal use of the 5G network, thus network slicing is proposed as a solution that creates customized slices of the network specifically designed to meet the requirements of each service. Under the network slicing, the radio resources can be shared in orthogonal and non-orthogonal schemes. Motivated by Industrial Internet of Things (IIoT) scenarios where a large number of sensors may require connectivity with stringent requirements of latency and reliability, we propose the use of Non-Orthogonal Multiple Access (NOMA) to improve the number of URLLC devices that are connected in the uplink to the same base station (BS), for both orthogonal and non-orthogonal network slicing with eMBB devices. The multiple URLLC devices transmit simultaneously and across multiple frequency channels. We set the reliability requirements for the two services and evaluate the pairs of achievable sum rates. We show that, even with overlapping transmissions from multiple eMBB and URLLC devices, the use of NOMA techniques allows us to guarantee the reliability requirements for both services.
Transforming Multi-Conditioned Generation from Meaning Representation

Our study focuses on language generation by considering various information representing the meaning of utterances as multiple conditions of generation. Generating an utterance from a Meaning representation (MR) usually passes two steps: sentence planning and surface realization. However, we propose a simple one-stage framework to generate utterances directly from MR. Our model is based on GPT2 and generates utterances with flat conditions on slot and value pairs, which does not need to determine the structure of the sentence. We evaluate several systems in the E2E dataset with 6 automatic metrics. Our system is a simple method, but it demonstrates comparable performance to previous systems in automated metrics. In addition, using only 10% of the dataset without any other techniques, our model achieves comparable performance, and shows the possibility of performing zero-shot generation and expanding to other datasets.
Identification of Urinary Exosomal miRNAs for the Non-Invasive Diagnosis of Prostate Cancer

Background Novel and non-invasive biomarkers with higher sensitivity and specificity for the diagnosis of prostate cancer (PCa) is urgently needed. In this study, we used next-generation sequencing (NGS) to characterize the genome-wide exosomal miRNA expression profiling in urine specimens and explored the diagnostic potential of urinary exosomal miRNAs for PCa. Methods Urinary exosomal microRNA expression profiling was performed by next-generation sequencing (NGS) and then validated by quantitative real-time PCR. Results Significant downregulation of urinary exosomal miR-375 was observed in PCa patients compared with healthy controls, while the expression levels of urinary exosomal miR-451a, miR-486-3p and miR-486-5p were found to be significantly up-regulated in the PCa patients. Furthermore, the expression level of urinary exosomal miR-375 showed a significant correlation with the clinical T-stage and bone metastasis of patients with PCa (P<0.05). Receiver operator characteristic curve demonstrated that the urinary exosomal miR-375, miR-451a, miR-486-3p and miR-486-5p levels can be used to differentiate PCa patients from healthy controls, with area under the curves (AUCs) of 0.788, 0.757, 0.704 and 0.796, respectively. The urinary exosomal miR-375 was found to be superior in discriminating between localized and metastatic PCa with an AUC of 0.806. Moreover, PCa patients can be distinguished from patients with benign prostatic hyperplasia by using a panel combining urinary exosomal miR-375 and miR-451a with an AUC of 0.726. Conclusion These findings demonstrate that the urinary exosomal miRNAs can serve as novel and non-invasive biomarkers for diagnosing and predicting the progression of PCa.
Underweight and Associated Factors Among Teenage Adolescent Girls in Resource-poor Settings: A Cross-sectional Study

Background and Purpose Understanding the undernutrition status of teenage adolescent girls living in urban slums and its associated factors is meaningful to formulate customized health strategies. This study aimed to determine the prevalence of being underweight and associated factors among teenage adolescent girls in urban slums. Materials and Methods In this cross-sectional study, we enrolled a total of 418 teenage adolescent girls from five of 210 urban slums of Varanasi district, Uttar Pradesh, India employing two-stage probability sampling for the selection of households and subjects, between September 2016 and July 2017. The study of underwight subjects was assessed with BMI for age using standard criteria. Factors associated with being underweight were determined by multivariable logistic regression analysis. Results Of 418 study subjects, 49.76% (208/418) were underweight. Results revealed that sociodemographic factors such as teenage adolescent girls who were from SC/ST (schedule caste/schedule tribe) caste/ethnicity (adjusted odds ratio (AOR)=2.02, 95%CI: 1.00–4.23), subjects whose father’s education level was primary or lower (AOR=1.87, 95%CI: 1.12–3.11), and number of people in the family >4 (AOR=2.18, 95%CI: 1.18–4.03) were associated with being underweight. Likewise, dietary behavior-related factors such as vegetarian (AOR=2.21, 95%CI: 1.25–3.92), and <3 meals per day (AOR=2.36, 95%CI: 1.40–3.98) than their counterparts were associated with being underweight. In addition, teenage adolescent girls from food-insecure households (AOR=3.33, 95%CI: 2.01–5.51) were more likely to be underweight than those from food-secure households. Conclusion The higher burden of underweight among teenage adolescent girls in Indian urban slums needs to be addressed through specific public health interventions such as by improving education, providing education regarding dietary behavior, and having access to sufficient, safe, and nutritious foods.