Miracle-dz/UniFAR-FTUs huggingface dataset:
Moiré and beyond in transition metal dichalcogenide twisted bilayers

Fabricating van der Waals bilayer heterostructures (BL-HS) by stacking the same or different two-dimensional layers, offers a unique physical system with rich electronic and optical properties. Twist-angle between component layers has emerged as a remarkable parameter that can control the period of lateral confinement, and nature of the exciton (Coulomb bound electron–hole pair) in reciprocal space thus creating exotic physical states including moiré excitons (MXs). In this review article, we focus on opto-electronic properties of excitons in transition metal dichalcogenide semiconductor twisted BL-HS. We look at existing evidence of MXs in localized and strongly correlated states, and at nanoscale mapping of moiré superlattice and lattice-reconstruction. This review will be helpful in guiding the community as well as motivating work in areas such as near-field optical measurements and controlling the creation of novel physical states.
A Calibration Approach to Transportability with Observational Data

An important consideration in clinical research studies is proper evaluation of internal and external validity. While randomized clinical trials can overcome several threats to internal validity, they may be prone to poor external validity. Conversely, large prospective observational studies sampled from a broadly generalizable population may be externally valid, yet susceptible to threats to internal validity, particularly confounding. Thus, methods that address confounding and enhance transportability of study results across populations are essential for internally and externally valid causal inference, respectively. We develop a weighting method which estimates the effect of an intervention on an outcome in an observational study which can then be transported to a second, possibly unrelated target population. The proposed methodology employs calibration estimators to generate complementary balancing and sampling weights to address confounding and transportability, respectively, enabling valid estimation of the target population average treatment effect. A simulation study is conducted to demonstrate the advantages and similarities of the calibration approach against alternative techniques. We also test the performance of the calibration estimator-based inference in a motivating real data example comparing whether the effect of biguanides versus sulfonylureas - the two most common oral diabetes medication classes for initial treatment - on all-cause mortality described in a historical cohort applies to a contemporary cohort of US Veterans with diabetes.
COVID-Robot: Monitoring Social Distancing Constraints in Crowded Scenarios

Maintaining social distancing norms between humans has become an indispensable precaution to slow down the transmission of COVID-19. We present a novel method to automatically detect pairs of humans in a crowded scenario who are not adhering to the social distance constraint, i.e. about 6 feet of space between them. Our approach makes no assumption about the crowd density or pedestrian walking directions. We use a mobile robot with commodity sensors, namely an RGB-D camera and a 2-D lidar to perform collision-free navigation in a crowd and estimate the distance between all detected individuals in the camera's field of view. In addition, we also equip the robot with a thermal camera that wirelessly transmits thermal images to a security/healthcare personnel who monitors if any individual exhibits a higher than normal temperature. In indoor scenarios, our mobile robot can also be combined with static mounted CCTV cameras to further improve the performance in terms of number of social distancing breaches detected, accurately pursuing walking pedestrians etc. We highlight the performance benefits of our approach in different static and dynamic indoor scenarios.
Classification of astrophysical events from gravitational wave signature

In recent years, improvements in Deep Learning techniques towards Gravitational Wave astronomy have led to a significant rise in various classification algorithm development. A few of these algorithms have been successfully employed to search gravitational waves from binary blackhole merger events. However, these algorithms still lack success with significant time duration and further prediction of the merger events parameters. In this work, we intended to advance the boundaries of deep learning techniques, using the convolutional neural networks, to go beyond binary classification and predicting complicated features that possess physical significance. This method is not a replacement for the already established and thoroughly examined methods like matched filtering for the detection of gravitational waves but is an alternative method wherein human interference is minimal. The deep learning model we present has been trained on 12s of data segment, aimed to predict the 27 features of any LIGO time series data. During training, the maximum accuracy attained was 90.93%, with a validation accuracy of 89.97%.
Real-Time Spatio-Temporal LiDAR Point Cloud Compression

Compressing massive LiDAR point clouds in real-time is critical to autonomous machines such as drones and self-driving cars. While most of the recent prior work has focused on compressing individual point cloud frames, this paper proposes a novel system that effectively compresses a sequence of point clouds. The idea to exploit both the spatial and temporal redundancies in a sequence of point cloud frames. We first identify a key frame in a point cloud sequence and spatially encode the key frame by iterative plane fitting. We then exploit the fact that consecutive point clouds have large overlaps in the physical space, and thus spatially encoded data can be (re-)used to encode the temporal stream. Temporal encoding by reusing spatial encoding data not only improves the compression rate, but also avoids redundant computations, which significantly improves the compression speed. Experiments show that our compression system achieves 40× to 90× compression rate, significantly higher than the MPEG’s LiDAR point cloud compression standard, while retaining high end-to-end application accuracies. Meanwhile, our compression system has a compression speed that matches the point cloud generation rate by today LiDARs and out-performs existing compression systems, enabling real-time point cloud transmission.
Photometric classification of Hyper Suprime-Cam transients using machine learning

The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to the need for fast type classification for spectroscopic follow-up and prompted us to develop a machine learning algorithm using a deep neural network with highway layers. This algorithm is trained by actual observed cadence and filter combinations such that we can directly input the observed data array without any interpretation. We tested our model with a dataset from the LSST classification challenge (Deep Drilling Field). Our classifier scores an area under the curve (AUC) of 0.996 for binary classification (SN Ia or non-SN Ia) and 95.3% accuracy for three-class classification (SN Ia, SN Ibc, or SN II). Application of our binary classification to HSC transient data yields an AUC score of 0.925. With two weeks of HSC data since the first detection, this classifier achieves 78.1% accuracy for binary classification, and the accuracy increases to 84.2% with the full dataset. This paper discusses the potential use of machine learning for SN type classification purposes.
Toward an End-to-End Auto-tuning Framework in HPC PowerStack

Efficiently utilizing procured power and optimizing performance of scientific applications under power and energy constraints are challenging. The HPC PowerStack defines a software stack to manage power and energy of high-performance computing systems and standardizes the interfaces between different components of the stack. This survey paper presents the findings of a working group focused on the end-to-end tuning of the PowerStack. First, we provide a background on the PowerStack layer-specific tuning efforts in terms of their high-level objectives, the constraints and optimization goals, layer-specific telemetry, and control parameters, and we list the existing software solutions that address those challenges. Second, we propose the PowerStack end-to-end auto-tuning framework, identify the opportunities in co-tuning different layers in the PowerStack, and present specific use cases and solutions. Third, we discuss the research opportunities and challenges for collective auto-tuning of two or more management layers (or domains) in the PowerStack. This paper takes the first steps in identifying and aggregating the important R&D challenges in streamlining the optimization efforts across the layers of the PowerStack.
Dehaze-GLCGAN: Unpaired Single Image De-hazing via Adversarial Training

Single image de-hazing is a challenging problem, and it is far from solved. Most current solutions require paired image datasets that include both hazy images and their corresponding haze-free ground-truth images. However, in reality, lighting conditions and other factors can produce a range of haze-free images that can serve as ground truth for a hazy image, and a single ground truth image cannot capture that range. This limits the scalability and practicality of paired image datasets in real-world applications. In this paper, we focus on unpaired single image de-hazing and we do not rely on the ground truth image or physical scattering model. We reduce the image de-hazing problem to an image-to-image translation problem and propose a dehazing Global-Local Cycle-consistent Generative Adversarial Network (Dehaze-GLCGAN). Generator network of Dehaze-GLCGAN combines an encoder-decoder architecture with residual blocks to better recover the haze free scene. We also employ a global-local discriminator structure to deal with spatially varying haze. Through ablation study, we demonstrate the effectiveness of different factors in the performance of the proposed network. Our extensive experiments over three benchmark datasets show that our network outperforms previous work in terms of PSNR and SSIM while being trained on smaller amount of data compared to other methods.
Learning Interpretable Representation for Controllable Polyphonic Music Generation

While deep generative models have become the leading methods for algorithmic composition, it remains a challenging problem to control the generation process because the latent variables of most deep-learning models lack good interpretability. Inspired by the content-style disentanglement idea, we design a novel architecture, under the VAE framework, that effectively learns two interpretable latent factors of polyphonic music: chord and texture. The current model focuses on learning 8-beat long piano composition segments. We show that such chord-texture disentanglement provides a controllable generation pathway leading to a wide spectrum of applications, including compositional style transfer, texture variation, and accompaniment arrangement. Both objective and subjective evaluations show that our method achieves a successful disentanglement and high quality controlled music generation.
Accuracy of slender body theory in approximating force exerted by thin fiber on viscous fluid

We consider the mapping properties of the integral operator arising in nonlocal slender body theory (SBT) for the model geometry of a straight, periodic filament. It is well known that the classical singular SBT integral operator suffers from high wavenumber instabilities, making it unsuitable for approximating the slender body inverse problem, where the fiber velocity is prescribed and the integral operator must be inverted to find the force density along the fiber. Regularizations of the integral operator must therefore be used instead. Here, we consider two regularization methods: spectral truncation and the δ ‐regularization of Tornberg and Shelley (2004). We compare the mapping properties of these approximations to the underlying partial differential equation (PDE) solution, which for the inverse problem is simply the Stokes Dirichlet problem with data constrained to be constant on cross sections. For the straight‐but‐periodic fiber with constant radius ε>0 , we explicitly calculate the spectrum of the operator mapping fiber velocity to force for both the PDE and the approximations. We prove that the spectrum of the original SBT operator agrees closely with the PDE operator at low wavenumbers but differs at high frequencies, allowing us to define a truncated approximation with a wavenumber cutoff ∼1/ε . For both the truncated and δ ‐regularized approximations, we obtain rigorous L2 ‐based convergence to the PDE solution as ε→0 : A fiber velocity with H1 regularity gives O(ε) convergence, while a fiber velocity with at least H2 regularity yields O(ε2) convergence. Moreover, we determine the dependence of the δ ‐regularized error estimate on the regularization parameter δ .
Single Board Computers (SBC): The Future of Next Generation Pedagogies in Pakistan

ARM processors have taken over the mobile industry from a long time now. Future of data centers and the IT industry is estimated to make use of ARM Processors. Projects like Openstack on ARM are enabling use of ARM in data centers . Single board computers (SBCs) based on ARM processors have become the norm these days. Reason for their popularity lies in their cost effective and power efficient nature. There are hundreds of them available in the market having different sizes, compute power and prices. The reason for their popularity is largely due to the rise of new technology called IoT (Internet of Things) but there is another perspective where they can become handy. Low Price and Power Usage of single board computers makes them top candidate to be used for teaching many courses with hands-on experience in developing countries like Pakistan. Many boards support full Linux distributions and can be used as general-purpose computers while many of them are open hardware based. In this paper, we have reviewed the famous options available and tried to figure out which of them are better for teaching what kind of courses
Exploiting Fully Convolutional Network and Visualization Techniques on Spontaneous Speech for Dementia Detection

In this paper, we exploit a Fully Convolutional Network (FCN) to analyze the audio data of spontaneous speech for dementia detection. A fully convolutional network accommodates speech samples with varying lengths, thus enabling us to analyze the speech sample without manual segmentation. Specifically, we first obtain the Mel Frequency Cepstral Coefficient (MFCC) feature map from each participant's audio data and convert the speech classification task on audio data to an image classification task on MFCC feature maps. Then, to solve the data insufficiency problem, we apply transfer learning by adopting a pre-trained backbone Convolutional Neural Network (CNN) model from the MobileNet architecture and the ImageNet dataset. We further build a convolutional layer to produce a heatmap using Otsu's method for visualization, enabling us to understand the impact of the time-series audio segments on the classification results. We demonstrate that our classification model achieves 66.7% over the testing dataset, 62.5% of the baseline model provided in the ADReSS challenge. Through the visualization technique, we can evaluate the impact of audio segments, such as filled pauses from the participants and repeated questions from the investigator, on the classification results.
Discouraging Pool Block Withholding Attacks in Bitcoins

The arisen of Bitcoin has led to much enthusiasm for blockchain research and block mining, and the extensive existence of mining pools helps its participants (i.e., miners) gain reward more frequently. Recently, the mining pools are proved to be vulnerable for several possible attacks, and pool block withholding attack is one of them: one strategic pool manager sends some of her miners to other pools and these miners pretend to work on the puzzles but actually do nothing. And these miners still get reward since the pool manager can not recognize these malicious miners. In this work, we revisit the game-theoretic model for pool block withholding attacks and propose a revised approach to reallocate the reward to the miners. Fortunately, in the new model, the pool managers have strong incentive to not launch such attacks. We show that for any number of mining pools, no-pool-attacks is always a Nash equilibrium. Moreover, with only two minority mining pools participating, no-pool-attacks is actually the unique Nash equilibrium.
Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications.

The recent advancements in the Internet of Things (IoT) are giving rise to the proliferation of interconnected devices, enabling various smart applications. These enormous number of IoT devices generates a large capacity of data that further require intelligent data analysis and processing methods, such as Deep Learning (DL). Notably, the DL algorithms, when applied in the Industrial Internet of Things (IIoT), can enable various applications such as smart assembling, smart manufacturing, efficient networking, and accident detection-and-prevention. Therefore, motivated by these numerous applications; in this paper, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, auto-encoders, and recurrent neural networks and there use in different industries. Then, we outline numerous use cases of DL for IIoT systems, including smart manufacturing, smart metering, smart agriculture, etc. Moreover, we categorize several research challenges regarding the effective design and appropriate implementation of DL-IIoT. Finally, we present several future research directions to inspire and motivate further research in this area.
Deep Architectures for Modulation Recognition with Multiple Receive Antennas

Modulation recognition using deep neural networks has shown promising advantage over conventional algorithms. However, most existing research focuses on single receive antenna. In this paper, modulation recognition with multiple receive antennas using deep neural networks is investigated and four different architectures are introduced, including equal-gain CNN, multi-view CNN, 2-dimensional CNN and 3-dimensional CNN. Each architecture is constructed based on a ResNet and tuned to the extent that its performance does not further improve when the network size and parameters change with a given dataset. These architectures are then compared in terms of classification accuracy. Simulation results show that 3-dimensional CNN yields the overall best performance, while the equal-gain CNN leads to the lowest performance. Further, both 2-dimensional CNN and 3-dimensional CNN, which jointly extract features from multiple receive antennas with different feature encoding, outperforms either equal-gain or multi-view CNN, which fuses extracted features from each antenna. This indicates that utilizing inherent structures within deep neural networks to jointly extract features from different antennas can achieve better performance than the schemes that combine individually encoded features from each antenna, and extending the dimension of CNN from two to three can enhance feature extraction capabilities in the context of modulation recognition.
Taxonomic revision and biogeography of the Tamarix -feeding Diorhabda elongata (Brullé, 1832) species group (Coleoptera: Chrysomelidae: Galerucinae: Galerucini) and analysis of their potential in biological control of Tamarisk

The primarily Palearctic Diorhabda elongata species group is established for five Tamarix-feeding sibling species(tamarisk beetles): D. elongata (Brulle, 1832), D. carinata (Faldermann, 1837), D. sublineata (Lucas, 1849) REVISEDSTATUS, D. carinulata (Desbrochers, 1870), and D. meridionalis Berti & Rapilly, 1973 NEW STATUS. Diorhabda koltzei ab. basicornis Laboissiere, 1935 and D. e. deserticola Chen, 1961 are synonymized under D. carinulata NEWSYNONYMY. Illustrated keys utilize genitalia, including male endophallic sclerites and female vaginal palpi andinternal sternite VIII. Distribution, comparative biogeography, biology, and potential in biological control of Tamarix inNorth America are reviewed. Diorhabda elongata is circummediterranean, favoring Mediterranean and temperate forestsof Italy to western Turkey. Diorhabda carinata resides in warm temperate grasslands, deserts, and forests of southernUkraine south to Iraq and east to western China. Diorhabda sublineata occupies Mediterranean woodlands from Franceto North Africa and subtropical deserts east to Iraq. Diorhabda carinulata primarily inhabits cold temperate deserts ofMongolia and China west to Russia and south to montane grasslands and warm deserts in southern Iran. Diorhabda meridionalis primarily occupies maritime subtropical deserts of southern Pakistan and Iran to Syria. Northern climatypesof D. carinulata are effective in Tamarix biological control, especially in the Great Basin desert. Diorhabda elongata isprobably best suited to Mediterranean woodlands of northern California. Northern climatypes of D. carinata may be bestsuited for central U.S. grasslands. Diorhabda sublineata, D. meridionalis, and southern climatypes of D. carinata and D. carinulata may each be uniquely suited to areas of the southwestern U.S.
Curriculum Learning for Recurrent Video Object Segmentation

Video object segmentation can be understood as a sequence-to-sequence task that can benefit from the curriculum learning strategies for better and faster training of deep neural networks. This work explores different schedule sampling and frame skipping variations to significantly improve the performance of a recurrent architecture. Our results on the car class of the KITTI-MOTS challenge indicate that, surprisingly, an inverse schedule sampling is a better option than a classic forward one. Also, that a progressive skipping of frames during training is beneficial, but only when training with the ground truth masks instead of the predicted ones. Source code and trained models are available at this http URL.
PHAT XX. AGB Stars and Other Cool Giants in M31 Star Clusters

The presence of asymptotic giant branch (AGB) stars in clusters provides key constraints for stellar models, as has been demonstrated with historical data from the Magellanic Clouds. In this work, we look for candidate AGB stars in M31 star clusters from the Panchromatic Hubble Andromeda Treasury survey. Our photometric criteria selects stars brighter than the tip of the red giant branch, which includes the bulk of the thermally pulsing AGB stars as well as early-AGB stars and other luminous cool giants expected in young stellar populations (e.g., massive red supergiants, and intermediate-mass red helium-burning stars). The AGB stars can be differentiated, a posteriori, using the ages already estimated for our cluster sample. 937 candidates are found within the cluster aperture radii, half (∼450) of which are very likely cluster members. Cross-matching with additional databases reveals two carbon stars and 10 secure variables among them. The field-corrected age distribution reveals the presence of young supergiants peaking at ages smaller than 108 yr, followed by a long tail of AGB stars extending up to the oldest possible ages. This long tail reveals the general decrease in the numbers of AGB stars from initial values of at 108 yr down to at 1010 yr. Theoretical models of near-solar metallicity reproduce this general trend, although with localized discrepancies over some age intervals, whose origin is not yet identified. The entire catalog is released together with finding charts to facilitate follow-up studies.