Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory and design of a tapered line distributed photodetector

We present the theory and design of a tapered line distributed photodetector (TLDP). In the previously demonstrated velocity-matched distributed photodetector (VMDP), high electrical bandwidth is achieved by proper termination in the input end to absorb reverse traveling waves, sacrificing one-half of the quantum efficiency. By utilizing the tapered line structure and phase matching between optical waves and microwaves in our analyzed structure, a traveling-wave photodetector is more realizable and ultrahigh bandwidth can be attained due to removal of the extra input dummy load that sacrifices one-half of the total quantum efficiency. To investigate the advantages of TLDP over VMDP, we calculate their electrical bandwidth performances by using an analytic photodistributed current model. We adopted low-temperature-grown (LTG) GaAs-based metal-semiconductor-metal (MSM) traveling-wave photodetectors as example unit active devices in the analytic bandwidth calculation for their high-speed and high-power performances. Both VMDP and TLDP in our simulation are assumed to be transferred onto glass substrates, which would achieve high microwave velocity/impedance and make radiation loss negligible. The simulated bandwidth of a properly designed LTG GaAs MSM TLDP is /spl sim/325 GHz, which is higher than the simulated bandwidth of the LTG GaAs MSM VMDP with an open-circuit input end (/spl sim/240 GHz) and is almost comparable to the simulated bandwidth of an input-terminated LTG GaAs MSM VMDP (/spl sim/330 GHz). This proposed method can be applied to the design of high-bandwidth distributed photodetectors for radio-frequency photonic systems and optoelectronic generation of high-power microwaves and millimeter waves.
Screening nearest-neighbor interactions in networks of exciton-polariton condensates through spin-orbit coupling

We study the modiﬁcation of the spatial coupling parameter between interacting ballistic exciton-polariton condensates in the presence of photonic spin orbit coupling appearing from TE-TM splitting in planar semiconductor microcavities. We propose a strategy to make the coupling strength between next-nearest-neighbours stronger than between nearest-neighbour, which inverts the con-ventional idea of the spatial coupling hierarchy between sites. Our strategy relies on the dominantly populated high-momentum components in the ballistic condensates which, in the presence of TE-TM splitting, lead to rapid radial precession of the polariton pseudospin. As a consequence, condensate pairs experience distance-periodic screening of their interaction strength, severely modifying their synchronization and condensation threshold solutions.
Stochastic Learning Equation using Monotone Increasing Resolution of Quantization

In this paper, we propose a quantized learning equation with a monotone increasing resolution of quantization and stochastic analysis for the proposed algorithm. According to the white noise hypothesis for the quantization error with dense and uniform distribution, we can regard the quantization error as i.i. d. \ white noise. Based on this, we show that the learning equation with monotonically increasing quantization resolution converges weakly as the distribution viewpoint. The analysis of this paper shows that global optimization is possible for a domain that satisfies the Lipschitz condition instead of local convergence properties such as the Hessian constraint of the objective function.
PerCQA: Persian Community Question Answering Dataset

Community Question Answering (CQA) forums provide answers to many real-life questions. These forums are trendy among machine learning researchers due to their large size. Automatic answer selection, answer ranking, question retrieval, expert finding, and fact-checking are example learning tasks performed using CQA data. This paper presents PerCQA, the first Persian dataset for CQA. This dataset contains the questions and answers crawled from the most well-known Persian forum. After data acquisition, we provide rigorous annotation guidelines in an iterative process and then the annotation of question-answer pairs in SemEvalCQA format. PerCQA contains 989 questions and 21,915 annotated answers. We make PerCQA publicly available to encourage more research in Persian CQA. We also build strong benchmarks for the task of answer selection in PerCQA by using mono- and multi-lingual pre-trained language models.
SOFIA/FORCAST Monitoring of the Dust Emission from R Aqr: Start of the Eclipse

We present mid-infrared (mid-IR) spectra from our continued monitoring of R Aquarii, the nearest symbiotic Mira, using the Stratospheric Observatory for Infrared Astronomy (SOFIA). New photometric and spectroscipic data were obtained with the Faint Object infraRed CAmera for the SOFIA Telescope in 2018 and 2019 after the system had started its “eclipse,” during which it became two magnitudes fainter in the visual. The mid-IR flux, in particular the 10 μm silicate feature, has strengthened compared with the previous cycles. Radiative transfer models for the circumstellar dust emission were calculated for the new spectra, and recalculated for those previously obtained using more appropriate values of the near-IR magnitudes to constrain the properties of the asymptotic giant branch spectra heating the dust. The modeling shows that the luminosity dependence on pulsation phase is not affected by the onset of the eclipse, and that the increase in the mid-IR flux is due to a higher dust density. The models also confirm our earlier results that micron-size grains are present, and that no changes in the grain composition are required to explain the variations in the spectra.
Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy

Large crowd-sourced datasets are often noisy and relation classification (RC) datasets are no exception. Reannotating the entire dataset is one probable solution however it is not always viable due to time and budget constraints. This paper addresses the problem of efficient reannotation of a large noisy dataset for the RC. Our goal is to catch more annotation errors in the dataset while reannotating fewer instances. Existing work on RC dataset reannotation lacks the flexibility about how much data to reannotate. We introduce the concept of a reannotation budget to overcome this limitation. The immediate follow-up problem is: Given a specific reannotation budget, which subset of the data should we reannotate? To address this problem, we present two strategies to selectively reannotate RC datasets. Our strategies utilize the taxonomic hierarchy of relation labels. The intuition of our work is to rely on the graph distance between actual and predicted relation labels in the label hierarchy graph. We evaluate our reannotation strategies on the well-known TACRED dataset. We design our experiments to answer three specific research questions. First, does our strategy select novel candidates for reannotation? Second, for a given reannotation budget is our reannotation strategy more efficient at catching annotation errors? Third, what is the impact of data reannotation on RC model performance measurement? Experimental results show that our both reannotation strategies are novel and efficient. Our analysis indicates that the current reported performance of RC models on noisy TACRED data is inflated.
Extensions of the Chalmers nonlinear HEMT and MESFET model

The ability to simulate temperature, dispersion, and soft-breakdown effects as well as a new /spl alpha/ dependence was added to the Chalmers nonlinear model for high electron mobility transistor (HEMT's) and metal semiconductor field-effect transistor (MESFET's). DC, pulsed dc, low frequency (10 Hz-10 MHz), RF, and small signal S-parameter measurements (1-18 GHz) have been made on a large number of commercial HEMT and MESFET devices from different manufacturers in the temperature range 17-400 K in order to evaluate the validity of the model extensions.
Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia

This survey focuses on the current problems in Earth systems science where machine learning algorithms can be applied. It provides an overview of previous work, ongoing work at the Ministry of Earth Sciences, Gov. of India, and future applications of ML algorithms to some significant earth science problems. We provide a comparison of previous work with this survey, a mind map of multidimensional areas related to machine learning and a Gartner’s hype cycle for machine learning in Earth system science (ESS). We mainly focus on the critical components in Earth Sciences, including atmospheric, Ocean, Seismology, and biosphere, and cover AI/ML applications to statistical downscaling and forecasting problems.
Aligning random graphs with a sub-tree similarity message-passing algorithm

The problem of aligning Erdős–Rényi random graphs is a noisy, average-case version of the graph isomorphism problem, in which a pair of correlated random graphs is observed through a random permutation of their vertices. We study a polynomial time message-passing algorithm devised to solve the inference problem of partially recovering the hidden permutation, in the sparse regime with constant average degrees. We perform extensive numerical simulations to determine the range of parameters in which this algorithm achieves partial recovery. We also introduce a generalized ensemble of correlated random graphs with prescribed degree distributions, and extend the algorithm to this case.
Multi-modal Attention Network for Stock Movements Prediction

Stock prices move as piece-wise trending fluctuation rather than a purely random walk. Traditionally, the prediction of future stock movements is based on the historical trading record. Nowadays, with the development of social media, many active participants in the market choose to publicize their strategies, which provides a window to glimpse over the whole market's attitude towards future movements by extracting the semantics behind social media. However, social media contains conflicting information and cannot replace historical records completely. In this work, we propose a multi-modality attention network to reduce conflicts and integrate semantic and numeric features to predict future stock movements comprehensively. Specifically, we first extract semantic information from social media and estimate their credibility based on posters' identity and public reputation. Then we incorporate the semantic from online posts and numeric features from historical records to make the trading strategy. Experimental results show that our approach outperforms previous methods by a significant margin in both prediction accuracy (61.20\%) and trading profits (9.13\%). It demonstrates that our method improves the performance of stock movements prediction and informs future research on multi-modality fusion towards stock prediction.
Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds

Estimating the structure of vegetation is a crucial first step for many environmental and ecological applications (Daubenmire 1956). In particular, pasture land management requires estimating the occupancy of the different vegetation strata within agricultural parcels. This is a time-consuming undertaking, often performed with in situ ocular approximate measurements. Nowadays, airborne platforms allow public and private actors to gather high accuracy geometric and radiometric data over large areas (Chen 2007). Bolstered by the compelling improvements (Guo et al., 2020) and increased accessibility of deep learning for 3D point clouds, we propose a 3D deep learning method to estimate the occupancy of different vegetation strata from airborne LiDAR and camera sensors. Our method predicts raster occupancy maps for three vegetation strata (lower, medium, and higher) from 3D point clouds. Our training scheme allows our network to only be supervised with aggregated occupancy values at the plot level, which are easier to produce than point or pixel-level annotations. We also propose to use priors on the stratum elevation and the occupancy maps to improve the legibility and interpretability of the resulting maps.
Acoustic scene classification using auditory datasets

The approach used not only challenges some of the fundamental mathematical techniques used so far in early experiments of the same trend but also introduces new scopes and new horizons for interesting results. The physics governing spectrograms have been optimized in the project along with exploring how it handles the intense requirements of the problem at hand. Major contributions and developments brought under the light, through this project involve using better mathematical techniques and problem-specific machine learning methods. Improvised data analysis and data augmentation for audio datasets like frequency masking and random frequency-time stretching are used in the project and hence are explained in this paper. In the used methodology, the audio transforms principle were also tried and explored, and indeed the insights gained were used constructively in the later stages of the project. Using a deep learning principle is surely one of them. Also, in this paper, the potential scopes and upcoming research openings in both short and long term tunnel of time has been presented. Although much of the results gained are domain-specific as of now, they are surely potent enough to produce novel solutions in various different domains of diverse backgrounds.
High-Transmission Neutron Optical Devices Utilizing Micro-Machined Structures

Neutrons are a powerful probe in material science with unique penetrating abilities. A major challenge stems from the fact that neutron optical devices are limited to refractive indices on the order of n≈1±10−5. By exploiting advances in precision manufacturing, we designed and constructed micro-meter period triangular grating with a high-aspect ratio of 14.3. The manufacturing quality is demonstrated with white-light interferometric data and microscope imaging. Neutron-scattering experiment results are presented, showing agreement with refraction modelling. The capabilities of neutron Fresnel prisms and lenses based on this design are contrasted with existing neutron focusing techniques, and the path separation of a prism-based neutron interferometer is estimated.
Adversarial Attack for Asynchronous Event-based Data

Deep neural networks (DNNs) are vulnerable to adversarial examples that are carefully designed to cause the deep learning model to make mistakes. Adversarial examples of 2D images and 3D point clouds have been extensively studied, but studies on event-based data are limited. Event-based data can be an alternative to a 2D image under high-speed movements, such as autonomous driving. However, the given adversarial events make the current deep learning model vulnerable to safety issues. In this work, we generate adversarial examples and then train the robust models for event-based data, for the first time. Our algorithm shifts the time of the original events and generates additional adversarial events. Additional adversarial events are generated in two stages. First, null events are added to the event-based data to generate additional adversarial events. The perturbation size can be controlled with the number of null events. Second, the location and time of additional adversarial events are set to mislead DNNs in a gradient-based attack. Our algorithm achieves an attack success rate of 97.95% on the N-Caltech101 dataset. Furthermore, the adversarial training model improves robustness on the adversarial event data compared to the original model.
PreDisM: Pre-Disaster Modelling With CNN Ensembles for At-Risk Communities

The machine learning community has recently had increased interest in the climate and disaster damage domain due to a marked increased occurrences of natural hazards (e.g., hurricanes, forest fires, floods, earthquakes). However, not enough attention has been devoted to mitigating probable destruction from impending natural hazards. We explore this crucial space by predicting building-level damages on a before-the-fact basis that would allow state actors and non-governmental organizations to be best equipped with resource distribution to minimize or preempt losses. We introduce PreDisM that employs an ensemble of ResNets and fully connected layers over decision trees to capture image-level and meta-level information to accurately estimate weakness of man-made structures to disaster-occurrences. Our model performs well and is responsive to tuning across types of disasters and highlights the space of preemptive hazard damage modelling.
RISE: Rank in Similarity Graph Edge-Count Two-Sample Test

Two-sample hypothesis testing for high-dimensional data is ubiquitous nowadays. Rank-based tests are popular nonparametric methods for univariate data. However, they are diﬃcult to be extended to high-dimensional data. In this paper, we pro-pose a new non-parametric two-sample testing procedure, R ank I n S imilarity graph E dge-count two-sample test (RISE). The new test statistic is constructed on a rank-weighted similarity graph, such as the k -nearest neighbor graph. As a result, RISE can also be applied to non-Euclidean data. Theoretically, we prove that, under some mild conditions, the new test statistic converges to the χ 22 distribution under the per-mutation null distribution, enabling a fast type-I error control. RISE exhibits good power under a wide range of alternatives compared to existing methods, as shown in extensive simulations. The new test is illustrated on the New York City taxi data for comparing travel patterns in consecutive months and a brain network dataset in comparing male and female subjects.
Neuro-Symbolic Hierarchical Rule Induction

We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a set of meta-rules organised in a hierarchical structure, first-order rules are invented by learning embeddings to match facts and body predicates of a meta-rule. To instantiate it, we specifically design an expressive set of generic meta-rules, and demonstrate they generate a consequent fragment of Horn clauses. During training, we inject a controlled \pw{Gumbel} noise to avoid local optima and employ interpretability-regularization term to further guide the convergence to interpretable rules. We empirically validate our model on various tasks (ILP, visual genome, reinforcement learning) against several state-of-the-art methods.
Quantum spin Hall effect from multi-scale band inversion in twisted bilayer Bi$_2$(Te$_{1-x}$Se$_x$)$_3$

Moiré materials have become one of the most active fields in material science in recent years due to their high tunability, and their unique properties emerge from the Moiré-scale structure modulation. Here, we propose twisted bilayer Bi2(Te1−xSex)3 as a new Moiré material where the Moiré-scale modulation induces a topological phase transition. We show, in twisted bilayer Bi2(Te1−xSex)3, a topological insulator domain and a normal insulator domain coexist in the Moiré lattice structure, and edge states on the domain boundary make nearly flat bands that dominate the material properties. The edge states further contribute to a Moiré-scale band inversion, resulting in Moiré-scale topological states. There are corresponding Moiré-scale edge states and they are so to speak “edge state from edge state”, which is a unique feature of twisted bilayer Bi2(Te1−xSex)3. Our result not only proposes novel quantum phases in twisted bilayer Bi2Te3-family, but also suggests the twisting of stacking sensitive topological materials paves an avenue in the search for novel quantum materials and devices.
A Survey on Interpretable Reinforcement Learning

Although deep reinforcement learning has become a promising machine learning approach for sequential decision-making problems, it is still not mature enough for high-stake domains such as autonomous driving or medical applications. In such contexts, a learned policy needs for instance to be interpretable, so that it can be inspected before any deployment (e.g., for safety and verifiability reasons). This survey provides an overview of various approaches to achieve higher interpretability in reinforcement learning (RL). To that aim, we distinguish interpretability (as a property of a model) and explainability (as a post-hoc operation, with the intervention of a proxy) and discuss them in the context of RL with an emphasis on the former notion. In particular, we argue that interpretable RL may embrace different facets: interpretable inputs, interpretable (transition/reward) models, and interpretable decision-making. Based on this scheme, we summarize and analyze recent work related to interpretable RL with an emphasis on papers published in the past 10 years. We also discuss briefly some related research areas and point to some potential promising research directions.
A comparative study on machine learning models combining with outlier detection and balanced sampling methods for credit scoring

Peer-to-peer (P2P) lending platforms have grown rapidly over the past decade as the network infrastructure has improved and the demand for personal lending has grown. Such platforms allow users to create peer-to-peer lending relationships without the help of traditional financial institutions. Assessing the borrowers' credit is crucial to reduce the default rate and benign development of P2P platforms. Building a personal credit scoring machine learning model can effectively predict whether users will repay loans on the P2P platform. And the handling of data outliers and sample imbalance problems can affect the final effect of machine learning models. There have been some studies on balanced sampling methods, but the effect of outlier detection methods and their combination with balanced sampling methods on the effectiveness of machine learning models has not been fully studied. In this paper, the influence of using different outlier detection methods and balanced sampling methods on commonly used machine learning models is investigated. Experiments on 44,487 Lending Club samples show that proper outlier detection can improve the effectiveness of the machine learning model, and the balanced sampling method only has a good effect on a few machine learning models, such as MLP.
A Review of Bayesian Modelling in Glaciology

Bayesian methods for modelling and inference are being increasingly used in the cryospheric sciences, and glaciology in particular. Here, we present a review of recent works in glaciology that adopt a Bayesian approach when conducting an analysis. We organise the chapter into three categories: i) Gaussian-Gaussian models, ii) Bayesian hierarchical models, and iii) Bayesian calibration approaches. In addition, we present two detailed case studies that involve the application of Bayesian hierarchical models in glaciology. The first case study is on the spatial prediction of surface mass balance across the Icelandic mountain glacier Langjökull, and the second is on the prediction of sea-level rise contributions from the Antactcic ice sheet. This chapter is presented in such a way that it is accessible to both statisticians as well as earth scientists. This work will appear as a chapter of the forthcoming book titled Statistical Modelling Using Latent Gaussian Models with Applications in Geophysics and Environmental Sciences, expected to be published by Springer in 2022.