Miracle-dz/UniFAR-FTUs huggingface dataset:
Tunable spin polarization and electronic structure of bottom-up synthesized 
MoSi2N4
 materials

Rajibul Islam, ∗ Barun Ghosh, 3, ∗ Carmine Autieri, Sugata Chowdhury, 5 Arun Bansil, Amit Agarwal, and Bahadur Singh † International Research Centre MagTop, Institute of Physics, Polish Academy of Sciences, Aleja Lotnikow 32/46, PL-02668 Warsaw, Poland Department of Physics, Indian Institute of Technology, Kanpur 208016, India Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA Department of Physics and Astronomy, Howard University, Washington, D.C. 20059, USA IBM-HBCU Quantum Center, Howard University, Washington, D.C. 20059, USA Department of Condensed Matter Physics and Materials Science, Tata Institute of Fundamental Research, Mumbai 400005, India
Learning Algorithms for Regenerative Stopping Problems with Applications to Shipping Consolidation in Logistics

We study regenerative stopping problems in which the system starts anew whenever the controller decides to stop and the long-term average cost is to be minimized. Traditional model-based solutions involve estimating the underlying process from data and computing strategies for the estimated model. In this paper, we compare such solutions to deep reinforcement learning and imitation learning which involve learning a neural network policy from simulations. We evaluate the different approaches on a real-world problem of shipping consolidation in logistics and demonstrate that deep learning can be effectively used to solve such problems.
High tide: a systematic search for ellipsoidal variables in ASAS-SN

The majority of non-merging stellar mass black holes are discovered by observing high energy emission from accretion processes. Here, we pursue the large, but still mostly unstudied population of non-interacting black holes and neutron stars by searching for the tidally induced ellipsoidal variability of their stellar companions. We start from a sample of about 200 000 rotational variables, semiregular variables, and eclipsing binary stars from the All-Sky Automated Survey for Supernovae. We use a χ2 ratio test followed by visual inspection to identify 369 candidates for ellipsoidal variability. We also discuss how to combine the amplitude of the variability with mass and radius estimates for observed stars to calculate a minimum companion mass, identifying the most promising candidates for high mass companions.
Adapting Monolingual Models: Data can be Scarce when Language Similarity is High

For many (minority) languages, the resources needed to train large models are not available. We investigate the performance of zero-shot transfer learning with as little data as possible, and the influence of language similarity in this process. We retrain the lexical layers of four BERT-based models using data from two low-resource target language varieties, while the Transformer layers are independently fine-tuned on a POS-tagging task in the model's source language. By combining the new lexical layers and fine-tuned Transformer layers, we achieve high task performance for both target languages. With high language similarity, 10MB of data appears sufficient to achieve substantial monolingual transfer performance. Monolingual BERT-based models generally achieve higher downstream task performance after retraining the lexical layer than multilingual BERT, even when the target language is included in the multilingual model.
Automated Primary Hyperparathyroidism Screening with Neural Networks

Primary Hyperparathyroidism(PHPT) is a relatively common disease, affecting about one in every 1,000 adults. However, screening for PHPT can be difficult, meaning it often goes undiagnosed for long periods of time. While looking at specific blood test results independently can help indicate whether a patient has PHPT, often these blood result levels can all be within their respective normal ranges despite the patient having PHPT. Based on clinical data from the real world, in this work, we propose a novel approach to screening PHPT with neural network (NN) architectures, achieving over 97% accuracy with common blood values as inputs. Further, we propose a second model achieving over 99% accuracy with additional lab test values as inputs. Moreover, compared to traditional PHPT screening methods, our NN models can reduce the false negatives of traditional screening methods by 99%.
MODS—A USV-Oriented Object Detection and Obstacle Segmentation Benchmark

Small-sized unmanned surface vehicles (USV) are coastal water devices with a broad range of applications such as environmental control and surveillance. A crucial capability for autonomous operation is obstacle detection for timely reaction and collision avoidance, which has been recently explored in the context of camera-based visual scene interpretation. Owing to curated datasets, substantial advances in scene interpretation have been made in a related field of unmanned ground vehicles. However, the current maritime datasets do not adequately capture the complexity of real-world USV scenes and the evaluation protocols are not standardised, which makes cross-paper comparison of different methods difficult and hinders the progress. To address these issues, we introduce a new obstacle detection benchmark MODS, which considers two major perception tasks: maritime object detection and the more general maritime obstacle segmentation. We present a new diverse maritime evaluation dataset containing approximately 81k stereo images synchronized with an on-board IMU, with over 60k objects annotated. We propose a new obstacle segmentation performance evaluation protocol that reflects the detection accuracy in a way meaningful for practical USV navigation. Nineteen recent state-of-the-art object detection and obstacle segmentation methods are evaluated using the proposed protocol, creating a benchmark to facilitate development of the field. The proposed dataset, as well as evaluation routines, are made publicly available at vicos.si/resources.
Future Constraints on the Reionization History and the Ionizing Sources from Gamma-Ray Burst Afterglows

We forecast the reionization history constraints, inferred from Lyα damping wing absorption features, for a future sample of ∼20 z ≥ 6 gamma-ray burst (GRB) afterglows. We describe each afterglow spectrum by a three-parameter model. First, L characterizes the size of the ionized region (the “bubble size”) around a GRB host halo. Second, 〈x H i 〉 is the volume-averaged neutral fraction outside of the ionized bubble around the GRB, which is approximated as spatially uniform. Finally, N H i denotes the column density of a local damped Lyα absorber (DLA) associated with the GRB host galaxy. The size distribution of ionized regions is extracted from a numerical simulation of reionization and evolves strongly across the epoch of reionization (EoR). The model DLA column densities follow the empirical distribution determined from current GRB afterglow spectra. We use a Fisher matrix formalism to forecast the 〈x H i (z)〉 constraints that can be obtained from follow-up spectroscopy of afterglows with S/N = 20 per R = 3000 resolution element at the continuum. We find that the neutral fraction may be determined to better than 10%–15% (1σ) accuracy from this data across multiple independent redshift bins at z ∼ 6–10, spanning much of the EoR, although the precision degrades somewhat near the end of reionization. A more futuristic survey with 80 GRB afterglows at z ≥ 6 can improve the precision here by a factor of 2 and extend measurements out to z ∼ 14. We further discuss how these constraints may be combined with estimates of the escape fraction of ionizing photons derived from the DLA column density distribution toward GRBs extracted at slightly lower redshift, z ∼ 5. This combination will help in testing whether we have an accurate census of the sources that reionized the universe.
TABBIE: Pretrained Representations of Tabular Data

Existing work on tabular representation-learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT. While this joint pretraining improves tasks involving paired tables and text (e.g., answering questions about tables), we show that it underperforms on tasks that operate over tables without any associated text (e.g., populating missing cells). We devise a simple pretraining objective (corrupt cell detection) that learns exclusively from tabular data and reaches the state-of-the-art on a suite of table-based prediction tasks. Unlike competing approaches, our model (TABBIE) provides embeddings of all table substructures (cells, rows, and columns), and it also requires far less compute to train. A qualitative analysis of our model’s learned cell, column, and row representations shows that it understands complex table semantics and numerical trends.
Machine Collaboration

We propose a new ensemble framework for supervised learning, called machine collaboration (MaC), using a collection of possibly heterogeneous base learning methods (hereafter, base machines) for prediction tasks. Unlike bagging/stacking (a parallel and independent framework) and boosting (a sequential and top‐down framework), MaC is a type of circular and recursive learning framework. The circular and recursive nature helps the base machines to transfer information circularly and update their structures and parameters accordingly. The theoretical result on the risk bound of the estimator from MaC reveals that the circular and recursive feature can help MaC reduce risk via a parsimonious ensemble. We conduct extensive experiments on MaC using both simulated data and 119 benchmark real datasets. The results demonstrate that in most cases, MaC performs significantly better than several other state‐of‐the‐art methods, including classification and regression trees, neural networks, stacking, and boosting.
Contextual Bandits with Sparse Data in Web setting

–This paper is a scoping study to identify current methods used in handling sparse data with contextual bandits in web settings. The area is highly current and state of the art methods are identified. The years 2017-2020 are investigated, and 19 method articles are identified, and two review articles. Five categories of methods are described, making it easy to choose how to address sparse data using contextual bandits with a method available for modification in the specific setting of concern. In addition, each method has multiple techniques to choose from for future evaluation. The problem areas are also mentioned that each article covers. An overall updated understanding of sparse data problems using contextual bandits in web settings is given. The identified methods are policy evaluation (off-line and on-line) , hybrid-method, model representation (clusters and deep neural networks), dimensionality reduction, and simulation. Keywords––sparse data, contextual bandit, multi-armed bandit, reinforcement learning, web
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification

It is evident that deep text classification models trained on human data could be biased. In particular, they produce biased outcomes for texts that explicitly include identity terms of certain demographic groups. We refer to this type of bias as explicit bias, which has been extensively studied. However, deep text classification models can also produce biased outcomes for texts written by authors of certain demographic groups. We refer to such bias as implicit bias of which we still have a rather limited understanding. In this paper, we first demonstrate that implicit bias exists in different text classification tasks for different demographic groups. Then, we build a learning-based interpretation method to deepen our knowledge of implicit bias. Specifically, we verify that classifiers learn to make predictions based on language features that are related to the demographic attributes of the authors. Next, we propose a framework Debiased-TC to train deep text classifiers to make predictions on the right features and consequently mitigate implicit bias. We conduct extensive experiments on three real-world datasets. The results show that the text classification models trained under our proposed framework outperform traditional models significantly in terms of fairness, and also slightly in terms of classification performance.
DBNet: A Dual-branch Network Architecture Processing on Spectrum and Waveform for Single-channel Speech Enhancement

In real acoustic environment, speech enhancement is an arduous task to improve the quality and intelligibility of speech interfered by background noise and reverberation. Over the past years, deep learning has shown great potential on speech enhancement. In this paper, we propose a novel real-time framework called DBNet which is a dual-branch structure with alternate interconnection. Each branch incorporates an encoder-decoder architecture with skip connections. The two branches are responsible for spectrum and waveform modeling, respectively. A bridge layer is adopted to exchange information between the two branches. Systematic evaluation and comparison show that the proposed system substantially outperforms related algorithms under very challenging environments. And in INTERSPEECH 2021 Deep Noise Suppression (DNS) challenge, the proposed system ranks the top 8 in real-time track 1 in terms of the Mean Opinion Score (MOS) of the ITU-T P.835 framework.
Targeted Quality Measurement of Health Care Providers

Measuring quality of cancer care delivered by US health providers is challenging. Patients receiving oncology care vary substantially in disease presentation among other key characteristics. In this paper we discuss a framework for institutional quality measurement which addresses the heterogeneity of patient populations. For this, we follow recent statistical developments on health outcomes research and conceptualize the task of quality measurement as a causal inference problem, helping to target flexible covariate profiles that can represent specific populations of interest. To our knowledge, such covariate profiles have not been used in the quality measurement literature. We use different clinically relevant covariate profiles and evaluate methods for layered case-mix adjustments that combine weighting and regression modeling approaches in a sequential manner in order to reduce model extrapolation and allow for provider effect modification. We appraise these methods in an extensive simulation study and highlight the practical utility of weighting methods that warn the investigator when case-mix adjustments are infeasible without some form of extrapolation that goes beyond the support of the data. In our study of cancer-care outcomes, we assess the performance of oncology practices for different profiles that correspond to the types of patients who may receive cancer care. We describe how the methods examined may be particularly important for high-stakes quality measurement, such as public reporting or performance-based payments. These methods may also be applied to support the health care decisions of individual patients and provide a path to personalized quality measurement. ∗Departments of Health Care Policy, Biostatistics, and Statistics, Harvard University, 180 A Longwood Avenue, Office 307-D, Boston, MA 02115; email: zubizarreta@hcp.med.harvard.edu. †Department of Biostatistics, Harvard University, 677 Huntington Avenue, Boston, MA 02115; email: yigeli@g.harvard.edu. ‡Department of Health Care Policy, Harvard University, 180 A Longwood Avenue, Office 201, Boston, MA 02115; email: keating@hcp.med.harvard.edu. §Department of Health Care Policy, Harvard University, 180 A Longwood Avenue, Office 301-A, Boston, MA 02115; email: landrum@hcp.med.harvard.edu. Acknowledgements: The authors thank Ambarish Chattopadhyay, Michael Chernew, Richard Frank, Bijan Niknam, and Alan Zaslavsky for helpful comments and conversations. This study used the linked SEERMedicare database. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the efforts of the National Cancer Institute; the Office of Research, Development and Information, CMS; Information Management Services (IMS), Inc.; and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-Medicare database. The 1 ar X iv :2 10 5. 02 37 9v 2 [ st at . A P] 2 8 O ct 2 02 1
Activity-Aware Deep Cognitive Fatigue Assessment using Wearables

Cognitive fatigue is a common problem among workers which has become an increasing global problem. While existing multi-modal wearable sensors-aided automatic cognitive fatigue monitoring tools have focused on physical and physiological sensors (ECG, PPG, Actigraphy) analytic on specific group of people (say gamers, athletes, construction workers), activity-awareness is utmost importance due to its different responses on physiology in different person. In this paper, we propose a novel framework, Activity-Aware Recurrent Neural Network (AcRoNN), that can generalize individual activity recognition and improve cognitive fatigue estimation significantly. We evaluate and compare our proposed method with state-of-art methods using one real-time collected dataset from 5 individuals and another publicly available dataset from 27 individuals achieving max. 19% improvement over the baseline model.
SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery

Pan-sharpening is a process of merging a high-resolution (HR) panchromatic (PAN) image and its corresponding low-resolution (LR) multi-spectral (MS) image to create an HR-MS and pan-sharpened image. However, due to the different sensors’ locations, characteristics and acquisition time, PAN and MS image pairs often tend to have various amounts of misalignment. Conventional deep-learning-based methods that were trained with such misaligned PAN-MS image pairs suffer from diverse artifacts such as double-edge and blur artifacts in the resultant PAN-sharpened images. In this paper, we propose a novel framework called shift-invariant pan-sharpening with moving object alignment (SIPSA-Net) which is the first method to take into account such large misalignment of moving object regions for PAN sharpening. The SISPA-Net has a feature alignment module (FAM) that can adjust one feature to be aligned to another feature, even between the two different PAN and MS domains. For better alignment in pan-sharpened images, a shift-invariant spectral loss is newly designed, which ignores the inherent misalignment in the original MS input, thereby having the same effect as optimizing the spectral loss with a well-aligned MS image. Extensive experimental results show that our SIPSA-Net can generate pan-sharpened images with remarkable improvements in terms of visual quality and alignment, compared to the state-of-the-art methods.
Bregman circumcenters: applications

Recently, we systematically studied the basic theory of Bregman circumcenters in another paper. In this work, we aim to investigate the application of Bregman circumcenters. Here, we propose the forward Bregman monotonicity which is a generalization of the powerful Fejér monotonicity, and show a weak convergence result of the forward Bregman monotone sequence. We also naturally introduce the Bregman circumcenter mappings associated with a finite set of operators. Then we provide sufficient conditions for the sequence of iterations of the forward Bregman circumcenter mapping to be forward Bregman monotone. Furthermore, we prove that the sequence of iterations of the forward Bregman circumcenter mapping weakly converges to a point in the intersection of the fixed point sets of relevant operators, which reduces to the known weak convergence result of the circumcentered method under the Euclidean distance. In addition, particular examples are provided to illustrate the Bregman isometry and Browder’s demiclosedness principle, and our convergence result. 2020 Mathematics Subject Classification: Primary 90C48, 49M37, 47H05; Secondary 90C25, 65K05, 52A41.
Genetic Algorithms For Extractive Summarization

Most current work in NLP utilizes deep learning, which requires a lot of training data and computational power. This paper investigates the strengths of Genetic Algorithms (GAs) for extractive summarization, as we hypothesized that GAs could construct more efficient solutions for the summarization task due to their relative customizability relative to deep learning models. This is done by building a vocabulary set, the words of which are represented as an array of weights, and optimizing those set of weights with the GA. These weights can be used to build an overall weighting of a sentence, which can then be passed to some threshold for extraction. Our results showed that the GA was able to learn a weight representation that could filter out excessive vocabulary and thus dictate sentence importance based on common English words.
Appropriate basis selection based on Bayesian inference for analyzing measured data reflecting photoelectron wave interference

In this study, we applied Bayesian inference to extended X-ray absorption fine structure (EXAFS) to select an appropriate basis from Fourier, windowed Fourier and advanced Fourier bases, and we extracted magnitude spectra obtained by these bases, which are closely related to a local structure near the target atom. Based on our method, we also estimate optimal physical parameters from EXAFS signals alone using prior physical knowledge, which can generally be realized in condensed systems. To evaluate our method, the well-known EXAFS spectrum of copper was used for the EXAFS data analysis. We found that the advanced Fourier basis is an appropriate basis for the quantitative regression of the EXAFS signal and that the estimation of the Debye-Waller factor can be robustly realized by using the advanced Fourier basis alone. Bayesian inference based on minimal restrictions allows us to not only eliminate some unphysical results but also select an appropriate basis. Bayesian inference enables us to simultaneously select an appropriate basis and optimized physical parameters without FEFF analysis, which allows extraction of magnitude spectra, which are closely related to a local structure, from EXAFS signals alone. These advantages lead to the general usage of Bayesian inference for EXAFS. ∗ igayasu1219@cs.tsukuba. ac.jp
Bandit based centralized matching in two-sided markets for peer to peer lending

Sequential fundraising in two sided online platforms enable peer to peer lending by sequentially bringing potential contributors, each of whose decisions impact other contributors in the market. However, understanding the dynamics of sequential contributions in online platforms for peer lending has been an open ended research question. The centralized investment mechanism in these platforms makes it difficult to understand the implicit competition that borrowers face from a single lender at any point in time. Matching markets are a model of pairing agents where the preferences of agents from both sides in terms of their preferred pairing for transactions can allow to decentralize the market. We study investment designs in two sided platforms using matching markets when the investors or lenders also face restrictions on the investments based on borrower preferences. This situation creates an implicit competition among the lenders in addition to the existing borrower competition, especially when the lenders are uncertain about their standing in the market and thereby the probability of their investments being accepted or the borrower loan requests for projects reaching the reserve price. We devise a technique based on sequential decision making that allows the lenders to adjust their choices based on the dynamics of uncertainty from competition over time. We simulate two sided market matchings in a sequential decision framework and show the dynamics of the lender regret amassed compared to the optimal borrower-lender matching and find that the lender regret depends on the initial preferences set by the lenders which could affect their learning over decision making steps.