Miracle-dz/UniFAR-FTUs huggingface dataset:
Invidious Comparisons: Ranking and Selection as Compound Decisions

There is an innate human tendency, one might call it the “league table mentality,” to construct rankings. Schools, hospitals, sports teams, movies, and myriad other objects are ranked even though their inherent multi‐dimensionality would suggest that—at best—only partial orderings were possible. We consider a large class of elementary ranking problems in which we observe noisy, scalar measurements of merit for n objects of potentially heterogeneous precision and are asked to select a group of the objects that are “most meritorious. ” The problem is naturally formulated in the compound decision framework of Robbins's (1956) empirical Bayes theory, but it also exhibits close connections to the recent literature on multiple testing. The nonparametric maximum likelihood estimator for mixture models (Kiefer and Wolfowitz (1956)) is employed to construct optimal ranking and selection rules. Performance of the rules is evaluated in simulations and an application to ranking U.S. kidney dialysis centers.
Properties of ultralight bosons from heavy quasar spins via superradiance

The mass and the spin of accreting and jetted black holes, at the center of Active Galactic Nuclei (AGNs), can be probed by analyzing their electromagnetic spectra. For this purpose, we use the Spin-Modified Fundamental Plane of black hole activity, which non-linearly connects the following four variables (in the source frame): radio luminosity, X-ray or optical luminosity (via the [OIII] emission line), black hole mass and spin. Taking into account the uncertainties in luminosity measurements, conversion factors, relativistic beaming and physical properties of the AGN system, we derive lower bounds on the spins of a group of heavy, jetted AGNs. Using these results, we study the direct implications on the mass spectrum of the ultra-light particles of scalar (axion-like), vector (dark photon) and tensor types (additional spin-2 particles). We close unexplored gap in the parameter space 10-20-10-19eV. We obtain upper bounds on the axion decay constant (equivalently lower bounds on the self-interaction strength) considering self-interactions could prevent the axion particles entering the instability, and be the reason for non-observation of superradiance. Assuming axion is described by mass and decay constant, we obtain upper limits on what fraction of dark matter can be formed by ultra-light particles and find that single spieces axion-like light particle can constitute at most 10% of the dark matter in the mass range: 10-21 < μ (eV) < 10-17.
NeuXus: A Biosignal Processing and Classification Pipeline for Real-Time Brain-Computer Interaction

In the last few years, Brain-Computer Interfaces (BCI’s) have progressed as an emerging research area in the domain of neurotechnology. This is primarily due to the introduction of low-cost electroencephalographic (EEG) systems that render BCI technology accessible for non-medical research, but also due to the advancements of signal processing and machine learning methods. Consequently, BCI’s can now provide a wide new range of possibilities in the way users interact with a computer system (e.g., neuroadaptive interfaces). However, major challenges must still be addressed for BCI systems to mature into an established communication medium for effective brain-computer interaction. One of the major challenges involves the easy integration of real-time processing pipelines with portable EEG systems for out-of-the-lab use. To date, despite the amount of options current open-source tools provide, most toolboxes focus mainly in extending the processing and classification methods but lack on the ability to provide an easy-to-design yet extensible architecture for ubiquitous use. Here, we present NeuXus, a modular Python toolbox for real-time biosignal processing and pipeline design. NeuXus is open-source and platform independent, providing high-level implementation of processing pipelines for easy BCI design and deployment, including for simultaneous EEG-fMRI acquisitions through real-time gradient artifact correction.
EmotionGIF-IITP-AINLPML: Ensemble-based Automated Deep Neural System for predicting category(ies) of a GIF response

In this paper, we describe the systems submitted by our IITP-AINLPML team in the shared task of SocialNLP 2020, EmotionGIF 2020, on predicting the category(ies) of a GIF response for a given unlabelled tweet. For the round 1 phase of the task, we propose an attention-based Bi-directional GRU network trained on both the tweet (text) and their replies (text wherever available) and the given category(ies) for its GIF response. In the round 2 phase, we build several deep neural-based classifiers for the task and report the final predictions through a majority voting based ensemble technique. Our proposed models attain the best Mean Recall (MR) scores of 52.92% and 53.80% in round 1 and round 2, respectively.
Code Switching Language Model Using Monolingual Training Data

Training a code-switching (CS) language model using only monolingual data is still an ongoing research problem. In this paper, a CS language model is trained using only monolingual training data. As recurrent neural network (RNN) models are best suited for predicting sequential data. In this work, an RNN language model is trained using alternate batches from only monolingual English and Spanish data and the perplexity of the language model is computed. From the results, it is concluded that using alternate batches of monolingual data in training reduced the perplexity of a CS language model. The results were consistently improved using mean square error (MSE) in the output embeddings of RNN based language model. By combining both methods, perplexity is reduced from 299.63 to 80.38. The proposed methods were comparable to the language model fine tune with code-switch training
TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems

We present a data-driven, end-to-end approach to transaction-based dialog systems that performs at near-human levels in terms of verbal response quality and factual grounding accuracy. We show that two essential components of the system produce these results: a sufficiently large and diverse, in-domain labeled dataset, and a neural network-based, pre-trained model that generates both verbal responses and API call predictions. In terms of data, we introduce TicketTalk, a movie ticketing dialog dataset with 23,789 annotated conversations. The conversations range from completely open-ended and unrestricted to more structured, both in terms of their knowledge base, discourse features, and number of turns. In qualitative human evaluations, model-generated responses trained on just 10,000 TicketTalk dialogs were rated to “make sense” 86.5% of the time, almost the same as human responses in the same contexts. Our simple, API-focused annotation schema results in a much easier labeling task making it faster and more cost effective. It is also the key component for being able to predict API calls accurately. We handle factual grounding by incorporating API calls in the training data, allowing our model to learn which actions to take and when. Trained on the same 10,000-dialog set, the model’s API call predictions were rated to be correct 93.9% of the time in our evaluations, surpassing the ratings for the corresponding human labels. We show how API prediction and response generation scores improve as the dataset size incrementally increases from 5000 to 21,000 dialogs. Our analysis also clearly illustrates the benefits of pre-training. To facilitate future work on transaction-based dialog systems, we are publicly releasing the TicketTalk dataset at https://git.io/JL8an.
Multi-grained Trajectory Graph Convolutional Networks for Habit-unrelated Human Motion Prediction

Human motion prediction is an essential part for human-robot collaboration. Unlike most of the existing methods mainly focusing on improving the effectiveness of spatiotemporal modeling for accurate prediction, we take effectiveness and efficiency into consideration, aiming at the prediction quality, computational efficiency and the lightweight of the model. A multi-grained trajectory graph convolutional networks based and lightweight framework is proposed for habit-unrelated human motion prediction. Specifically, we represent human motion as multi-grained trajectories, including joint trajectory and sub-joint trajectory. Based on the advanced representation, multi-grained trajectory graph convolutional networks are proposed to explore the spatiotemporal dependencies at the multiple granularities. Moreover, considering the right-handedness habit of the vast majority of people, a new motion generation method is proposed to generate the motion with left-handedness, to better model the motion with less bias to the human habit. Experimental results on challenging datasets, including Human3.6M and CMU Mocap, show that the proposed model outperforms state-of-the-art with less than 0.12 times parameters, which demonstrates the effectiveness and efficiency of our proposed method.
Stochastic Gradient Variance Reduction by Solving a Filtering Problem

Deep neural networks (DNN) are typically optimized using stochastic gradient descent (SGD). However, the estimation of the gradient using stochastic samples tends to be noisy and unreliable, resulting in large gradient variance and bad convergence. In this paper, we propose \textbf{Filter Gradient Decent}~(FGD), an efficient stochastic optimization algorithm that makes the consistent estimation of the local gradient by solving an adaptive filtering problem with different design of filters. Our method reduces variance in stochastic gradient descent by incorporating the historical states to enhance the current estimation. It is able to correct noisy gradient direction as well as to accelerate the convergence of learning. We demonstrate the effectiveness of the proposed Filter Gradient Descent on numerical optimization and training neural networks, where it achieves superior and robust performance compared with traditional momentum-based methods. To the best of our knowledge, we are the first to provide a practical solution that integrates filtering into gradient estimation by making the analogy between gradient estimation and filtering problems in signal processing. (The code is provided in https://github.com/Adamdad/Filter-Gradient-Decent)
Understanding Age of Information in Large-Scale Wireless Networks

The notion of age-of-information (AoI) is investigated in the context of large-scale wireless networks, in which transmitters need to send a sequence of information packets, which are generated as independent Bernoulli processes, to their intended receivers over a shared spectrum. Due to interference, the rate of packet depletion at any given node is entangled with both the spatial configurations, which determine the path loss, and temporal dynamics, which influence the active states, of the other transmitters, resulting in the queues to interact with each other in both space and time over the entire network. To that end, variants in the packet update frequency affect not just the inter-arrival time but also the departure process, and the impact of such phenomena on the AoI is not well understood. In this paper, we establish a theoretical framework to characterize the AoI performance in the aforementioned setting. Particularly, tractable expressions are derived for both the peak and average AoI under two different transmission protocols, namely the first-come-first-serve (FCFS) and the last-come-first-serve with preemption (LCFS-PR). Additionally, our analysis also accounts for the effects of channel access controls such as ALOHA on the AoI. The accuracy of the analysis is verified via simulations, and based on the theoretical outcomes, we find that: $i$ ) networks operating under LCFS-PR are able to attain smaller values of peak and average AoI than that under FCFS, whereas the gain is more pronounced when the infrastructure is densely deployed, $ii$ ) in sparsely deployed networks, ALOHA with a universally designed channel access probability is not instrumental in reducing the AoI, thus calling for more advanced channel access approaches, and $iii$ ) when the infrastructure is densely rolled out, there exists a non-trivial ALOHA channel access probability that minimizes the peak and average AoI under both FCFS and LCFS-PR.
CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80

Computer-assisted surgery has been developed to enhance surgery correctness and safety. However, researchers and engineers suffer from limited annotated data to develop and train better algorithms. Consequently, the development of fundamental algorithms such as Simultaneous Localization and Mapping (SLAM) is limited. This article elaborates on the efforts of preparing the dataset for semantic segmentation, which is the foundation of many computer-assisted surgery mechanisms. Based on the Cholec80 dataset [3], we extracted 8,080 laparoscopic cholecystectomy image frames from 17 video clips in Cholec80 and annotated the images. The dataset is named CholecSeg8K and its total size is 3GB. Each of these images is annotated at pixel-level for thirteen classes, which are commonly founded in laparoscopic cholecystectomy surgery. CholecSeg8k is released under the license CC BY- NC-SA 4.0.
Goal Reasoning by Selecting Subgoals with Deep Q-Learning

In this work we propose a goal reasoning method which learns to select subgoals with Deep Q-Learning in order to decrease the load of a planner when faced with scenarios with tight time restrictions, such as online execution systems. We have designed a CNN-based goal selection module and trained it on a standard video game environment, testing it on different games (planning domains) and levels (planning problems) to measure its generalization abilities. When comparing its performance with a satisfying planner, the results obtained show both approaches are able to find plans of good quality, but our method greatly decreases planning time. We conclude our approach can be successfully applied to different types of domains (games), and shows good generalization properties when evaluated on new levels (problems) of the same game (domain).
Partial Identifiability in Discrete Data With Measurement Error

When data contains measurement errors, it is necessary to make assumptions relating the observed, erroneous data to the unobserved true phenomena of interest. These assumptions should be justifiable on substantive grounds, but are often motivated by mathematical convenience, for the sake of exactly identifying the target of inference. We adopt the view that it is preferable to present bounds under justifiable assumptions than to pursue exact identification under dubious ones. To that end, we demonstrate how a broad class of modeling assumptions involving discrete variables, including common measurement error and conditional independence assumptions, can be expressed as linear constraints on the parameters of the model. We then use linear programming techniques to produce sharp bounds for factual and counterfactual distributions under measurement error in such models. We additionally propose a procedure for obtaining outer bounds on non-linear models. Our method yields sharp bounds in a number of important settings -- such as the instrumental variable scenario with measurement error -- for which no bounds were previously known.
BaPipe: Exploration of Balanced Pipeline Parallelism for DNN Training

The size of deep neural networks (DNNs) grows rapidly as the complexity of the machine learning algorithm increases. To satisfy the requirement of computation and memory of DNN training, distributed deep learning based on model parallelism has been widely recognized. We propose a new pipeline parallelism training framework, BaPipe, which can automatically explore pipeline parallelism training methods and balanced partition strategies for DNN distributed training. In BaPipe, each accelerator calculates the forward propagation and backward propagation of different parts of networks to implement the intra-batch pipeline parallelism strategy. BaPipe uses a new load balancing automatic exploration strategy that considers the parameters of DNN models and the computation, memory, and communication resources of accelerator clusters. We have trained different DNNs such as VGG-16, ResNet-50, and GNMT on GPU clusters and simulated the performance of different FPGA clusters. Compared with state-of-the-art data parallelism and pipeline parallelism frameworks, BaPipe provides up to 3.2x speedup and 4x memory reduction in various platforms.
Finding Key Structures in MMORPG Graph with Hierarchical Graph Summarization

What are the key structures existing in a large real-world MMORPG (Massively Multiplayer Online Role-Playing Game) graph? How can we compactly summarize an MMORPG graph with hierarchical node labels, considering substructures at different levels of hierarchy? Recent MMORPGs generate complex interactions between entities inducing a heterogeneous graph where each entity has hierarchical labels. Succinctly summarizing a heterogeneous MMORPG graph is crucial to better understand its structure; however it is a challenging task since it needs to handle complex interactions and hierarchical labels efficiently. Although there exist few methods to summarize a large-scale graph, they do not deal with heterogeneous graphs with hierarchical node labels.We propose GSHL, a novel method that summarizes a heterogeneous graph with hierarchical labels. We formulate the encoding cost of hierarchical labels using MDL (Minimum Description Length). GSHL exploits the formulation to identify and segment subgraphs, and discovers compact and consistent structures in the graph. Experiments on a large real-world MMORPG graph with multi-million edges show that GSHL is a useful and scalable tool for summarizing the graph, finding important structures in the graph, and finding similar users.
Fake News Data Collection and Classification: Iterative Query Selection for Opaque Search Engines with Pseudo Relevance Feedback

Retrieving information from an online search engine is the first and most important step in many data mining tasks. Most of the search engines currently available on the web, including all social media platforms, are black-boxes (a.k.a opaque) supporting short keyword queries. In these settings, retrieving all posts and comments discussing a particular news item automatically and at large scales is a challenging task. In this paper, we propose a method for generating short keyword queries given a prototype document. The proposed algorithm interacts with the opaque search engine to iteratively improve the query. It is evaluated on the Twitter TREC Microblog 2012 and TREC-COVID 2019 datasets showing superior performance compared to state of the art and is applied to automatically collect large scale dataset for training machine learning classifiers for fake news detection. The classifiers training on 70,000 labeled news items and more than 61 million associated tweets automatically collected using the proposed method obtained impressive performance of AUC and accuracy of 0.92, and 0.86, respectively.
Bioinspired Microfluidic Assay for In Vitro Modeling of Leukocyte–Endothelium Interactions

Current in vitro models of the leukocyte adhesion cascade cannot be used for real-time studies of the entire leukocyte adhesion cascade, including rolling, adhesion, and migration in a single assay. In this study, we have developed and validated a novel bioinspired microfluidic assay (bMFA) and used it to test the hypothesis that blocking of specific steps in the adhesion/migration cascade significantly affects other steps of the cascade. The bMFA consists of an endothelialized microvascular network in communication with a tissue compartment via a 3 μm porous barrier. Human neutrophils in bMFA preferentially adhered to activated human endothelial cells near bifurcations with rolling and adhesion patterns in close agreement with in vivo observations. Treating endothelial cells with monoclonal antibodies to E-selectin or ICAM-1 or treating neutrophils with wortmannin reduced rolling, adhesion, and migration of neutrophils to 60%, 20%, and 18% of their respective control values. Antibody blocking of specific steps in the adhesion/migration cascade (e.g., mAb to E-selectin) significantly downregulated other steps of the cascade (e.g., migration). This novel in vitro assay provides a realistic human cell based model for basic science studies, identification of new treatment targets, selection of pathways to target validation, and rapid screening of candidate agents.
Rossby numbers of fully convective and partially convective stars

Abstract In this work, we investigate the stellar magnetic activity in the theoretical point of view, through the use of stellar structure and evolution models. We present theoretical values of convective turnover times and Rossby numbers for low-mass stars, calculated with the ATON stellar structure and evolution code. We concentrate our analysis on fully convective and partially convective stars motivated by recent observations of X-ray emission of slowly rotating fully convective stars, which suggest that the presence of a tachocline is not a central key for magnetic fields generation. We investigate the behavior of the convective turnover time evolution, as well as its radial profile inside the star. A discussion about the location where the convective turnover time is calculated in the stellar interior is also addressed. Our theoretical results are compared to observational data from low-mass stars.
The Role of Self-Esteem in the Relationship Between Loneliness and Life Satisfaction in Late Adulthood: Evidence from Poland

Background The life satisfaction of elderly persons has been extensively investigated and discussed. However, a literature review shows that relatively few studies have focused on the effect of loneliness on the life satisfaction of older adults. Some researchers have acknowledged that the character of the relationship between loneliness and life satisfaction is still unclear, and this association is much more complex that it appears to be. Therefore, the main purpose of the current study was to understand how loneliness is associated with satisfaction, and whether self-esteem and educational involvement in the University of the Third Age (U3A) courses can affect this relationship among elderly people. Methods The research was conducted on a group of 179 elderly adults (65% women). We measured loneliness, satisfaction, and self-esteem. The data were collected via the paper-and-pencil format through convenience sampling, just before the COVID-19 pandemic began. Results The results obtained show that loneliness correlated negatively with self-esteem and life satisfaction. Self-esteem was associated positively with life satisfaction. Self-esteem acted as a suppressor between loneliness and life satisfaction. The participation of older adults in U3A moderated the strength of the relationship between loneliness and life satisfaction (direct effect) and between loneliness and life satisfaction mediated by self-esteem (indirect effect). Conclusion The mediatory role of self-esteem and the moderating role of educational activities in the relationship between loneliness and life satisfaction in late adulthood have important developmental and social implications. It appears that although feelings of loneliness and social isolation have a negative association on the life satisfaction of older adults, this relationship may be altered by empowering seniors’ self-esteem through their involvement in lifelong learning. Such a solution supports the functioning of older people also at the social level.