Reviewerly/exHarmony huggingface dataset:
Improved Analysis of Clipping Algorithms for Non-convex Optimization

Gradient clipping is commonly used in training deep neural networks partly due to its practicability in relieving the exploding gradient problem. Recently, \citet{zhang2019gradient} show that clipped (stochastic) Gradient Descent (GD) converges faster than vanilla GD/SGD via introducing a new assumption called $(L_0, L_1)$-smoothness, which characterizes the violent fluctuation of gradients typically encountered in deep neural networks. However, their iteration complexities on the problem-dependent parameters are rather pessimistic, and theoretical justification of clipping combined with other crucial techniques, e.g. momentum acceleration, are still lacking. In this paper, we bridge the gap by presenting a general framework to study the clipping algorithms, which also takes momentum methods into consideration. We provide convergence analysis of the framework in both deterministic and stochastic setting, and demonstrate the tightness of our results by comparing them with existing lower bounds. Our results imply that the efficiency of clipping methods will not degenerate even in highly non-smooth regions of the landscape. Experiments confirm the superiority of clipping-based methods in deep learning tasks.
Global Bootstrapping Neural Network for Entity Set Expansion

Bootstrapping for entity set expansion (ESE) has been studied for a long period, which expands new entities using only a few seed entities as supervision.Recent end-to-end bootstrapping approaches have shown their advantages in information capturing and bootstrapping process modeling.However, due to the sparse supervision problem, previous endto-end methods often only leverage information from near neighborhoods (local semantics) rather than those propagated from the co-occurrence structure of the whole corpus (global semantics).To address this issue, this paper proposes Global Bootstrapping Network (GBN) with the "pre-training and fine-tuning" strategies for effective learning.Specifically, it contains a global-sighted encoder to capture and encode both local and global semantics into entity embedding, and an attention-guided decoder to sequentially expand new entities based on these embeddings.The experimental results show that the GBN learned by "pretraining and fine-tuning" strategies achieves state-of-the-art performance on two bootstrapping datasets.
Learning Retrospective Knowledge with Reverse Reinforcement Learning

We present a Reverse Reinforcement Learning (Reverse RL) approach for representing retrospective knowledge. General Value Functions (GVFs) have enjoyed great success in representing predictive knowledge, i.e., answering questions about possible future outcomes such as "how much fuel will be consumed in expectation if we drive from A to B?". GVFs, however, cannot answer questions like "how much fuel do we expect a car to have given it is at B at time $t$?". To answer this question, we need to know when that car had a full tank and how that car came to B. Since such questions emphasize the influence of possible past events on the present, we refer to their answers as retrospective knowledge. In this paper, we show how to represent retrospective knowledge with Reverse GVFs, which are trained via Reverse RL. We demonstrate empirically the utility of Reverse GVFs in both representation learning and anomaly detection.
IDEAL: Inexact decentralized accelerated augmented Lagrangian method

We introduce a framework for designing primal methods under the decentralized optimization setting where local functions are smooth and strongly convex. Our approach consists of approximately solving a sequence of sub-problems induced by the accelerated augmented Lagrangian method, thereby providing a systematic way for deriving several well-known decentralized algorithms including EXTRA arXiv:1404.6264 and SSDA arXiv:1702.08704. When coupled with accelerated gradient descent, our framework yields a novel primal algorithm whose convergence rate is optimal and matched by recently derived lower bounds. We provide experimental results that demonstrate the effectiveness of the proposed algorithm on highly ill-conditioned problems.
Application of bioecological systems theory to higher education: Best evidence review

Urie Bronfenbrenner’s bioecological theory is an important model to illustrate the complexity of reciprocal interactions between growing persons and their multilevel socioecological milieus. In higher education, students’ achievement may be similarly affected by manifold bioecological systems. The applications of this holistic theory have been widely considered in higher education across the world, but it seems that a simplistic reductionist approach is popular in higher education of Ethiopian to determine educational outputs. The objective of this article is, therefore, pertaining partial application of Bronfenbrenner’s bioecological model to higher education with an intention to surpass students’ learning. This theory may offer useful theoretical frameworks and practical implications for practitioners. Thereby, they could demonstrate comprehensive maneuvers to successfully handle multilevel socioecological factors that counteract students’ learning. Although it is very challenging to apply bioecological theory in a complete manner, implications of key concepts such as a person, microsystem, mesosystem, exosystem, macrosystem, and chronosystem variables have been discussed.
Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration

Gaussian processes and other kernel-based methods are used extensively to construct approximations of multivariate data sets. The accuracy of these approximations is dependent on the data used. This paper presents a computationally efficient algorithm to greedily select training samples that minimize the weighted L p error of kernel-based approximations for a given number of data. The method successively generates nested samples, with the goal of minimizing the error in high probability regions of densities specified by users. The algorithm presented is extremely simple and can be implemented using existing pivoted Cholesky factorization methods. Training samples are generated in batches which allows training data to be evaluated (labeled) in parallel. For smooth kernels, the algorithm performs comparably with the greedy integrated variance design but has significantly lower complexity. Numerical experiments demonstrate the efficacy of the approach for bounded, unbounded, multi-modal and non-tensor product densities. We also show how to use the proposed algorithm to efficiently generate surrogates for inferring unknown model parameters from data using Bayesian inference.
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning.

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is often large. Searching in a large goal space poses difficulties for both high-level subgoal generation and low-level policy learning. In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a $k$-step adjacent region of the current state using an adjacency constraint. We theoretically prove that the proposed adjacency constraint preserves the optimal hierarchical policy in deterministic MDPs, and show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks show that incorporating the adjacency constraint improves the performance of state-of-the-art HRL approaches in both deterministic and stochastic environments.
Online Bitrate Selection for Viewport Adaptive 360-Degree Video Streaming

360-degree video streaming provides users with immersive experience by letting users determine their field-of-views (FoVs) in real time. To efficiently utilize the limited bandwidth resources, recent works have proposed a viewport adaptive 360-degree video streaming model by exploiting the bitrate adaptation in spatial and temporal domains. In this paper, under this video streaming model, we propose an online bitrate selection algorithm to enhance the user’s quality of experience (QoE). This is achieved by characterizing the user’s personalized FoV and real-time downloading capacity in an online fashion. We address the unknown user-specific FoV by introducing the reference FoV and design an online bitrate selection algorithm to learn the difference between the user’s actual FoV and the reference FoV. We prove that as the number of video segments increases, the performance of the proposed online algorithm approaches the optimal performance asymptotically, with a bounded error. We perform trace-driven simulations with real-world datasets. Simulation results show that under the scenario where the available video bitrates are relatively high, our proposed algorithm can improve the user’s viewing quality level between <inline-formula><tex-math notation="LaTeX">$4.2\!-\!29.4$</tex-math></inline-formula> percent and reduce the average intra-segment quality switch by at least 12.4 percent when compared with several existing methods.
Feature Importance Ranking for Deep Learning

Feature importance ranking has become a powerful tool for explainable AI. However, its nature of combinatorial optimization poses a great challenge for deep learning. In this paper, we propose a novel dual-net architecture consisting of operator and selector for discovery of an optimal feature subset of a fixed size and ranking the importance of those features in the optimal subset simultaneously. During learning, the operator is trained for a supervised learning task via optimal feature subset candidates generated by the selector that learns predicting the learning performance of the operator working on different optimal subset candidates. We develop an alternate learning algorithm that trains two nets jointly and incorporates a stochastic local search procedure into learning to address the combinatorial optimization challenge. In deployment, the selector generates an optimal feature subset and ranks feature importance, while the operator makes predictions based on the optimal subset for test data. A thorough evaluation on synthetic, benchmark and real data sets suggests that our approach outperforms several state-of-the-art feature importance ranking and supervised feature selection methods. (Our source code is available: this https URL)
A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression

Existing OIE (Open Information Extraction) algorithms are independent of each other such that there exist lots of redundant works; the featured strategies are not reusable and not adaptive to new tasks. This paper proposes a new pipeline to build OIE systems, where an Open-domain Information eXpression (OIX) task is proposed to provide a platform for all OIE strategies. The OIX is an OIE friendly expression of a sentence without information loss. The generation procedure of OIX contains shared works of OIE algorithms so that OIE strategies can be developed on the platform of OIX as inference operations focusing on more critical problems. Based on the same platform of OIX, the OIE strategies are reusable, and people can select a set of strategies to assemble their algorithm for a specific task so that the adaptability may be significantly increased. This paper focuses on the task of OIX and propose a solution – Open Information Annotation (OIA). OIA is a predicate-function-argument annotation for sentences. We label a data set of sentence-OIA pairs and propose a dependency-based rule system to generate OIA annotations from sentences. The evaluation results reveal that learning the OIA from a sentence is a challenge owing to the complexity of natural language sentences, and it is worthy of attracting more attention from the research community.
Photon-number parity of heralded single photons from a Bragg-reflection waveguide reconstructed loss-tolerantly via moment generating function

Due to their strict photon-number correlation, the twin beams produced in parametric down-conversion (PDC) work well for heralded state generation. Often, however, this state manipulation is distorted by the optical losses in the herald and by the higher photon-number contributions inevitable in the PDC process. In order to find feasible figures of merit for characterizing the heralded states, we investigate their normalized factorial moments of the photon number that can be accessed regardless of the optical losses in the detection. We then perform a measurement of the joint photon statistics of twin beams from a semiconductor Bragg-reflection waveguide with transition-edge sensors acting as photon-number-resolving detectors. We extract the photon-number parity of heralded single photons in a loss-tolerant fashion by utilizing the moment generating function. The photon-number parity is highly practicable in quantum state characterization, since it takes into account the complete photon-number content of the target state.
CCTV based system for detection of anti-virus masks

This paper presents the use of neural networks to detect people with anti-virus masks. The algorithm allows to determine if a person has a correctly worn mask (covered nose and mouth). The use of neural networks has been compared with typical Haar cascade frontal face solutions available in the OpenCV library. The proposed solution has been checked for efficiency and precision, as well as for the minimum resolution requirements of the resulting facial image. The software works on still images as well as on video sequences from computer webcams and CCTV cameras.
Revisiting iso-recursive subtyping

The Amber rules are well-known and widely used for subtyping iso-recursive types. They were first briefly and informally introduced in 1985 by Cardelli in a manuscript describing the Amber language. Despite their use over many years, important aspects of the metatheory of the iso-recursive style Amber rules have not been studied in depth or turn out to be quite challenging to formalize. This paper aims to revisit the problem of subtyping iso-recursive types. We start by introducing a novel declarative specification that we believe captures the “spirit” of Amber-style iso-recursive subtyping. Informally, the specification states that two recursive types are subtypes if all their finite unfoldings are subtypes . The Amber rules are shown to be sound with respect to this declarative specification. We then derive a sound , complete and decidable algorithmic formulation of subtyping that employs a novel double unfolding rule. Compared to the Amber rules, the double unfolding rule has the advantage of: 1) being modular; 2) not requiring reflexivity to be built in; and 3) leading to an easy proof of transitivity of subtyping. This work sheds new insights on the theory of subtyping iso-recursive types, and the new double unfolding rule has important advantages over the original Amber rules for both implementations and metatheoretical studies involving recursive types. All results are mechanically formalized in the Coq theorem prover. As far as we know, this is the first comprehensive treatment of iso-recursive subtyping dealing with unrestricted recursive types in a theorem prover.
PENGUJIAN HIPOTESIS SIMULTAN MODEL REGRESI NONPARAMETRIK SPLINE TRUNCATED DALAM PEMODELAN KASUS EKONOMI

The pattern in a relationship between the response variable and the predictor variable can be known and some cannot be known. In determining the unknown pattern of relationships, nonparametric regression approaches can be used. The nonparametric regression approach is very flexible. One of the most frequently used nonparametric regression approaches is the truncated spline. Truncated splines are polynomial pieces that are segmented and continuous. The purpose of this study is to obtain the best estimator model in the Gini Ratio case against the variables suspected of influencing it, then perform simultaneous hypothesis testing on the nonparametric regression model. The criteria for the goodness of the model use the GCV and R2 values. In the case modeling of the District / City Gini Ratio in East Java Province using a nonparametric regression approach, it was found that the truncated spline estimator with 3 knots points gave quite good results. This is indicated by the coefficient of determination of the truncated spline estimator, which is 84.76%. Based on the results of simultaneous testing, it was found that the open unemployment rate, the percentage of poor people and the rate of economic growth simultaneously had an influence on the Gini Ratio.
COVID-19 Literature Topic-Based Search via Hierarchical NMF

Rachel Grotheer, Longxiu Huang, Yihuan Huang, Alona Kryshchenko, Oleksandr Kryshchenko, Pengyu Li, Xia Li, Elizaveta Rebrova, Kyung Ha, Deanna Needell. Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020. 2020.
Directivity of the half-dome bundengan musical instrument

Bundengan is a traditional musical instrument from Indonesia that has a half-dome structure. It is fitted with clipped strings and long, thin bamboo bars that generate metal-like and drum-like sounds, respectively. In the past, a bundengan would be played for the personal pleasure of its player. Driven by the needs of conservation, nowadays, its use is steered towards stage performance for a larger audience. In this work, we investigate the directivity of the bundengan as one of the important aspects to consider when setting the musical instrument on stage. We measured the sound of plucked strings from four bundengans, made by traditional and contemporary craftsmen. The sound was recorded at a radius of 1 m from the centerline of the base of the instruments and at a height of 0.5 m from the floor, covering a circular angle of 360 deg. The bundengans generally produce comparable directivity patterns. Still, a number of differences are observed, possibly due to the small variations in the size of the domes and the placements of the strings inside the domes. These directivity plots provide the possibility to assess how suitable the bundengan actually is for stage performance. [Work supported by the National Geographic Society.]
IoT Based Crop-Field Monitoring and Precise Irrigation System Using Crop Water Requirement

Existing practices of crop irrigation is manual and based on generic traditional recommendations. Crops when provided lesser water, shows reduced growth and reduced uptake of calcium. Excessive irrigation leads to root death and water wastage. Hence, irrigating crops with precise water becomes an important problem. Towards this objective, an IoT based crop field monitoring and precise irrigation system is proposed that monitors crop-field and computes precise crop water requirement based on its life cycle and climatic conditions. Using this computed crop water requirement, a pump motor is operated automatically whenever soil moisture decreases below permanent welting point. The motor is shut down once the required water is pumped out to crops. The proposed system is installed in a crop-field of brindle plant and the crop is irrigated for 6 months. It is observed that 53% of water has been saved from wastage.
Pairwise-GAN: Pose-Based View Synthesis Through Pair-Wise Training

Three-dimensional face reconstruction is one of the popular applications in computer vision. However, even state-of-the-art models still require frontal face as inputs, restricting its usage scenarios in the wild. A similar dilemma also happens in face recognition. New research designed to recover the frontal face from a single side-pose facial image has emerged. The state-of-the-art in this area is the Face-Transformation generative adversarial network, which is based on the CycleGAN. This inspired our researchwhich explores two models’ performance from pixel transformation in frontal facial synthesis, Pix2Pix and CycleGAN. We conducted the experiments on five different loss functions on Pix2Pix to improve its performance, then followed by proposing a new network Pairwise-GAN in frontal facial synthesis. Pairwise-GAN uses two parallel U-Nets as the generator and PatchGAN as the discriminator. The detailed hyper-parameters are also discussed. Based on the quantitative measurement by face similarity comparison, our results showed that Pix2Pix with L1 loss, gradient difference loss, and identity loss results in 2.72 $$\%$$ of improvement at average similarity compared to the default Pix2Pix model. Additionally, the performance of Pairwise-GAN is 5.4 $$\%$$ better than the CycleGAN, 9.1 $$\%$$ than the Pix2Pix, and 14.22 $$\%$$ than the CR-GAN at the average similarity. More experiment results and codes were released at https://github.com/XuyangSHEN/Pairwise-GAN .
Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks

A Pseudo-Random Number Generator (PRNG) is any algorithm generating a sequence of numbers approximating properties of random numbers. These numbers are widely employed in mid-level cryptography and in software applications. Test suites are used to evaluate the quality of PRNGs by checking statistical properties of the generated sequences. These sequences are commonly represented bit by bit. This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve a partially observable Markov Decision Process (MDP), where the full state is the period of the generated sequence, and the observation at each time-step is the last sequence of bits appended to such states. We use Long-Short Term Memory (LSTM) architecture to model the temporal relationship between observations at different time-steps by tasking the LSTM memory with the extraction of significant features of the hidden portion of the MDP’s states. We show that modeling a PRNG with a partially observable MDP and an LSTM architecture largely improves the results of the fully observable feedforward RL approach introduced in previous work.