Miracle-dz/UniFAR-FTUs huggingface dataset:
Connecting the Dots Between MLE and RL for Sequence Prediction

Sequence prediction models can be learned from example sequences with a variety of training algorithms. Maximum likelihood learning is simple and efﬁcient, yet can suffer from compounding error at test time. Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efﬁciency. A rich set of other algorithms such as RAML, SPG, and data noising, have also been developed from different perspectives. This paper establishes a formal connection between these algorithms. We present a generalized entropy regularized policy optimization formulation, and show that the apparently distinct algorithms can all be reformulated as special instances of the framework, with the only difference being the conﬁg-urations of a reward function and a couple of hyperparameters. The uniﬁed interpretation offers a systematic view of the varying properties of exploration and learning efﬁciency. Besides, inspired from the framework, we present a new algorithm that dynamically interpolates among the family of algorithms for scheduled sequence model learning. Experiments on machine translation, text summarization, and game imitation learning demonstrate the superiority of the proposed algorithm.
Equation Discovery for Nonlinear System Identification

Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two interlaced parts of modeling that are computer-aided. The first performs computer-aided identification of a model structure composed of elements selected from user-specified domain-specific modeling knowledge, while the second part performs parameter estimation. In this paper, recent developments of the equation discovery method called process-based modeling, suited for nonlinear system identification, are elaborated and illustrated in two continuous-time case studies. The first case study illustrates the use of the process-based modeling on synthetic data while the second case-study evaluates process-based modeling on measured data for a standard system-identification benchmark. The experimental results clearly demonstrate the ability of process-based modeling to reconstruct both model structure and parameters from measured data.
Robust Optimization of Nanometer SRAM Designs

Robust Optimization of Nanometer SRAM Designs. (December 2009) Akshit Dayal, B.E. , Delhi College of Engineering, University of Delhi; M.S., Texas A&M University Chair of Advisory Committee: Dr. Peng Li Technology scaling has been the most obvious choice of designers and chip manufacturing companies to improve the performance of analog and digital circuits. With the ever shrinking technological node, process variations can no longer be ignored and play a significant role in determining the performance of nanoscaled devices. By choosing a worst case design methodology, circuit designers have been very munificent with the design parameters chosen, often manifesting in pessimistic designs with significant area overheads. Significant work has been done in estimating the impact of intra-die process variations on circuit performance, pertinently, noise margin and standby leakage power, for fixed transistor channel dimensions. However, for an optimal, high yield, SRAM cell design, it is absolutely imperative to analyze the impact of process variations at every design point, especially, since the distribution of process variations is a statistically varying parameter and has an inverse correlation with the area of the MOS transistor. Furthermore, the first order analytical models used for optimization of SRAM memories are not as accurate and the impact of voltage and its inclusion as an input, along with other design parameters, is often ignored. In this thesis, the performance parameters of a nano-scaled 6-T SRAM cell are modeled as an accurate, yield aware, empirical polynomial predictor, in the presence of intra-die process variations. The estimated empirical models are used in a constrained non-linear, robust optimization framework to design an SRAM cell, for a 45 nm CMOS technology, having optimal performance, according to bounds specified for the circuit performance parameters, with the objective of minimizing on-chip area. This statistically
Enhanced X-ray emission from candidate Lyman continuum emitting galaxies

X-ray binaries may have helped reionize the early Universe by enabling Lyman continuum escape. We analysed a set of eight local galaxies that are potential Lyman leaking galaxies, identified by a blue colour and weak emission lines, using Chandra X-ray observations. Five of the galaxies feature X-ray sources, while three galaxies are not significantly detected in X-rays. X-ray luminosities were found for the galaxies and X-ray sources. Four of the galaxies have elevated X-ray luminosity versus what would be expected based on star formation rate and metallicity. The presence of detected X-ray sources within the galaxies is found to correlate with the ratio of the star formation rate estimated from the near-ultraviolet flux to that estimated from the infrared. This implies reduced obscuration due to dust in the galaxies with X-ray sources. These results support the idea that X-ray binaries may be an important part of the process of reionziation.
SetGANs: Enforcing Distributional Accuracy in Generative Adversarial Networks

This paper addresses the ability of generative adversarial networks (GANs) to model complex distributions of data in high-dimensional spaces. Our proposition is that the more effective the adversary is in discriminating the output of the generator, the more effective the generator will be at modeling (or generating) the distribution represented by the training data. The most extreme failure of GANs in this context is mode collapse, and there are several proposed methods to address that problem. However, mode collapse is merely a symptom of a more general problem of GANs, where the generator fools the adversary while failing to faithfully model the distribution of the training data. Here, we address the challenge of constructing and evaluating GANs that more effectively represent the input distribution. We introduce an adversarial architecture that processes sets of generated and real samples, and discriminates between the origins of these sets (i.e., training versus generated data) in a flexible, permutation invariant manner. We present quantitative and qualitative results that demonstrate the effectiveness of this approach relative to state-of-the-art methods for avoiding mode collapse.
Helion: Enabling a Natural Perspective of Home Automation

Security researchers have recently discovered significant security and safety issues related to home automation and developed approaches to address them. Such approaches often face design and evaluation challenges which arise from their restricted perspective of home automation that is bounded by the IoT apps they analyze. The challenges of past work can be overcome by relying on a deeper understanding of realistic home automation usage. More specifically, the availability of natural home automation scenarios, i.e., sequences of home automation events that may realistically occur in an end-user's home, could help security researchers design better security/safety systems. This paper presents Helion, a framework for building a natural perspective of home automation. Helion identifies the regularities in user-driven home automation, i.e., from user-driven routines that are increasingly being created by users through intuitive platform UIs. Our intuition for designing Helion is that smart home event sequences created by users exhibit an inherent set of semantic patterns, or naturalness that can be modeled and used to generate valid and useful scenarios. To evaluate our approach, we first empirically demonstrate that this naturalness hypothesis holds, with a corpus of 30,518 home automation events, constructed from 273 routines collected from 40 users. We then demonstrate that the scenarios generated by Helion are reasonable and valid from an end-user perspective, through an evaluation with 16 external evaluators. We further show the usefulness of Helion's scenarios by generating 17 home security/safety policies with significantly less effort than existing approaches. We conclude by discussing key takeaways and future research challenges enabled by Helion's natural perspective of home automation.
Asynchronous Behavior Trees with Memory aimed at Aerial Vehicles with Redundancy in Flight Controller

Complex aircraft systems are becoming a target for automation. For successful operation, they require both efficient and readable mission execution system (MES). Flight control computer (FCC) units, as well as all important subsystems, are often duplicated. Discrete nature of MES does not allow small differences in data flow among redundant FCCs which are acceptable for continuous control algorithms. Therefore, mission state consistency has to be specifically maintained. We present a novel MES which includes FCC state synchronization. To achieve this result we developed the new concept of Asynchronous Behavior Tree with Memory (ABTM) and proposed a state synchronization algorithm. The implemented system was tested and proven to work in a real-time simulation of High Altitude Pseudo Satellite (HAPS) mission.
HTS: A Hardware Task Scheduler for Heterogeneous Systems

As the Moore's scaling era comes to an end, application specific hardware accelerators appear as an attractive way to improve the performance and power efficiency of our computing systems. A massively heterogeneous system with a large number of hardware accelerators along with multiple general purpose CPUs is a promising direction, but pose several challenges in terms of the run-time scheduling of tasks on the accelerators and design granularity of accelerators. This paper addresses these challenges by developing an example heterogeneous system to enable multiple applications to share the available accelerators. We propose to design accelerators at a lower abstraction to enable applications to be broken down into tasks that can be mapped on several accelerators. We observe that several real-life workloads can be broken down into common primitives that are shared across many workloads. Finally, we propose and design a hardware task scheduler inspired by the hardware schedulers in out-of-order superscalar processors to efficiently utilize the accelerators in the system by scheduling tasks in out-of-order and even speculatively. We evaluate the proposed system on both real-life and synthetic benchmarks based on Digital Signal Processing~(DSP) applications. Compared to executing the benchmark on a system with sequential scheduling, proposed scheduler achieves up to 12x improvement in performance.
On consistency types for lattice-based distributed programming languages

Distributed systems address an increasing demand for fast access to resources and provide fault tolerance for data. However, due to scalability requirements, software developers need to trade consistency for performance. For certain data, consistency guarantees may be weakened if application correctness is unaffected. In contrast, data flows from data with weak consistency to data with strong consistency requirements are problematic, since application correctness may be broken. In this paper, we propose LatCalculus, a language and type system for distributed programming languages with replicated data types. There are two principal new features: first, the type system statically enforces noninterference between data types with weak consistency and data types with strong consistency; second, static encapsulation enables replication of object graphs with internal aliasing.
Weight Normalization based Quantization for Deep Neural Network Compression

With the development of deep neural networks, the size of network models becomes larger and larger. Model compression has become an urgent need for deploying these network models to mobile or embedded devices. Model quantization is a representative model compression technique. Although a lot of quantization methods have been proposed, many of them suffer from a high quantization error caused by a long-tail distribution of network weights. In this paper, we propose a novel quantization method, called weight normalization based quantization (WNQ), for model compression. WNQ adopts weight normalization to avoid the long-tail distribution of network weights and subsequently reduces the quantization error. Experiments on CIFAR-100 and ImageNet show that WNQ can outperform other baselines to achieve state-of-the-art performance.
Model checking a C++ software framework: a case study

This paper presents a case study on applying two model checkers, Spin and Divine, to verify key properties of a C++ software framework, known as ADAPRO, originally developed at CERN. Spin was used for verifying properties on the design level. Divine was used for verifying simple test applications that interacted with the implementation. Both model checkers were found to have their own respective sets of pros and cons, but the overall experience was positive. Because both model checkers were used in a complementary manner, they provided valuable new insights into the framework, which would arguably have been hard to gain by traditional testing and analysis tools only. Translating the C++ source code into the modeling language of the Spin model checker helped to find flaws in the original design. With Divine, defects were found in parts of the code base that had already been subject to hundreds of hours of unit tests, integration tests, and acceptance tests. Most importantly, model checking was found to be easy to integrate into the workflow of the software project and bring added value, not only as verification, but also validation methodology. Therefore, using model checking for developing library-level code seems realistic and worth the effort.
Chipscale plasmonic modulators and switches based on metal–insulator–metal waveguides with Ge2Sb2Te5

Abstract. We introduce phase-change material Ge2Sb2Te5 (GST) into metal–insulator–metal (MIM) waveguide systems to realize chipscale plasmonic modulators and switches in the telecommunication band. Benefitting from the high contrast of optical properties between amorphous and crystalline GST, the three proposed structures can act as reconfigurable and nonvolatile modulators and switches with excellent modulation depth 14 dB and fast response time in subnanosecond while possessing small footprints, simple frameworks, and easy fabrication. We provide solutions to design active devices in MIM waveguide systems and can find potential applications in more compact all-optical circuits for information processing and storage.
FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control

In recent years significant progress has been made in dealing with challenging problems using reinforcement learning.Despite its great success, reinforcement learning still faces challenge in continuous control tasks. Conventional methods always compute the derivatives of the optimal goal with a costly computation resources, and are inefficient, unstable and lack of robust-ness when dealing with such tasks. Alternatively, derivative-based methods treat the optimization process as a blackbox and show robustness and stability in learning continuous control tasks, but not data efficient in learning. The combination of both methods so as to get the best of the both has raised attention. However, most of the existing combination works adopt complex neural networks (NNs) as the policy for control. The double-edged sword of deep NNs can yield better performance, but also makes it difficult for parameter tuning and computation. To this end, in this paper we presents a novel method called FiDi-RL, which incorporates deep RL with Finite-Difference (FiDi) policy search.FiDi-RL combines Deep Deterministic Policy Gradients (DDPG)with Augment Random Search (ARS) and aims at improving the data efficiency of ARS. The empirical results show that FiDi-RL can improves the performance and stability of ARS, and provide competitive results against some existing deep reinforcement learning methods
A Joint Optimization Approach of LiDAR-Camera Fusion for Accurate Dense 3-D Reconstructions

Fusing data from LiDAR and camera is conceptually attractive because of their complementary properties. For instance, camera images are of higher resolution and have colors, while LiDAR data provide more accurate range measurements and have a wider field of view. However, the sensor fusion problem remains challenging since it is difficult to find reliable correlations between data of very different characteristics (geometry versus texture, sparse versus dense). This letter proposes an offline LiDAR-camera fusion method to build dense, accurate 3-D models. Specifically, our method jointly solves a bundle adjustment problem and a cloud registration problem to compute camera poses and the sensor extrinsic calibration. In experiments, we show that our method can achieve an average accuracy of 2.7 mm and resolution of 70 points/cm$^2$ by comparing to the ground truth data from a survey scanner. Furthermore, the extrinsic calibration result is discussed and shown to outperform the state-of-the-art method.
A greedy algorithm for sparse precision matrix approximation

Precision matrix estimation is an important problem in statistical data analysis. This paper introduces a fast sparse precision matrix estimation algorithm, namely GISS$^{\rho}$, which is originally introduced for compressive sensing. The algorithm GISS$^{\rho}$ is derived based on $l_1$ minimization while with the computation advantage of greedy algorithms. We analyze the asymptotic convergence rate of the proposed GISS$^{\rho}$ for sparse precision matrix estimation and sparsity recovery properties with respect to the stopping criteria. Finally, we numerically compare GISS$^{\rho}$ to other sparse recovery algorithms, such as ADMM and HTP in three settings of precision matrix estimation. The numerical results show the advantages of the proposed algorithm.
Power Lindley distribution and software metrics

The Lindley distribution and its numerous generalizations are widely used in statistical and engineering practice. Recently, a power transformation of Lindley distribution, called the power Lindley distribution, has been introduced by M. E. Ghitany et al., who initiated the investigation of its properties and possible applications. In this article, new results on the power Lindley distribution are presented. The focus of this work is on the moment-(in)determinacy of the distribution for various values of the parameters. Afterwards, certain applications are provided to describe data sets of software metrics.
Performance of Random Forest Machine Learning Algorithms in Binary Supernovae Classification

We consider random forest machine learning algorithms applied to the classification of Type Ia and core-collapse supernovae (CCSNe) by full light curves. We also quantitatively show the potential of early-epoch classification using the same machine learning techniques. The algorithm uses the shape and magnitude of the light curve peak to determine the classification. This an initial study to essentially determine SN type with as few data points as possible. New all-sky surveys will discover new transients at a rapid rate and decisions will have to be made on very little data which transients to follow-up. Here we present an initial method where as few as five data points near peak, we an identify whether it is a Type Ia or CCSNe with better than 80% accuracy. Furthermore, we provide an introduction into the use of machine learning for classification of astrophysical transients, including potential pitfalls, best practices, and areas in need of future research.
Self-supervised Hyperspectral Image Restoration using Separable Image Prior

Supervised learning with a convolutional neural network is recognized as a powerful means of image restoration. However, most such methods have been designed for application to grayscale and/or color images; therefore, they have limited success when applied to hyperspectral image restoration. This is partially owing to large datasets being difficult to collect, and also the heavy computational load associated with the restoration of an image with many spectral bands. To address this difficulty, we propose a novel self-supervised learning strategy for application to hyperspectral image restoration. Our method automatically creates a training dataset from a single degraded image and trains a denoising network without any clear images. Another notable feature of our method is the use of a separable convolutional layer. We undertake experiments to prove that the use of a separable network allows us to acquire the prior of a hyperspectral image and to realize efficient restoration. We demonstrate the validity of our method through extensive experiments and show that our method has better characteristics than those that are currently regarded as state-of-the-art.
A Capsule Network for Recommendation and Explaining What You Like and Dislike

User reviews contain rich semantics towards the preference of users to features of items. Recently, many deep learning based solutions have been proposed by exploiting reviews for recommendation. The attention mechanism is mainly adopted in these works to identify words or aspects that are important for rating prediction. However, it is still hard to understand whether a user likes or dislikes an aspect of an item according to what viewpoint the user holds and to what extent, without examining the review details. Here, we consider a pair of a viewpoint held by a user and an aspect of an item as a logic unit. Reasoning a rating behavior by discovering the informative logic units from the reviews and resolving their corresponding sentiments could enable a better rating prediction with explanation. To this end, in this paper, we propose a capsule network based model for rating prediction with user reviews, named CARP. For each user-item pair, CARP is devised to extract the informative logic units from the reviews and infer their corresponding sentiments. The model firstly extracts the viewpoints and aspects from the user and item review documents respectively. Then we derive the representation of each logic unit based on its constituent viewpoint and aspect. A sentiment capsule architecture with a novel Routing by Bi-Agreement mechanism is proposed to identify the informative logic unit and the sentiment based representations in user-item level for rating prediction. Extensive experiments are conducted over seven real-world datasets with diverse characteristics. Our results demonstrate that the proposed CARP obtains substantial performance gain over recently proposed state-of-the-art models in terms of prediction accuracy. Further analysis shows that our model can successfully discover the interpretable reasons at a finer level of granularity.