Miracle-dz/UniFAR-FTUs huggingface dataset:
THERMOLUMINESCENCE CHARACTERISTICS OF ZnS:Cu NANOPOSPHORS

The thermoluminescence (TL) characteristics of copper doped zincsulphide nanoparticles under UV radiations have been studied. The average particle size of the nanoparticles was found to be 2 – 3 nm, which is confirmed by TEM micrograph. The TL glow curve shows a single peak at 540K. TL intensity was found optimum for 5 minutes UV exposure. Further increase in the dose results into a decrease of TL intensity. Variation in TL intensity as a function of cerium concentration is studied and 2milliMole is found to be the optimum concentration for TL. The trap parameters namely, activation energy (E), order of kinetics (b) and frequency factor (s) of ZnS:Cu (2mM) sample have been determined using Chen’s method. The order of Kinetics is found to be 1 indicating re-trapping of charge carriers in ZnS:Cu nanoparticles. The PL emission spectrum shows peaks at 369nm, 398 and 518nm when excited at 254nm.
Agent Cognition

Introduction.138 Example Application Domains . 141 Terminology and Cognitive Factors . 141 Embodiment vs. Artifact . 142 Monolothic vs. Multi-Agent Systems . 142 Explanation, Control, and Error . 144 Explanation in Recommender Systems.144 Explanation in Expert Systems . 145 Automation and Error.145 Situation Awareness . 146 SA-Based Agent Transparency.146 Trust, User Experience, and System Perceptions.146 Cognitive Load.147 Cognitive Refection.147 Reported and True Domain Knowledge.147 Introduction to Task Paradigms.148 Movie Recommendation Methodology . 149 Task and User Interface Design . 149 Agent Design.150 ECR Manipulation . 150 Explanation Manipulation . 151 Control Manipulation . 151 Error Manipulation.151 Procedure.151 Accommodating Subjective Decision Making.152 Diner’s Dilemma Methodology . 152 Task and User Interface Design . 153 Agent Design.154 ECR Manipulation . 156 Explanation Manipulation . 156
Characterization of Functionalized Single-Walled Carbon Nanotubes at Individual Nanotube-Thin Bundle Level

Individual and thin bundles of aminopolymer-functionalized single-walled carbon nanotubes (SWNTs) were successfully imaged using high-resolution transmission electron microscopy (HR-TEM). The corresponding electron energy loss spectroscopy (EELS) characterization of the same TEM specimen before and after ex situ thermal defunctionalization confirmed that the nanotube surface was covered with nitrogen-containing functionalities in the functionalized SWNT sample.
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware

Machine learning-based malware detection is known to be vulnerable to adversarial evasion attacks. The state-of-the-art is that there are no effective defenses against these attacks. As a response to the adversarial malware classification challenge organized by the MIT Lincoln Lab and associated with the AAAI-19 Workshop on Artificial Intelligence for Cyber Security (AICS'2019), we propose six guiding principles to enhance the robustness of deep neural networks. Some of these principles have been scattered in the literature, but the others are introduced in this paper for the first time. Under the guidance of these six principles, we propose a defense framework to enhance the robustness of deep neural networks against adversarial malware evasion attacks. By conducting experiments with the Drebin Android malware dataset, we show that the framework can achieve a 98.49% accuracy (on average) against grey-box attacks, where the attacker knows some information about the defense and the defender knows some information about the attack, and an 89.14% accuracy (on average) against the more capable white-box attacks, where the attacker knows everything about the defense and the defender knows some information about the attack. The framework wins the AICS'2019 challenge by achieving a 76.02% accuracy, where neither the attacker (i.e., the challenge organizer) knows the framework or defense nor we (the defender) know the attacks. This gap highlights the importance of knowing about the attack.
Computational simulations of the vibrational properties of glasses

Glasses show vibrational properties that are markedly different to those of crystals which are known as phonons. For example, excess lowfrequency modes (the so-called boson peak), vibrational localization, and strong scattering of phonons have been the most discussed topics, and a theoretical understanding of these phenomena is challenging. To address this problem, computational simulations are a powerful tool, which have been employed by many previous works. In this chapter, we describe simulation methods for studying the vibrational properties of glasses (and any solid-state materials). We first present a method for studying vibrational eigenmodes. Since vibrational motions of particles are excited along eigenmodes, the eigenmodes are fundamental to descriptions of vibrational properties. The eigenmodes in glasses are non-phonon modes in general, and some of them are even localized in space. We next present a method of analysing phonon transport, which is also crucial for understanding vibrational properties. Since phonons are not eigenmodes in glasses, they are decomposed into several different, non-phonon eigenmodes. As a result, phonons in glasses are strongly scattered. In addition, we describe how to analyse the elastic response. The elastic response of glasses is also anomalous with respect to that of crystals. Finally, we briefly introduce recent advances that have been achieved by means of large-scale computational simulations.
High-Level FPGA Accelerator Design for Structured-Mesh-Based Explicit Numerical Solvers

This paper presents a workflow for synthesizing near-optimal FPGA implementations of structured-mesh based stencil applications for explicit solvers. It leverages key characteristics of the application class and its computation-communication pattern and the architectural capabilities of the FPGA to accelerate solvers for high-performance computing applications. Key new features of the workflow are (1) the unification of standard state-of-the-art techniques with a number of high-gain optimizations such as batching and spatial blocking/tiling, motivated by increasing throughput for real-world workloads and (2) the development and use of a predictive analytical model to explore the design space, and obtain resource and performance estimates. Three representative applications are implemented using the design workflow on a Xilinx Alveo U280 FPGA, demonstrating near-optimal performance and over 85% predictive model accuracy. These are compared with equivalent highly-optimized implementations of the same applications on modern HPC-grade GPUs (Nvidia V100), analyzing time to solution, bandwidth, and energy consumption. Performance results indicate comparable runtimes with the V100 GPU, with over 2× energy savings for the largest non-trivial application on the FPGA. Our investigation shows the challenges of achieving high performance on current generation FPGAs compared to traditional architectures. We discuss determinants for a given stencil code to be amenable to FPGA implementation, providing insights into the feasibility and profitability of a design and its resulting performance.
The growth of intracluster light in XCS-HSC galaxy clusters from 0.1 < z < 0.5

We estimate the intracluster light (ICL) component within a sample of 18 clusters detected in the XMM Cluster Survey (XCS) data using the deep (∼26.8 mag) Hyper Suprime-Cam Subaru Strategic Programme data release 1 i-band data. We apply a rest-frame μB = 25 mag arcsec−2 isophotal threshold to our clusters, below which we define light as the ICL within an aperture of RX,500 (X-ray estimate of R500) centred on the brightest cluster galaxy (BCG). After applying careful masking and corrections for flux losses from background subtraction, we recover ∼20 per cent of the ICL flux, approximately four times our estimate of the typical background at the same isophotal level (${\sim}5{{\ \rm per\ cent}}$). We find that the ICL makes up about ${\sim}24{{\ \rm per\ cent}}$ of the total cluster stellar mass on average (∼41 per cent including the flux contained in the BCG within 50 kpc); this value is well matched with other observational studies and semi-analytic/numerical simulations, but is significantly smaller than results from recent hydrodynamical simulations (even when measured in an observationally consistent way). We find no evidence for any links between the amount of ICL flux with cluster mass, but find a growth rate of 2–4 for the ICL between 0.1 < z < 0.5. We conclude that the ICL is the dominant evolutionary component of stellar mass in clusters from z ∼ 1. Our work highlights the need for a consistent approach when measuring ICL alongside the need for deeper imaging, in order to unambiguously measure the ICL across as broad a redshift range as possible (e.g. 10-yr stacked imaging from the Vera C. Rubin Observatory).
Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO With Lens Arrays

The millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays (DLA) have received great attention due to their simple hardware implementation and excellent performance. In this work, we investigate the joint design of beam selection and digital precoding matrices for mmWave MU-MIMO systems with DLA to maximize the sum-rate subject to the transmit power constraint and the constraints of the selection matrix structure. The investigated non-convex problem with discrete variables and coupled constraints is challenging to solve and an efficient framework of joint neural network (NN) design is proposed to tackle it. Specifically, the proposed framework consists of a deep reinforcement learning (DRL)-based NN and a deep-unfolding NN, which are employed to optimize the beam selection and digital precoding matrices, respectively. As for the DRL-based NN, we formulate the beam selection problem as a Markov decision process and a double deep Q-network algorithm is developed to solve it. The base station is considered to be an agent, where the state, action, and reward function are carefully designed. Regarding the design of the digital precoding matrix, we develop an iterative weighted minimum mean-square error algorithm induced deep-unfolding NN, which unfolds this algorithm into a layer-wise structure with introduced trainable parameters. Simulation results verify that this jointly trained NN remarkably outperforms the existing iterative algorithms with reduced complexity and stronger robustness.
Evaluating Fairness in the Presence of Spatial Autocorrelation

In spite of considerable practical importance, current algorithmic fairness literature lacks in technical methods to account for underlying geographic dependency while evaluating or mitigating fairness issues for spatial data. We initiate the study of spatial fairness in this paper, taking the first step towards formalizing this line of quantitative methods. Fairness considerations for spatial data often get confounded by the underlying spatial autocorrelation. We propose hypothesis testing methodology to detect the presence and strength of this effect, then mitigate it using a spatial filtering-based approach—in order to enable application of existing bias detection metrics. We evaluate our proposed methodology through numerical experiments on real and synthetic datasets, demonstrating that in presence of several types of confounding effects due to the underlying spatial structure our testing methods perform well in maintaining low type-II errors and nominal type-I errors.
Optimal Regularity of Mixed Dirichlet-Conormal Boundary Value Problems for Parabolic Operators

We obtain the maximal regularity for the mixed Dirichlet-conormal problem in cylindrical domains with time-dependent separations, which is the first of its kind. The boundary of the domain is assumed to be Reifenberg-flat and the separation is locally sufficiently close to a Lipschitz function of m variables, where m = 0, . , d − 2, with respect to the Hausdorff distance. We consider solutions in both Lp-based Sobolev spaces and Lq,p-based mixed-norm Sobolev spaces.
Label Augmentation via Time-based Knowledge Distillation for Financial Anomaly Detection

Detecting anomalies has become increasingly critical to the financial service industry. Anomalous events are often indicative of illegal activities such as fraud, identity theft, network intrusion, account takeover, and money laundering. Financial anomaly detection use cases face serious challenges due to the dynamic nature of the underlying patterns especially in adversarial environments such as constantly changing fraud tactics. While retraining the models with the new patterns is absolutely essential; keeping up with the rapid changes introduces other challenges as it moves the model away from older patterns or continuously grows the size of the training data. The resulting data growth is hard to manage and it reduces the agility of the models' response to the latest attacks. Due to the data size limitations and the need to track the latest patterns, older time periods are often dropped in practice, which in turn, causes vulnerabilities. In this study, we propose a label augmentation approach to utilize the learning from older models to boost the latest. Experimental results show that the proposed approach provides a significant reduction in training time, while providing potential performance improvement.
Exploring Data and Knowledge combined Anomaly Explanation of Multivariate Industrial Data

The demand for high-performance anomaly detection techniques of IoT data becomes urgent, especially in industry field. The anomaly identification and explanation in time series data is one essential task in IoT data mining. Since that the existing anomaly detection techniques focus on the identification of anomalies, the explanation of anomalies is not well-solved. We address the anomaly explanation problem for multivariate IoT data and propose a 3-step self-contained method in this paper. We formalize and utilize the domain knowledge in our method, and identify the anomalies by the violation of constraints. We propose set-cover-based anomaly explanation algorithms to discover the anomaly events reflected by violation features, and further develop knowledge update algorithms to improve the original knowledge set. Experimental results on real datasets from large-scale IoT systems verify that our method computes high-quality explanation solutions of anomalies. Our work provides a guide to navigate the explicable anomaly detection in both IoT fault diagnosis and temporal data cleaning.
PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing

We present the first multi-task learning model – named PhoNLP – for joint Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. Experiments on Vietnamese benchmark datasets show that PhoNLP produces state-of-the-art results, outperforming a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT (Nguyen and Nguyen, 2020) for each task independently. We publicly release PhoNLP as an open-source toolkit under the Apache License 2.0. Although we specify PhoNLP for Vietnamese, our PhoNLP training and evaluation command scripts in fact can directly work for other languages that have a pre-trained BERT-based language model and gold annotated corpora available for the three tasks of POS tagging, NER and dependency parsing. We hope that PhoNLP can serve as a strong baseline and useful toolkit for future NLP research and applications to not only Vietnamese but also the other languages. Our PhoNLP is available at https://github.com/VinAIResearch/PhoNLP
Development of a respiratory sound labeling software for training a deep learning-based respiratory sound analysis model

Respiratory auscultation can help healthcare professionals detect abnormal respiratory conditions if adventitious lung sounds are heard. The state-of-the-art artificial intelligence technologies based on deep learning show great potential in the development of automated respiratory sound analysis. To train a deep learning-based model, a huge number of accurate labels of normal breath sounds and adventitious sounds are needed. In this paper, we demonstrate the work of developing a respiratory sound labeling software to help annotators identify and label the inhalation, exhalation, and adventitious respiratory sound more accurately and quickly. Our labeling software integrates six features from MATLAB Audio Labeler, and one commercial audio editor, RX7. As of October, 2019, we have labeled 9,765 15- second-long audio files of breathing lung sounds, and accrued 34,095 inhalation labels,18,349 exhalation labels, 13,883 continuous adventitious sounds (CASs) labels and 15,606 discontinuous adventitious sounds (DASs) labels, which are significantly larger than previously published studies. The trained convolutional recurrent neural networks based on these labels showed good performance with F1-scores of 86.0% on inhalation event detection, 51.6% on CASs event detection and 71.4% on DASs event detection. In conclusion, our results show that our proposed respiratory sound labeling software could easily pre-define a label, perform one-click labeling, and overall facilitate the process of accurately labeling. This software helps develop deep learning-based models that require a huge amount of labeled acoustic data.
TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes

MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications. These datatypes are recursively constructed at runtime from primitive Named Types defined in the MPI standard. More recently, the development and deployment of CUDA-aware MPI implementations has encouraged the transition of distributed high-performance MPI codes to use GPUs. Such implementations allow MPI functions to directly operate on GPU buffers, easing integration of GPU compute into MPI codes. This work first presents a novel datatype handling strategy for nested strided datatypes, which finds a middle ground between the specialized or generic handling in prior work. This work also shows that the performance characteristics of non-contiguous data handling can be modeled with empirical system measurements, and used to transparently improve MPI_Send/Recv latency. Finally, despite substantial attention to non-contiguous GPU data and CUDA-aware MPI implementations, good performance cannot be taken for granted. This work demonstrates its contributions through an MPI interposer library, TEMPI. TEMPI can be used with existing MPI deployments without system or application changes. Ultimately, the interposed-library model of this work demonstrates MPI_Pack speedup of up to 242000x and MPI_Send speedup of up to 59000x compared to the MPI implementation deployed on a leadership-class supercomputer. This yields speedup of more than 917x in a 3D halo exchange with 3072 processes.
Reinforcement Learning–based Collective Entity Alignment with Adaptive Features

Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs). For entities to be aligned, existing EA solutions treat them separately and generate alignment results as ranked lists of entities on the other side. Nevertheless, this decision-making paradigm fails to take into account the interdependence among entities. Although some recent efforts mitigate this issue by imposing the 1-to-1 constraint on the alignment process, they still cannot adequately model the underlying interdependence and the results tend to be sub-optimal. To fill in this gap, in this work, we delve into the dynamics of the decision-making process, and offer a reinforcement learning (RL)–based model to align entities collectively. Under the RL framework, we devise the coherence and exclusiveness constraints to characterize the interdependence and restrict collective alignment. Additionally, to generate more precise inputs to the RL framework, we employ representative features to capture different aspects of the similarity between entities in heterogeneous KGs, which are integrated by an adaptive feature fusion strategy. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks and compared against state-of-the-art solutions. The empirical results verify its effectiveness and superiority.
In-situ bandaged Josephson junctions for superconducting quantum processors

Shadow evaporation is commonly used to micro-fabricate the key element of superconducting qubits—the Josephson junction. However, in conventional two-angle deposition circuit topology, unwanted stray Josephson junctions are created which contribute to dielectric loss. So far, this could be avoided by shorting the stray junctions with a so-called bandage layer deposited in an additional lithography step, which may further contaminate the chip surface. Here, we present an improved shadow evaporation technique allowing one to fabricate sub-micrometer-sized Josephson junctions together with bandage layers in a single lithography step. We also show that junction aging is significantly reduced when junction electrodes are passivated in an oxygen atmosphere directly after deposition.
Stromlo Stellar Tracks: Non-solar-scaled Abundances for Massive Stars

We present the Stromlo Stellar Tracks, a set of stellar evolutionary tracks, computed by modifying the Modules for Experiments in Stellar Astrophysics (MESA) 1D stellar evolution package, to fit the Galactic Concordance abundances for hot (T > 8000 K) massive (≥10 M ⊙) main-sequence (MS) stars. Until now, all stellar evolution tracks have been computed at solar, scaled-solar, or α-element-enhanced abundances, and none of these models correctly represent the Galactic Concordance abundances at different metallicities. This paper is the first implementation of Galactic Concordance abundances to the stellar evolution models. The Stromlo tracks cover massive stars (10 ≤ M/M ⊙ ≤ 300) with varying rotations (v/v crit = 0.0, 0.2, 0.4) and a finely sampled grid of metallicities (−2.0 ≤ [Z/H] ≤ +0.5; Δ[Z/H] = 0.1) evolved from the pre-main sequence to the end of 12C burning. We find that the implementation of Galactic Concordance abundances is critical for the evolution of MS, massive hot stars in order to estimate accurate stellar outputs (L, T eff, g), which, in turn, have a significant impact on determining the ionizing photon luminosity budgets. We additionally support prior findings of the importance that rotation plays on the evolution of massive stars and their ionizing budget. The evolutionary tracks for our Galactic Concordance abundance scaling provide a more empirically motivated approach than simple uniform abundance scaling with metallicity for the analysis of H ii regions and have considerable implications for determining nebular emission lines and metallicity. Therefore, it is important to refine existing stellar evolutionary models for comprehensive high-redshift extragalactic studies. The Stromlo tracks are available to the astronomical community.