Miracle-dz/UniFAR-FTUs huggingface dataset:
Situation of Combustion Utilization of High Sodium Coal

A review of the previous research on higher sodium coal combustion technologies was presented in the paper.According to the extraction procedure,the sodium in coal can be divided into water-soluble sodium,acid-soluble sodium and insoluble sodium and the effects of coal rank,maceral and chlorine on the existence form of sodium were also introduced.Furthermore,the conversion rule of the sodium in coal during the combustion process was summarized.The forms of sodium in coal transforms each other in early stage of combustion and are released in the later stage of combustion.The fouling mechanisms caused by sodium during the coal combustion process can be illustrated as follows:the vaporized sodium at high temperature will condense on water wall and react with the substance in flue gas to form sulphates.The fouling and deposition during the coal combustion should be the comprehensive result of the high-temperature sintering deposition and corrosion due to the sulphates.Finally,the current control technologies for the high sodium coal fouling and deposition were briefly introduced and some potentially promising research topics on the upgrading of the high sodium coal were discussed.
Design of a prototype laser-plasma injector for the DESY-II synchrotron

The present state of progress in laser wakefield acceleration encourages considering it as a practical alternative to conventional particle accelerators. A promising application would be to use a laser plasma accelerator as an injector for a synchrotron light source. The injector could deliver low charge (∼ 100 pC) beams to top up the storage ring and reduce the load on the chain of conventional injectors. Yet, the energy spread and jitter of the laser plasma beam pose a significant difficulty for an efficient injection. In this paper we propose a design of a prototype injector to deliver 500 MeV low-intensity electron bunches to the DESY-II electron synchrotron. The design utilizes presently available conventional accelerator technology, such as a chicane and an X-band radio frequency cavity, to reduce the energy spread and jitter of the electron beam down to a sub-per-mille level.
Multi-kV Class β-Ga₂O₃ MESFETs With a Lateral Figure of Merit Up to 355 MW/cm²

We demonstrate over 3 kV gate-pad-connected field plated (GPFP) <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> lateral MESFETs with high lateral figures of merit (LFOM) using metalorganic vapor phase epitaxy (MOVPE) grown channel layers and regrown ohmic contact layers. Using an improved low-temperature MOVPE selective area epitaxy process, we show that a total contact resistance to the channel as low as <inline-formula> <tex-math notation="LaTeX">$1.4~\Omega $ </tex-math></inline-formula>. mm can be achieved. The GPFP design adopted here using plasma-enhanced chemical vapor deposited (PECVD) SiN<sub>x</sub> dielectric and SiN<sub>x</sub>/SiO<sub>2</sub> wrap-around passivation exhibits up to ~14% improved <inline-formula> <tex-math notation="LaTeX">$\text{R}_{{\text {ON}}}$ </tex-math></inline-formula>, up to ~70% improved breakdown voltage (<inline-formula> <tex-math notation="LaTeX">$\text{V}_{{\text {BR}}}= \text {V}_{{\text {DS}}}-\text {V}_{{\text {GS}}}$ </tex-math></inline-formula>) resulting in up to <inline-formula> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> higher LFOM compared to the non-FP <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> lateral MESFETs. The <inline-formula> <tex-math notation="LaTeX">$\text{V}_{{\text {BR}}}$ </tex-math></inline-formula> (~2.5 kV) and LFOM (355 MW/cm<sup>2</sup>) measured simultaneously in our GPFP <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> lateral MESFET (with <inline-formula> <tex-math notation="LaTeX">$\text{L}_{{\text {GD}}} = {10}\mu \text{m}$ </tex-math></inline-formula>) is the highest value achieved in any depletion-mode <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> lateral device to date.
Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network

Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine learning (PPML). Several PPML models have been proposed based on various FHE schemes and approaches. Although FHE schemes are suitable as tools for implementing PPML models, previous PPML models based on FHE, such as CryptoNet, SEALion, and CryptoDL, are limited to simple and nonstandard types of machine learning models; they have not proven to be efficient and accurate with more practical and advanced datasets. Previous PPML schemes replaced non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and did not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could neither use standard activation functions nor employ large numbers of layers. In this work, we first implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and plaintext model parameters. Instead of replacing the non-arithmetic functions with simple arithmetic functions, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as ReLU and Softmax, with sufficient precision. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate an arbitrary deep learning model on encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.43% identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 92.43%±2.65%, which is quite close to that of the original ResNet-20 CNN model (91.89%). It takes approximately 3 h for inference on a dual Intel Xeon Platinum 8280 CPU (112 cores) with 172 GB of memory. We believe that this opens the possibility of applying FHE to an advanced deep PPML model.
Chandra/LETGS Studies of the Collisional Plasma in 4U 1626–67

We present an analysis of Chandra/LETGS observations of the ultracompact X-ray binary (UCXB)4U 1626–67, continuing our project to analyze the existing Chandra gratings data of this interesting source. The extremely-low-mass, hydrogen-depleted donor star provides a unique opportunity to study the properties and structure of the metal-rich accreted plasma. There are strong, double-peaked emission features of O vii–VIII and Ne ix–X, but no other identified emission lines are detected. Our spectral fit simultaneously models the emission-line profiles and the plasma parameters, using a two-temperature collisionally-ionized plasma. Based on our line-profile fitting, we constrain the inclination of the system to 25–60° and the inner disk radius to ∼1500 gravitational radii, in turn constraining the donor mass to ≲0.026 M ⊙, while our plasma modeling confirms previous reports of high neon abundance in the source, establishing a Ne/O ratio in the system of 0.47 ± 0.04, while simultaneously estimating a very low Fe/O ratio of 0.0042 ± 0.0008 and limiting the Mg/O ratio to less than 1% by number. We discuss these results in light of previous work.
Latent Correlation-Based Multiview Learning and Self-Supervision: A Unifying Perspective

Multiple views of data, both naturally acquired (e.g., image and audio) and artiﬁcially produced (e.g., via adding different noise to data samples), have proven useful in enhancing representation learning. Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artiﬁcial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., SimCLR and Barlow Twins . Both types of approaches often involve learning neural feature extractors such that the embeddings of data exhibit high cross-view correlations. Although intuitive, the effectiveness of correlation-based neural embedding is only empirically validated. This work puts forth a theory-backed framework for unsupervised multiview learning. Our development starts with proposing a multiview model, where each view is a nonlinear mixture of shared and private components. Consequently, the learning problem boils down to shared/private component identiﬁcation and disentanglement. Under this model, latent correlation maximization is shown to guarantee the extraction of the shared components across views (up to certain ambiguities). In addition, the private information in each view can be provably disentangled from the shared using proper regularization design. The method is tested on a series of tasks, e.g., downstream clustering, which all show promising performance. Our development also provides a unifying perspective for understanding various DCCA and SSL schemes.
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation

Although teacher forcing has become the main training paradigm for neural machine translation, it usually makes predictions only conditioned on past information, and hence lacks global planning for the future. To address this problem, we introduce another decoder, called seer decoder, into the encoder-decoder framework during training, which involves future information in target predictions. Meanwhile, we force the conventional decoder to simulate the behaviors of the seer decoder via knowledge distillation. In this way, at test the conventional decoder can perform like the seer decoder without the attendance of it. Experiment results on the Chinese-English, English-German and English-Romanian translation tasks show our method can outperform competitive baselines significantly and achieves greater improvements on the bigger data sets. Besides, the experiments also prove knowledge distillation the best way to transfer knowledge from the seer decoder to the conventional decoder compared to adversarial learning and L2 regularization.
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation

Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire. This paper describes Diffusion-Decoding models with Contrastive representations (D2C), a paradigm for training unconditional variational autoencoders (VAEs) for few-shot conditional image generation. D2C uses a learned diffusion-based prior over the latent representations to improve generation and contrastive self-supervised learning to improve representation quality. D2C can adapt to novel generation tasks conditioned on labels or manipulation constraints, by learning from as few as 100 labeled examples. On conditional generation from new labels, D2C achieves superior performance over state-of-the-art VAEs and diffusion models. On conditional image manipulation, D2C generations are two orders of magnitude faster to produce over StyleGAN2 ones and are preferred by 50% - 60% of the human evaluators in a double-blind study.
Modeling Language Usage and Listener Engagement in Podcasts

While there is an abundance of advice to podcast creators on how to speak in ways that engage their listeners, there has been little data-driven analysis of podcasts that relates linguistic style with engagement. In this paper, we investigate how various factors – vocabulary diversity, distinctiveness, emotion, and syntax, among others – correlate with engagement, based on analysis of the creators’ written descriptions and transcripts of the audio. We build models with different textual representations, and show that the identified features are highly predictive of engagement. Our analysis tests popular wisdom about stylistic elements in high-engagement podcasts, corroborating some pieces of advice and adding new perspectives on others.
Corruption-Robust Offline Reinforcement Learning

We study the adversarial robustness in offline reinforcement learning. Given a batch dataset consisting of tuples $(s, a, r, s')$, an adversary is allowed to arbitrarily modify $\epsilon$ fraction of the tuples. From the corrupted dataset the learner aims to robustly identify a near-optimal policy. We first show that a worst-case $\Omega(d\epsilon)$ optimality gap is unavoidable in linear MDP of dimension $d$, even if the adversary only corrupts the reward element in a tuple. This contrasts with dimension-free results in robust supervised learning and best-known lower-bound in the online RL setting with corruption. Next, we propose robust variants of the Least-Square Value Iteration (LSVI) algorithm utilizing robust supervised learning oracles, which achieve near-matching performances in cases both with and without full data coverage. The algorithm requires the knowledge of $\epsilon$ to design the pessimism bonus in the no-coverage case. Surprisingly, in this case, the knowledge of $\epsilon$ is necessary, as we show that being adaptive to unknown $\epsilon$ is impossible.This again contrasts with recent results on corruption-robust online RL and implies that robust offline RL is a strictly harder problem.
Semi-Verified PAC Learning from the Crowd

We study the problem of crowdsourced PAC learning of threshold functions. This is a challenging problem and only recently have query-efficient algorithms been established under the assumption that a noticeable fraction of the workers are perfect. In this work, we investigate a more challenging case where the majority may behave adversarially and the rest behave as the Massart noise - a significant generalization of the perfectness assumption. We show that under the {semi-verified model} of Charikar et al. (2017), where we have (limited) access to a trusted oracle who always returns correct annotations, it is possible to PAC learn the underlying hypothesis class with a manageable amount of label queries. Moreover, we show that the labeling cost can be drastically mitigated via the more easily obtained comparison queries. Orthogonal to recent developments in semi-verified or list-decodable learning that crucially rely on data distributional assumptions, our PAC guarantee holds by exploring the wisdom of the crowd.
Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning

Commonsense reasoning research has so far been limited to English. We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English. We collect the Mickey corpus, consisting of 561k sentences in 11 different languages, which can be used for analyzing and improving ML-LMs. We propose Mickey Probe, a language-general probing task for fairly evaluating the common sense of popular ML-LMs across different languages. In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 14 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning. To improve the performance beyond English, we propose a simple yet effective method — multilingual contrastive pretraining (MCP). It significantly enhances sentence representations, yielding a large performance gain on both benchmarks (e.g., +2.7% accuracy for X-CSQA over XLM-R_L).
Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition

In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking advantage of these different time and frequency features between the useful signals and the interferences, the interference mitigation is formulated as an optimization problem: a sparse and low-rank decomposition of a Hankel matrix constructed by lifting the measurements. Then, an iterative optimization algorithm is proposed to tackle it by exploiting the Alternating Direction of Multipliers (ADMM) scheme. Compared to the existing methods, the proposed approach does not need to detect the interference and also improves the estimation accuracy of the separated useful signals. Both numerical simulations with point-like targets and experiment results with distributed targets (i.e., raindrops) are presented to demonstrate and verify its performance. The results show that the proposed approach is generally applicable for interference mitigation in both stationary and moving target scenarios.
Some combinatorial properties of splitting trees

We show that splitting forcing does not have the weak Sacks property below any condition, answering a question of Laguzzi, Mildenberger and Stuber-Rousselle. We also show how some partition results for splitting trees hold or fail and we determine the value of cardinal invariants after an ω2-length countable support iteration of splitting forcing.
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning

Anomalies are rare observations (e.g., data records or events) that deviate significantly from the others in the sample. Over the past few decades, research on anomaly mining has received increasing interests due to the implications of these occurrences in a wide range of disciplines - for instance, security, finance, and medicine. For this reason, anomaly detection, which aims to identify these rare observations, has become one of the most vital tasks in the world and has shown its power in preventing detrimental events, such as financial fraud, network intrusions, and social spam. The detection task is typically solved by identifying outlying data points in the feature space, which, inherently, overlooks the relational information in real-world data. At the same time, graphs have been prevalently used to represent the structural/relational information, which raises the graph anomaly detection problem - identifying anomalous graph objects (i.e., nodes, edges and sub-graphs) in a single graph, or anomalous graphs in a set/database of graphs. Conventional anomaly detection techniques cannot tackle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.). However, thanks to the advent of deep learning in breaking these limitations, graph anomaly detection with deep learning has received a growing attention recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. Specifically, we provide a taxonomy that follows a task-driven strategy and categorizes existing work according to the anomalous graph objects that they can detect. We especially focus on the challenges in this research area and discuss the key intuitions, technical details as well as relative strengths and weaknesses of various techniques in each category. From the survey results, we highlight 12 future research directions spanning unsolved and emerging problems introduced by graph data, anomaly detection, deep learning and real-world applications. Additionally, to provide a wealth of useful resources for future studies, we have compiled a set of open-source implementations, public datasets, and commonly-used evaluation metrics. With this survey, our goal is to create a “one-stop-shop” that provides a unified understanding of the problem categories and existing approaches, publicly available hands-on resources, and high-impact open challenges for graph anomaly detection using deep learning.
Unsupervised Place Recognition with Deep Embedding Learning over Radar Videos

We learn, in an unsupervised way, an embedding from sequences of radar images that is suitable for solving place recognition problem using complex radar data. We experiment on 280 km of data and show performance exceeding state-of-the-art supervised approaches, localising correctly 98.38% of the time when using just the nearest database candidate.
Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy

We propose a balanced coarsening scheme for multilevel hypergraph partitioning. In addition, an initial partitioning algorithm is designed to improve the quality of k-way hypergraph partitioning. By assigning vertex weights through the LPT algorithm, we generate a prior hypergraph under a relaxed balance constraint. With the prior hypergraph, we have defined the Wasserstein discrepancy to coordinate the optimal transport of coarsening process. And the optimal transport matrix is solved by Sinkhorn algorithm. Our coarsening scheme fully takes into account the minimization of connectivity metric (objective function). For the initial partitioning stage, we define a normalized cut function induced by Fiedler vector, which is theoretically proved to be a concave function. Thereby, a three-point algorithm is designed to find the best cut under the balance constraint.
Inference with generalizable classifier predictions

This paper addresses the problem of making statistical inference about a population that can only be identified through classifier predictions. The problem is motivated by scientific studies in which human labels of a population are replaced by a classifier. For downstream analysis of the population based on classifier predictions to be sound, the predictions must generalize equally across experimental conditions. In this paper, we formalize the task of statistical inference using classifier predictions, and propose bootstrap procedures to allow inference with a generalizable classifier. We demonstrate the performance of our methods through extensive simulations and a case study with live cell imaging data.
Transparent Model of Unabridged Data (TMUD)

Recent advancements in computational power and algorithms have enabled unabridged data (e.g., raw images or audio) to be used as input in some models (e.g., deep learning). However, the black box nature of such models reduces their likelihood of adoption by marketing scholars. Our paradigm of analysis, the Transparent Model of Unabridged Data (TMUD), enables researchers to investigate the inner workings of such black box models by incorporating an ex ante filtration module and an ex post experimentation module. We empirically demonstrate the TMUD by investigating the role of facial components and sexual dimorphism in face perceptions, which have implications for four marketing contexts: advertisement (perceptions of approachability, trustworthiness, and competence), brand (perceptions of whether a face represents a brand's typical customer), category (perceptions of whether a face represents a category's typical customer), and customer persona (perceptions of whether a face represents the persona of a brand's customer segment). Our results reveal new and useful findings that enrich the existing literature on face perception, most of which is based on abridged attributes (e.g., width of mouth). The TMUD has great potential to be a useful paradigm for generating theoretical insights and may encourage more marketing researchers and practitioners to use unabridged data.