Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemical Abundance of z ∼ 6 Quasar Broad-Line Regions in the XQR-30 Sample

The elemental abundances in the broad-line regions of high-redshift quasars trace the chemical evolution in the nuclear regions of massive galaxies in the early universe. In this work, we study metallicity-sensitive broad emission-line flux ratios in rest-frame UV spectra of 25 high-redshift (5.8 < z < 7.5) quasars observed with the VLT/X-shooter and Gemini/GNIRS instruments, ranging over $\log \left({\rm {M}_{\rm {BH}}/\rm {M}_{\odot }}\right) = 8.4-9.8$ in black hole mass and $\log \left(\rm {L}_{\rm {bol}}/\rm {erg \, s}^{-1}\right) = 46.7-47.7$ in bolometric luminosity. We fit individual spectra and composites generated by binning across quasar properties: bolometric luminosity, black hole mass, and blueshift of the C iv line, finding no redshift evolution in the emission-line ratios by comparing our high-redshift quasars to lower-redshift (2.0 < z < 5.0) results presented in the literature. Using Cloudy-based locally optimally-emitting cloud photoionisation model relations between metallicity and emission-line flux ratios, we find the observable properties of the broad emission lines to be consistent with emission from gas clouds with metallicity that are at least 2-4 times solar. Our high-redshift measurements also confirm that the blueshift of the C iv emission line is correlated with its equivalent width, which influences line ratios normalised against C iv. When accounting for the C iv blueshift, we find that the rest-frame UV emission-line flux ratios do not correlate appreciably with the black hole mass or bolometric luminosity.
Late multimodal fusion for image and audio music transcription

Music transcription, which deals with the conversion of music sources into a structured digital format, is a key problem for Music Information Retrieval (MIR). When addressing this challenge in computational terms, the MIR community follows two lines of research: music documents, which is the case of Optical Music Recognition (OMR), or audio recordings, which is the case of Automatic Music Transcription (AMT). The different nature of the aforementioned input data has conditioned these fields to develop modality-specific frameworks. However, their recent definition in terms of sequence labeling tasks leads to a common output representation, which enables research on a combined paradigm. In this respect, multimodal image and audio music transcription comprises the challenge of effectively combining the information conveyed by image and audio modalities. In this work, we explore this question at a late-fusion level: we study four combination approaches in order to merge, for the first time, the hypotheses regarding end-to-end OMR and AMT systems in a lattice-based search space. The results obtained for a series of performance scenarios -- in which the corresponding single-modality models yield different error rates -- showed interesting benefits of these approaches. In addition, two of the four strategies considered significantly improve the corresponding unimodal standard recognition frameworks.
Deep transfer learning for system identification using long short-term memory neural networks

Recurrent neural networks (RNNs) have many advantages over more traditional system identification techniques. They may be applied to linear and nonlinear systems, and they require fewer modeling assumptions. However, these neural network models may also need larger amounts of data to learn and generalize. Furthermore, neural networks training is a time-consuming process. Hence, building upon long-short term memory neural networks (LSTM), this paper proposes using two types of deep transfer learning, namely parameter fine-tuning and freezing, to reduce the data and computation requirements for system identification. We apply these techniques to identify two dynamical systems, namely a second-order linear system and a Wiener-Hammerstein nonlinear system. Results show that compared with direct learning, our method accelerates learning by 10% to 50%, which also saves data and computing resources.
Do They Accept or Resist Cybersecurity Measures? Development and Validation of the 13-Item Security Attitude Inventory (SA-13)

We present SA-13, the 13-item Security Attitude inventory. We develop and validate this assessment of cybersecurity attitudes by conducting an exploratory factor analysis, confirmatory factor analysis, and other tests with data from a U.S. Census-weighted Qualtrics panel ( N =209). Beyond a core six indicators of Engagement with Security Measures (SA-Engagement, three items) and Attentiveness to Security Measures (SA-Attentiveness, three items), our SA-13 inventory adds indicators of Resistance to Security Measures (SA-Resistance, four items) and Concernedness with Improving Compliance (SA-Concernedness, three items). SA-13 and the subscales exhibit desirable psychometric qualities; and higher scores on SA-13 and on the SA-Engagement and SA-Attentiveness subscales are associated with higher scores for security behavior intention and for self-reported recent security behaviors. SA-13 and the subscales are useful for researchers and security awareness teams who need a lightweight survey measure of user security attitudes. The composite score of the 13 indicators provides a compact measurement of cybersecurity decisional balance – the degree to which system users’ acceptance of security measures weighs against their resistance in deciding whether to adopt secure behaviors or to fully comply with policies and advice. SA-13 has potential to act as a diagnostic for this decisional balance and to measure the effects of security interventions outside of system log data. p<. 01), Confidence in Using Computers ( r= .350, p<.01), Need for Cognition ( r= .317, p<.01), DoSpeRT Health/Safety Risk-Taking Propensity ( r= -.302, p<.01), and GDMS-Dependence ( r= -.198, p<.01). The scale was not significantly associated with DoSpeRT Health/Safety Risk Perception.
Music Influence Modeling Based on Directed Network Model

Studying the history of music may provide a glimpse into the development of human cre-ativity as we examine the evolutionary and revolutionary trends in music and genres. First, a musical inﬂuence metric was created to construct a directed network of musical inﬂuence. Second, we examined the revolutions and development of musical genres, modeled the similarity, and explored similarities and inﬂuences within and between genres. Hierarchical cluster analysis and time series analysis of genres were used to explore the correlation between genres. Finally, Network Analysis, Semantic Analysis, and Random Forest Model are employed to ﬁnd the revolutionaries.Theabovework was applied to Country music to sort out and analyze its evolution. In studying the connection between music and the social environment, time series analysis is used to determine the impact of social, political, or technological changes on music.
Detecting Vocal Fatigue with Neural Embeddings

Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization. This paper investigates the effectiveness of neural embeddings for the detection of vocal fatigue. We compare x-vectors, ECAPA-TDNN, and wav2vec 2.0 embeddings on a corpus of academic spoken English. Low-dimensional mappings of the data reveal that neural embeddings capture information about the change in vocal characteristics of a speaker during prolonged voice usage. We show that vocal fatigue can be reliably predicted using all three types of neural embeddings after 40 minutes of continuous speaking when temporal smoothing and normalization are applied to the extracted embeddings. We employ support vector machines for classification and achieve accuracy scores of 81% using x-vectors, 85% using ECAPA-TDNN embeddings, and 82% using wav2vec 2.0 embeddings as input features. We obtain an accuracy score of 76%, when the trained system is applied to a different speaker and recording environment without any adaptation.
A New Method for Computing Stationary Distribution and Steady-State Performance Measures of a Continuous-State Markov Chain with a Queuing Application

Applications of stochastic models often involve the evaluation of steady-state performance, which requires solving a set of balance equations. In most cases of interest, the number of equations is infinite or even uncountable. As a result, numerical or analytical solutions are unavailable. This is true even when the system state is one-dimensional. This paper develops a general method for computing stationary distributions and steady-state performance measures of stochastic systems that can be described as continuous-state Markov chains supported on R. The balance equations are numerically solved by properly constructing a proxy Markov chain with finite states. We show the consistency of the approximate solution and provide deterministic non-asymptotic error bounds under the supremum norm. Our finite approximation method is near-optimal among all approximation methods using discrete distributions, including the empirical distributions generated by a simulation approach. We apply the developed method to compute the stationary distribution of virtual waiting time in a G/G/1+G queue and associated performance measures under certain mild but general differentiability and boundedness assumptions on the inter-arrival, service, and patience time distributions. Numerical experiments validate the accuracy and efficiency of our method, and show it outperforms a standard Markov chain Monte Carlo method by several orders of magnitude. The developed method is also significantly more accurate than the available fluid approximations for this queue.
Pengaturan Pendinginan Pada Kondensor Untuk Alat Destilasi Asap Cair

Abstract - Recently, the development of electronic technology is very fast. Behind of those advances, the human resource take part as a developer of electronic technology. Condenser is a cooling tubes used in distillation system to convert steam into water. There are peltier thermoelektrik components in radiator that release waste heat from the condenser tube using water circulation looping system by pump. In testing with combustion process of 10 Kg, coconut shell during 120 minutes at temperature of ± 600 ℃, resulted 1 L (1000 ml) liquid smoke which a result of condensation of combustion vapor. Keywords: condensers, distillation, liquid smoke, peltier thermoelectric Abstrak - Perkembangan teknologi elektronika pada zaman sekarang ini berkembang begitu cepat. Dibalik semua itu terdapat sumber daya manusia sebagai pengembang kemajuan teknologi elektronika. Kondensor adalah tabung pendingin yang digunakan dalam sistem destilasi untuk mengubah uap menjadi air. Dalam radiator terdapat komponen termoelektrik peltier yang berfungsi melepas kalor yang terbuang dari tabung kondensor dengan menggunakan sistem looping sirkulasi air menggunakan pompa. Dalam pengujian dihasilkan asap cair 1 L (1000 ml) yaitu suatu hasil kondensasi dari uap pembakaran bahan tempurung kelapa 10 Kg, pada temperatur dengan proses pembakaran selama 120 Menit. Kata kunci : asap cair, destilasi, kondensor, termoelektrik peltier
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems

A new data-driven method for operator learning of stochastic differential equations(SDE) is proposed in this paper. The central goal is to solve forward and inverse stochastic problems more effectively using limited data. Deep operator network(DeepONet) has been proposed recently for operator learning. Compared to other neural networks to learn functions, it aims at the problem of learning nonlinear operators. However, it can be challenging by using the original model to learn nonlinear operators for high-dimensional stochastic problems. We propose a new multi-resolution autoencoder DeepONet model referred to as MultiAuto-DeepONet to deal with this difficulty with the aid of convolutional autoencoder. The encoder part of the network is designed to reduce the dimensionality as well as discover the hidden features of high-dimensional stochastic inputs. The decoder is designed to have a special structure, i.e. in the form of DeepONet. The first DeepONet in decoder is designed to reconstruct the input function involving randomness while the second one is used to approximate the solution of desired equations. Those two DeepONets has a common branch net and two independent trunk nets. This architecture enables us to deal with multi-resolution inputs naturally. By adding $L_1$ regularization to our network, we found the outputs from the branch net and two trunk nets all have sparse structures. This reduces the number of trainable parameters in the neural network thus making the model more efficient. Finally, we conduct several numerical experiments to illustrate the effectiveness of our proposed MultiAuto-DeepONet model with uncertainty quantification.
Plastic Hinge Length for Lightly Reinforced Rectangular Concrete Walls

ABSTRACT This research investigates the plastic hinge length in lightly reinforced rectangular walls typically found in regions of low-to-moderate seismicity. Poor performance has been exhibited by lightly reinforced concrete walls in past earthquake events. A series of finite element analyses have been carried out which demonstrate that if the longitudinal reinforcement ratio in the wall is below a certain threshold value, there will not be sufficient reinforcement to cause secondary cracking, and instead fracture of the longitudinal reinforcement at a single crack could occur. A plastic hinge length equation has been derived based on the results from the numerical simulations.
Entailment Graph Learning with Textual Entailment and Soft Transitivity

Typed entailment graphs try to learn the entailment relations between predicates from text and model them as edges between predicate nodes. The construction of entailment graphs usually suffers from severe sparsity and unreliability of distributional similarity. We propose a two-stage method, Entailment Graph with Textual Entailment and Transitivity (EGT2). EGT2 learns the local entailment relations by recognizing the textual entailment between template sentences formed by typed CCG-parsed predicates. Based on the generated local graph, EGT2 then uses three novel soft transitivity constraints to consider the logical transitivity in entailment structures. Experiments on benchmark datasets show that EGT2 can well model the transitivity in entailment graph to alleviate the sparsity, and leads to signifcant improvement over current state-of-the-art methods.
Multi-task nonparallel support vector machine for classification

Direct multi-task twin support vector machine (DMTSVM) explores the shared information between multiple correlated tasks, then it produces better generalization performance. However, it contains matrix inversion operation when solving the dual problems, so it costs much running time. Moreover, kernel trick cannot be directly utilized in the nonlinear case. To eﬀectively avoid above problems, a novel multi-task nonparallel support vector machine (MTNPSVM) including linear and nonlinear cases is proposed in this paper. By introducing (cid:15) -insensitive loss instead of square loss in DMTSVM, MTNPSVM eﬀectively avoids matrix inversion operation and takes full advantage of the kernel trick. Theoretical implication of the model is further discussed. To further improve the computational eﬃciency, the alternating direction method of multipliers (ADMM) is employed when solving the dual problem. The computational complexity and convergence of the algorithm are provided. In addition, the property and sensitivity of the parameter in model are further explored. The experimental results on ﬁfteen benchmark datasets and twelve image datasets demonstrate the validity of MTNPSVM in comparison with the state-of-the-art algorithms. Finally, it is applied to real Chinese Wine dataset, and also veriﬁes its eﬀectiveness.
High Equivalent Width of Hα+[N ii] Emission in z ∼ 8 Lyman-break Galaxies from IRAC 5.8 μm Observations: Evidence for Efficient Lyman-continuum Photon Production in the Epoch of Reionization

We measure, for the first time, the median equivalent width (EW) of Hα+[N ii] in star-forming galaxies at z ∼ 8. Our estimate leverages the unique photometric depth of the Spitzer/IRAC 5.8 μm band mosaics (probing ≈5500–7100 Å at z ∼ 8) of the GOODS Reionization Era Wide Area Treasury from Spitzer (GREATS) program. We median-stacked the stamps of 102 Lyman-break galaxies in the 3.6, 4.5, 5.8, and 8.0 μm bands, after carefully removing potential contamination from neighboring sources. We infer an extreme rest-frame EW0(Hα+[N ii]) = 2328−1127+1326 Å from the measured red [3.6] − [5.8] = 0.82 ± 0.27 mag, consistent with young (≲107 yr) average stellar population ages at z ∼ 8. This implies an ionizing photon production efficiency of log(ξion,0/ergHz−1)=25.97−0.28+0.18 . Such a high value for photoproduction, similar to the highest values found at z ≲ 4, indicates that only modest escape fractions f esc ≲ 0.3 (at 2σ) are sufficient for galaxies brighter than M UV < −18 mag to reionize the neutral hydrogen at z ∼ 8. This requirement is relaxed even more to f esc ≤ 0.1 when considering galaxies brighter than M UV ≈ −13 mag, consistent with recent luminosity functions and as typically assumed in studies addressing reionization. These exceptional results clearly indicate that galaxies can be the dominant source of reionizing photons, and provide us with an exciting glimpse into what we might soon learn about the early universe, and particularly about the reionization epoch, from forthcoming JWST/MIRI and NIRCam programs.
Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Despite its technological benefits, the Internet of Things (IoT) has cyber weaknesses due to vulnerabilities in the wireless medium. Machine Larning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. An Advanced Persistent Threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys that fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth bridging the state of the art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.
Detection of Distracted Driver using Convolution Neural Network

— With over 50 million car sales annually and over 1.3 million deaths every year due to motor accidents we have chosen this space. India accounts for 11 per cent of global death in road accidents. Drivers are held responsible for 78% of accidents. Road safety problems in developing countries is a major concern and human behavior is ascribed as one of the main causes and accelerators of road safety problems. Driver distraction has been identified as the main reason for accidents. Distractions can be caused due to reasons such as mobile usage, drinking, operating instruments, facial makeup, social interaction. For the scope of this project, we will focus on building a highly efficient ML model to classify different driver distractions at runtime using computer vision. We would also analyze the overall speed and scalability of the model in order to be able to set it up on an edge device. We use CNN, VGG-16, RestNet50 and ensemble of CNN to predict the classes.
Persistent Memory Objects: Fast and Easy Crash Consistency for Persistent Memory

—DIMM-compatible persistent memory unites mem- ory and storage. Prior works utilize persistent memory either by combining the ﬁlesystem with direct access on memory mapped ﬁles or by managing it as a collection of objects while abolishing the POSIX abstraction. In contrast, we propose retaining the POSIX abstraction and extending it to provide support for persistent memory, using Persistent Memory Objects (PMOs). In this work, we design and implement PMOs, a crash-consistent abstraction for managing persistent memory. We introduce psync, a single system call, that a programmer can use to specify crash consistency points in their code, without needing to orchestrate durability explicitly. When rendering data crash consistent, our design incurs a overhead of ≈ 25% and ≈ 21% for parallel workloads and FileBench, respectively, compared to a system without crash consistency. Compared to NOVA-Fortis, our design provides a speedup of ≈ 1 . 67 × and ≈ 3 × for the two set of benchmarks, respectively.
Coarse-to-Fine Feature Mining for Video Semantic Segmentation

The contextual information plays a core role in semantic segmentation. As for video semantic segmentation, the contexts include static contexts and motional contexts, corresponding to static content and moving content in a video clip, respectively. The static contexts are well exploited in image semantic segmentation by learning multi-scale and global/long-range features. The motional contexts are studied in previous video semantic segmentation. However, there is no research about how to simultaneously learn static and motional contexts which are highly correlated and complementary to each other. To address this problem, we propose a Coarse-to-Fine Feature Mining (CFFM) technique to learn a unified presentation of static contexts and motional contexts. This technique consists of two parts: coarse-to-fine feature assembling and cross-frame feature mining. The former operation prepares data for further processing, enabling the subsequent joint learning of static and motional contexts. The latter operation mines useful information/contexts from the sequential frames to enhance the video contexts of the features of the target frame. The enhanced features can be directly applied for the final prediction. Experimental results on popular benchmarks demonstrate that the proposed CFFM performs favorably against state-of-the-art methods for video semantic segmentation. Our implementation is available at https://github.com/GuoleiSun/VSS-CFFM.
ProbNVS: Fast Novel View Synthesis with Learned Probability-Guided Sampling

Existing state-of-the-art novel view synthesis methods rely on either fairly accurate 3D geometry estimation or sampling of the entire space for neural volumetric rendering, which limit the overall efficiency. In order to improve the rendering efficiency by reducing sampling points without sacrificing rendering quality, we propose to build a novel view synthesis framework based on learned MVS priors that enables general, fast and photo-realistic view synthesis simultaneously. Specifically, fewer but important points are sampled under the guidance of depth probability distributions extracted from the learned MVS architecture. Based on the learned probability-guided sampling, a neural volume rendering module is elaborately devised to fully aggregate source view information as well as the learned scene structures to synthesize photorealistic target view images. Finally, the rendering results in uncertain, occluded and unreferenced regions can be further improved by incorporating a confidence-aware refinement module. Experiments show that our method achieves 15 to 40 times faster rendering compared to state-of-the-art baselines, with strong generalization capacity and comparable high-quality novel view synthesis performance.