Miracle-dz/UniFAR-FTUs huggingface dataset:
Charge imbalance resolved negativity of massless free bosons

In this paper, we consider the computation of symmetry resolved entanglement entropy of two disjoint intervals in the 1+1 dimensional free compactified boson conformal field theory by studying the four-point function of the fluxed twist fields. We obtained the exact scaling function of this four-point function and discussed its decompactification limit. This scaling function was used to obtain the charge imbalance resolved Rényi negativity. We test our analytic formula against exact numerical computation in the complex harmonic chain, finding perfect agreements.
Jointly Dynamic Topic Model for Recognition of Lead-lag Relationship in Two Text Corpora

Topic evolution modeling has received significant attentions in recent decades. Although various topic evolution models have been proposed, most studies focus on the single document corpus. However in practice, we can easily access data from multiple sources and also observe relationships between them. Then it is of great interest to recognize the relationship between multiple text corpora and further utilize this relationship to improve topic modeling. In this work, we focus on a special type of relationship between two text corpora, which we define as the"lead-lag relationship". This relationship characterizes the phenomenon that one text corpus would influence the topics to be discussed in the other text corpus in the future. To discover the lead-lag relationship, we propose a jointly dynamic topic model and also develop an embedding extension to address the modeling problem of large-scale text corpus. With the recognized lead-lag relationship, the similarities of the two text corpora can be figured out and the quality of topic learning in both corpora can be improved. We numerically investigate the performance of the jointly dynamic topic modeling approach using synthetic data. Finally, we apply the proposed model on two text corpora consisting of statistical papers and the graduation theses. Results show the proposed model can well recognize the lead-lag relationship between the two corpora, and the specific and shared topic patterns in the two corpora are also discovered.
The Airframe Digital Twin: Some Challenges to Realization

A new structural modeling concept, the Airframe Digital Twin, for designing and maintaining airframes is proposed. The Airframe Digital Twin is a tail number specific computational model of an individual aircraft. It has the potential to improve the way U.S. Air Force aircraft are managed over their entire lifecycle by creating individualized structural management plans. The Airframe Digital Twin can provide configuration control for each aircraft in the inventory. Through computational simulations performed with an Airframe Digital Twin, it can serve as a virtual health sensor, and provide a forecast of future maintenance needs for an individual aircraft. The Airframe Digital Twin concept is considered possible because of advances in high performance computing. An essential part of the Airframe Digital Twin is the ability to reduce the uncertainty in the model with increasing service experience through Bayesian updating.
COVID-19 Detection through Deep Feature Extraction

The SARS-CoV2 virus has caused a lot of tribulation to the human population. Predictive modeling that can accurately determine whether a person is infected with COVID-19 is imperative. The study proposes a novel approach that utilizes deep feature extraction technique, pre-trained ResNet50 acting as the backbone of the network, combined with Logistic Regression as the head model. The proposed model has been trained on Kaggle COVID-19 Radiography Dataset. The proposed model achieves a cross-validation accuracy of 100% on the COVID-19 and Normal X-Ray image classes. Similarly, when tested on combined three classes, the proposed model achieves 98.84% accuracy.
Off-Policy Correction For Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) provides a framework for problems involving multiple interacting agents. Despite appar-ent similarity to the single-agent case, multi-agent problems are often harder to train and analyze theoretically. In this work, we propose MA-Trace, a new on-policy actor-critic algorithm, which extends V-Trace to the MARL setting. The key advantage of our algorithm is its high scalability in a multi-worker setting. To this end, MA-Trace utilizes importance sampling as an off-policy correction method, which allows distributing the computations with no impact on the quality of training. Furthermore, our algorithm is theoretically grounded – we prove a fixed-point theorem that guarantees convergence. We evaluate the algorithm extensively on the StarCraft Multi-Agent Challenge, a standard benchmark for multi-agent algorithms. MA-Trace achieves high performance on all its tasks and exceeds state-of-the-art results on some of them.
Action Recognition with Domain Invariant Features of Skeleton Image

Due to the fast processing-speed and robustness it can achieve, skeleton-based action recognition has recently received the attention of the computer vision community. The recent Convolutional Neural Network (CNN)-based methods have shown commendable performance in learning spatio-temporal representations for skeleton sequence, which use skeleton image as input to a CNN. Since the CNN-based methods mainly encoding the temporal and skeleton joints simply as rows and columns, respectively, the latent correlation related to all joints may be lost caused by the 2D convolution. To solve this problem, we propose a novel CNN-based method with adversarial training for action recognition. We introduce a two-level domain adversarial learning to align the features of skeleton images from different view angles or subjects, respectively, thus further improve the generalization. We evaluated our proposed method on NTU RGB+D. It achieves competitive results compared with state-of-the-art methods and 2.4%, 1.9%accuracy gain than the baseline for cross-subject and cross-view.
Identifying Weak Subjects using Association Rule Mining

Many educational institutions in India today are concentrating on identifying the weak students and the subjects in which those students are weak in the current semester for improving their student results. They are even appointing a faculty member as a counselor to identify the weak stu- dents and to know in which courses the student is weak. After identifying this, this information will be given to the faculty who is teaching those courses so that he/she can take a special interest on those students or even conduct special classes to those students. In this paper we propose that the data mining technique called association rule mining can be applied to identify the subjects in which the students are weak in the current semester using previous semester's results.
How do kernel-based sensor fusion algorithms behave under high dimensional noise?

We study the behavior of two kernel based sensor fusion algorithms, nonparametric canonical correlation analysis (NCCA) and alternating diffusion (AD), under the nonnull setting that the clean datasets collected from two sensors are modeled by a common low dimensional manifold embedded in a high dimensional Euclidean space and the datasets are corrupted by high dimensional noise. We establish the asymptotic limits and convergence rates for the eigenvalues of the associated kernel matrices assuming that the sample dimension and sample size are comparably large, where NCCA and AD are conducted using the Gaussian kernel. It turns out that both the asymptotic limits and convergence rates depend on the signal-to-noise ratio (SNR) of each sensor and selected bandwidths. On one hand, we show that if NCCA and AD are directly applied to the noisy point clouds without any sanity check, it may generate artificial information that misleads scientists' interpretation. On the other hand, we prove that if the bandwidths are selected adequately, both NCCA and AD can be made robust to high dimensional noise when the SNRs are relatively large.
Potential for reducing the length of net feed intake test by weighing cattle more frequently.

SUMMARY Feed intake and liveweight data on 1X weaned Angus heifers were used in a preliminary evaluation to assess whether growth can be measured more accurately by more frequent weighing of cattle. This would allow a reduction in the length of net feed intake test. Net (residual) feed intake (NFI) is the amount of feed eaten, net of the requirements for maintenance and production. During the NFI test, cattle were fed a pelleted hay/grain diet for 70 days after a 21-day adjustment period. The cattle were weighed on automatic scales in front of the feeders any time they went to feed (continuous weighing). In addition the standard procedure of weekly weighing was employed. Approximately 8 weights per animal per day were obtained by continuous weighing. Phenotypic variances for average daily gain (ADG) were reduced more rapidly through continuous weighing than weekly weighing, indicating possible improvement in the accuracy of measurement of ADG. There was a very strong phenotypic correlation (r B 0.94) between the standard 70 day test with weekly weighing, and any test using continuous weighing which was 42 days or longer for NFI, or 56 days or longer for ADG.
BULLSEYE: A Compact, Camera-Based, Human-Machine Interface

BULLSEYE is a flexible, computer input device that operates by generating and analyzing the image of known props at 60 Hz to determine position, orientation, and user-controlled internal state variables. The compact device, containing both the camera and processing engine, has been built and fielded. This paper provides an overview of the concept and hardware, then examines the problems and design strategies associated with the interrelated areas of the environment, prop design, and color detection.
An End-to-End Framework for Dynamic Crime Profiling of Places

Much effort is being made to ensure the safety of people. One of the main requirements of travellers and city administrators is to have knowledge of places that are more prone to criminal activities. To rate a place as a potential crime location, it needs the past crime history at that location. Such data is not easily available in the public domain, however, it floats around on the Internet in the form of newspaper and social media posts, in an unstructured manner though. Consequently, a large number of works are reported on extracting crime information from news articles, providing piecemeal solutions to the problem. This chapter complements these works by building an end-to-end framework for crime profiling of any given location/area. It customizes individual components of the framework and provides a Spatio-temporal integration of crime information. It develops an automated framework that crawls online news articles, analyzes them, and extracts relevant information to create a crime knowledge base that gets dynamically updated in real-time. The crime density can be easily visualized in the form of a heat map which is generated by the knowledge base. As a case study, it investigates 345448 news articles published by 6 daily English newspapers collected for approximately two years. Experimental results show that the crime profiling matches with the ratings calculated manually by various organizations.
ShufaNet: Classification method for calligraphers who have reached the professional level

The authenticity of calligraphy is significant but difficult task in the realm of art, where the key problem is the few-shot classification of calligraphy. We propose a novel method, ShufaNet ("Shufa"is the pinyin of Chinese calligraphy), to classify Chinese calligraphers' styles based on metric learning in the case of few-shot, whose classification accuracy exceeds the level of students majoring in calligraphy. We present a new network architecture, including the unique expression of the style of handwriting fonts called ShufaLoss and the calligraphy category information as prior knowledge. Meanwhile, we modify the spatial attention module and create ShufaAttention for handwriting fonts based on the traditional Chinese nine Palace thought. For the training of the model, we build a calligraphers' data set. Our method achieved 65% accuracy rate in our data set for few-shot learning, surpassing resNet and other mainstream CNNs. Meanwhile, we conducted battle for calligraphy major students, and finally surpassed them. This is the first attempt of deep learning in the field of calligrapher classification, and we expect to provide ideas for subsequent research.
The momentum constraint equation in parameterised post-Newtonian cosmology

We derive a theory-independent version of the momentum constraint equation for use in cosmology, as a part of the parameterised post-Newtonian cosmology framework. Our equations are constructed by adapting the corresponding quantities from formalisms constructed for testing and constraining gravity in isolated astrophysical systems, thereby extending the domain of applicability of these approaches up to cosmological scales. Our parameterised equations include both scalar and divergenceless-vector gravitational potentials, and can be applied to both conservative and non-conservative theories of gravity. They can also be used to describe the gravitational fields of both non-linear structures and super-horizon perturbations. We apply the parameterised equations we propose to quintessence models of dark energy, as well as scalar-tensor and vector-tensor theories of gravity. We find them to work well in each case. Our equations are highly compact, and are intended to be useful for constraining gravity in a theory-independent fashion in cosmology.
Real-time Human Detection Model for Edge Devices

Building a small-sized fast surveillance system model to fit on limited resource devices is a challenging, yet an important task. Convolutional Neural Networks (CNNs) have replaced traditional feature extraction and machine learning models in detection and classification tasks. Various complex large CNN models are 10 proposed that achieve significant improvement in the accuracy. Lightweight CNN models have been recently introduced for real-time tasks. This paper suggests a CNN-based lightweight model that can fit on a limited edge device such as Raspberry Pi. Our proposed model provides better performance time, smaller size and comparable accuracy with existing method. The model performance is 15 evaluated on multiple benchmark datasets. It is also compared with existing models in terms of size, average processing time, and F-score. Other enhancements for future research are suggested.
Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation

This article presents a simple yet effective two-stage framework for semi-supervised medical image segmentation. Unlike prior state-of-the-art semi-supervised segmentation methods that predominantly rely on pseudo supervision directly on predictions, such as consistency regularization and pseudo labeling, our key insight is to explore the feature representation learning with labeled and unlabeled (i.e., pseudo labeled) images to regularize a more compact and better-separated feature space, which paves the way for low-density decision boundary learning and therefore enhances the segmentation performance. A stage-adaptive contrastive learning method is proposed, containing a boundary-aware contrastive loss that takes advantage of the labeled images in the first stage, as well as a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage. To obtain more accurate prototype estimation, which plays a critical role in prototype-aware contrastive learning, we present an aleatoric uncertainty-aware method to generate higher quality pseudo labels. Aleatoric-uncertainty adaptive (AUA) adaptively regularizes prediction consistency by taking advantage of image ambiguity, which, given its significance, is underexplored by existing works. Our method achieves the best results on three public medical image segmentation benchmarks.
Two Efficient Ridge Solutions for the Incremental Broad Learning System on Added Inputs

This paper proposes the recursive and square-root BLS algorithms to improve the original BLS for new added inputs, which utilize the inverse and inverse Cholesky factor of the Hermitian matrix in the ridge inverse, respectively, to update the ridge solution. The recursive BLS updates the inverse by the matrix inversion lemma, while the square-root BLS updates the upper-triangular inverse Cholesky factor by multiplying it with an upper-triangular intermediate matrix. When the added p training samples are more than the total k nodes in the network, i.e., p>k, the inverse of a sum of matrices is applied to take a smaller matrix inversion or inverse Cholesky factorization. For the distributed BLS with data-parallelism, we introduce the parallel implementation of the square-root BLS, which is deduced from the parallel implementation of the inverse Cholesky factorization. The original BLS based on the generalized inverse with the ridge regression assumes the ridge parameter lamda->0 in the ridge inverse. When lambda->0 is not satisfied, the numerical experiments on the MNIST and NORB datasets show that both the proposed ridge solutions improve the testing accuracy of the original BLS, and the improvement becomes more significant as lambda is bigger. On the other hand, compared to the original BLS, both the proposed BLS algorithms theoretically require less complexities, and are significantly faster in the simulations on the MNIST dataset. The speedups in total training time of the recursive and square-root BLS algorithms over the original BLS are 4.41 and 6.92 respectively when p>k, and are 2.80 and 1.59 respectively when p
Design of triple‐band T–U‐shaped CPW‐FED slot antennas

A triple‐band coplanar waveguide (CPW)‐fed slot antenna design for wireless/mobile communication applications is proposed. This antenna is designed on single‐layer PCB FR4 substrate with permittivity εr = 4.4, loss tangent tan δ = 0.0245, and thickness h = 1.6 mm. The antenna consists of T‐shaped signal strip and U‐shaped conducting strip in the upper side of T‐shaped signal strip. With the simple tuning slot parameters of the ground plane, the proposed antenna is suitable for WLAN 2.4/5.2/5.8, WiMAX 2.5/5.5, and WCDMA 1.92–2.17 GHz operations. The −10 dB bandwidth of the proposed antenna from measurement is 0.85 GHz (1.9–2.75 GHz) in the lower frequency band and 3.1 GHz (3.65–6.75 GHz) in the upper frequency band. The detailed analysis of geometrical parameter has been studied to optimize the performance of the designed antenna. The antenna size of radiation area and ground plane is 50 × 50 mm2. The measured results show a good agreement with the simulated results. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:844–848, 2014
Local additivity revisited

We make a number of simplifications in Gour and Friedland’s proof of local additivity of the minimum output entropy of a quantum channel. We follow them in reframing the question as one about the entanglement entropy of bipartite states associated with a d B × d E matrix. We use a different approach to reduce the general case to that of a square positive definite matrix. We use the integral representation of the log to obtain expressions for the first and second derivatives of the entropy, and then exploit the modular operator and functional calculus to streamline the proof following their underlying strategy. We also extend this result to the maximum relative entropy with respect to a fixed reference state, which has important implications for studying the superadditivity of the capacity of a quantum channel to transmit classical information.
Hydro Generator High Voltage Stator Windings: Part 4 – Type and Routine Production Testing

Abstract A rigorous program of type and routine testing is applied before and during the manufacturing of high voltage (HV) hydro generator stator windings. The purpose of type tests is to qualify the insulation system as suitable for its purpose, and the purpose of routine production testing is to ensure there are no manufacturing defects and deficiencies built into the coils during the manufacturing process. The testing for hydro generator windings is often specified by the purchaser, and quite commonly includes accelerated ageing tests. This paper outlines hydro generator winding type and routine production testing most commonly encountered in modern practice. The electrical tests carried out during and immediately after the winding installation are briefly covered at the end of this paper. The described tests apply equally to the resin rich coils and coils that are Vacuum Pressure Impregnated (VPI) in the coil shop before installation. Given the large physical sizes of hydro generators, globally processed VPI stators are rarely encountered and therefore not covered here.
Sociodemographic and clinical characteristics in child and youth mental health; comparison of routine outcome measurements of an Australian and Dutch outpatient cohort

Routine outcome measurement (ROM) data offer unique opportunities to study treatment outcomes in clinical practice, and can help to assess the real-world impact of mental health services for children and adolescents (youth). This is illustrated by studies using naturalistic data from specialist child and adolescent mental healthcare services (CAMHS), showing the proportion of patients with reliable improvement, recovery or deterioration (Burgess et al., 2015; Wolpert et al., 2016), and revealing specific subgroups of patients with greater risk of poor outcome (Garralda et al., 2000; Lundh et al., 2013; Murphy et al., 2015; Edbrooke-Childs et al., 2017). Naturalistic data are therefore undeniably necessary in addition to data derived from randomised clinical trials, which often have limited generalisability due to strict selection criteria (Rothwell, 2005; Van Noorden et al., 2014).
The interaction between suicidal ideation, insomnia symptoms, and student status

Abstract Evidence demonstrates increased vulnerability to thoughts and behaviors related to suicide (i.e., suicidal ideation) in students. This study examined the interaction between insomnia-symptoms and student-status (students vs. non-students) on reports of suicidal thoughts of behaviors. A total of 363 (N = 363) university students and 300 (N = 300) members of the general population provided complete data on measures of insomnia-symptoms and suicidal ideation. Students indicated greater reports of both total and lifetime ideation while also considering suicidal behavior within the past year. However, no differences were observed in reports of possible future attempt(s) and the disclosure of suicidal thoughts and behaviors to another person. Moreover, students presenting concurrent symptoms of insomnia reported significantly elevated levels of suicidal ideation relative to nonstudents. These outcomes highlight the possible role of insomnia symptoms in accentuating suicidal thoughts and behaviors in the student population.
Severity of urban transit bus crashes in Bangladesh

Unlike in developed countries where buses are a relatively safe mode of transport, there is a significant safety concern in many developing countries like Bangladesh regarding transit buses. Nevertheless, few studies have examined the factors contributing to the number or severity of bus crashes. Using the ordered probit model on bus crash data from 1998 to 2005 in Dhaka, Bangladesh, our study shows that there is a general increase in the severity of transit bus crashes over this period. Also, crash severity tends to increase when the collision occurs on weekends, off-peak periods, and two-way streets or involves only one vehicle, a pedestrian, and other vulnerable road users. On the other hand, the severity of a crash tends to be lower at locations with some form of police control or road medians, as well as for crashes involving hit object, parked vehicles, or sideswipes.