Miracle-dz/UniFAR-FTUs huggingface dataset:
D2-CovidNet: A Deep Learning Model for COVID-19 Detection in Chest X-Ray Images

Since the outbreak of Coronavirus disease 2019 (COVID-19), it has been spreading rapidly worldwide and has not yet been effectively controlled. Many researchers are studying novel Coronavirus pneumonia from chest X-ray images. In order to improve the detection accuracy, two modules sensitive to feature information, dual-path multiscale feature fusion module and dense depthwise separable convolution module, are proposed. Based on these two modules, a lightweight convolutional neural network model, D2-CovidNet, is designed to assist experts in diagnosing COVID-19 by identifying chest X-ray images. D2-CovidNet is tested on two public data sets, and its classification accuracy, precision, sensitivity, specificity, and F1-score are 94.56%, 95.14%, 94.02%, 96.61%, and 95.30%, respectively. Specifically, the precision, sensitivity, and specificity of the network for COVID-19 are 98.97%, 94.12%, and 99.84%, respectively. D2-CovidNet has fewer computation number and parameter number. Compared with other methods, D2-CovidNet can help diagnose COVID-19 more quickly and accurately.
Joint Waveform and Filter Designs for STAP-SLP-Based MIMO-DFRC Systems

Dual-function radar-communication (DFRC), which can simultaneously perform both radar and communication functionalities using the same hardware platform, spectral resource and transmit waveform, is a promising technique for realizing integrated sensing and communication (ISAC). Space-time adaptive processing (STAP) in multi-antenna radar systems is the primary tool for detecting moving targets in the presence of strong clutter. The idea of joint spatial-temporal optimization in STAP-based radar systems is consistent with the concept of symbol-level precoding (SLP) for multi-input multi-output (MIMO) communications, which optimizes the transmit waveform for each of the transmitted symbols. In this paper, we combine STAP and SLP and propose a novel STAP-SLP-based DFRC system that enjoys the advantages of both techniques. The radar output signal-to-interference-plus-noise ratio (SINR) is maximized by jointly optimizing the transmit waveform and receive filter, while satisfying the communication quality-of-service (QoS) constraint and various waveform constraints including constant-modulus, similarity and peak-to-average power ratio (PAPR). An efficient algorithm framework based on majorization-minimization (MM) and nonlinear equality constrained alternative direction method of multipliers (neADMM) methods is proposed to solve these complicated non-convex optimization problems. Simulation results verify the effectiveness of the proposed STAP-SLP-based MIMO-DRFC scheme and the associate algorithms.
Using Bayesian Evidence Synthesis Methods to Incorporate Real-World Evidence in Surrogate Endpoint Evaluation

Objective Traditionally, validation of surrogate endpoints has been carried out using randomized controlled trial (RCT) data. However, RCT data may be too limited to validate surrogate endpoints. In this article, we sought to improve the validation of surrogate endpoints with the inclusion of real-world evidence (RWE). Methods We use data from comparative RWE (cRWE) and single-arm RWE (sRWE) to supplement RCT evidence for the evaluation of progression-free survival (PFS) as a surrogate endpoint to overall survival (OS) in metastatic colorectal cancer (mCRC). Treatment effect estimates from RCTs, cRWE, and matched sRWE, comparing antiangiogenic treatments with chemotherapy, were used to inform surrogacy patterns and predictions of the treatment effect on OS from the treatment effect on PFS. Results Seven RCTs, 4 cRWE studies, and 2 matched sRWE studies were identified. The addition of RWE to RCTs reduced the uncertainty around the estimates of the parameters for the surrogate relationship. The addition of RWE to RCTs also improved the accuracy and precision of predictions of the treatment effect on OS obtained using data on the observed effect on PFS. Conclusion The addition of RWE to RCT data improved the precision of the parameters describing the surrogate relationship between treatment effects on PFS and OS and the predicted clinical benefit of antiangiogenic therapies in mCRC. Highlights Regulatory agencies increasingly rely on surrogate endpoints when making licensing decisions, and for the decisions to be robust, surrogate endpoints need to be validated. In the era of precision medicine, when surrogacy patterns may depend on the drug’s mechanism of action and trials of targeted therapies may be small, data from randomized controlled trials may be limited. Real-world evidence (RWE) is increasingly used at different stages of the drug development process. When used to enhance the evidence base for surrogate endpoint evaluation, RWE can improve inferences about the strength of surrogate relationships and the precision of predicted treatment effect on the final clinical outcome based on the observed effect on the surrogate endpoint in a new trial. Careful selection of RWE is needed to reduce risk of bias.
UMAD: Universal Model Adaptation under Domain and Category Shift

Learning to reject unknown samples (not present in the source classes) in the target domain is fairly important for unsupervised domain adaptation (UDA). There exist two typical UDA scenarios, i.e., open-set, and open-partial-set, and the latter assumes that not all source classes appear in the target domain. However, most prior methods are designed for one UDA scenario and always perform badly on the other UDA scenario. Moreover, they also require the labeled source data during adaptation, limiting their usability in data privacy-sensitive applications. To address these issues, this paper proposes a Universal Model ADaptation (UMAD) framework which handles both UDA scenarios without access to the source data nor prior knowledge about the category shift between domains. Specifically, we aim to learn a source model with an elegantly designed two-head classifier and provide it to the target domain. During adaptation, we develop an informative consistency score to help distinguish unknown samples from known samples. To achieve bilateral adaptation in the target domain, we further maximize localized mutual information to align known samples with the source classifier and employ an entropic loss to push unknown samples far away from the source classification boundary, respectively. Experiments on open-set and open-partial-set UDA scenarios demonstrate that UMAD, as a unified approach without access to source data, exhibits comparable, if not superior, performance to state-of-the-art data-dependent methods.
Adapting Document-Grounded Dialog Systems to Spoken Conversations using Data Augmentation and a Noisy Channel Model

This paper summarizes our submission to Task 2 of the second track of the 10th Dialog System Technology Challenge (DSTC10)"Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations". Similar to the previous year's iteration, the task consists of three subtasks: detecting whether a turn is knowledge seeking, selecting the relevant knowledge document and finally generating a grounded response. This year, the focus lies on adapting the system to noisy ASR transcripts. We explore different approaches to make the models more robust to this type of input and to adapt the generated responses to the style of spoken conversations. For the latter, we get the best results with a noisy channel model that additionally reduces the number of short and generic responses. Our best system achieved the 1st rank in the automatic and the 3rd rank in the human evaluation of the challenge.
The relativistic parsec-scale jets of the blazars TXS 0506+056 and PKS 0502+049 and their possible association with gamma-ray flares and neutrino production

The physical nature of the mechanism responsible for the emission of neutrinos in active galactic nuclei (AGN) has been matter of debate in the literature, with relativistic jets of radio-loud AGNs as possible candidates to be the sources of high energy neutrinos. The most prominent candidate so far is the blazar TXS 0506+056, which is found to be associated with the neutrino event IceCube-170922A. Furthermore, the IceCube reported an excess of neutrinos towards TXS 0506+056 between September 2014 and March 2015, even though this association needs additional investigation, considering the presence of a nearby γ-ray source, the quasar PKS 0502+049. Motivated by this, we studied the parsec-scale structures of TXS 0506+056 and PKS 0502+049 through radio interferometry at 8 and 15GHz. We identified twelve jet components in TXS 0506+056 and seven components in PKS 0502+049. The most reliable jet components show superluminal speeds ranging from 9.5c to 66c in the case of TXS 0506+056, and from 14.3c to 59c for PKS 0502+049, which were used to estimate a lower (upper) limit for the Lorentz factor (jet viewing angle) for both sources. A novel approach using simultaneously the brightness temperature of the core region and the apparent speeds of the jet components allowed us to infer basic jet parameters for TXS 0506+056 at distinct epochs. We also found that the emergence of new jet components coincides with the occurrence of gamma-ray flares. Interestingly, two of these coincidences in the case of PKS 0502+049 and one for TXS 0506+056 seems to be correlated with neutrino events detected by the IceCube Observatory.
Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications

Effective representation learning is the key in improving model performance for medical image analysis. In training deep learning models, a compromise often must be made between performance and trust, both of which are essential for medical applications. Moreover, models optimized with cross-entropy loss tend to suffer from unwarranted overconfidence in the majority class and over-cautiousness in the minority class. In this work, we integrate a new surrogate loss with self-supervised learning for computer-aided screening of COVID-19 patients using radiography images. In addition, we adopt a new quantification score to measure a model's trustworthiness. Ablation study is conducted for both the performance and the trust on feature learning methods and loss functions. Comparisons show that leveraging the new surrogate loss on self-supervised models can produce label-efficient networks that are both high-performing and trustworthy.
Simple Questions Generate Named Entity Recognition Datasets

Recent named entity recognition (NER) models often rely on human-annotated datasets requiring the vast engagement of professional knowledge on the target domain and entities. This work introduces an ask-to-generate approach, which automatically generates NER datasets by asking simple natural language questions to an open-domain question answering system (e.g., “Which disease? ”). Despite using fewer training resources, our models solely trained on the generated datasets largely outperform strong low-resource models by 19.5 F1 score across six popular NER benchmarks. Our models also show competitive performance with rich-resource models that additionally leverage in-domain dictionaries provided by domain experts. In few-shot NER, we outperform the previous best model by 5.2 F1 score on three benchmarks and achieve new state-of-the-art performance.
Teambuilding, Innovation and the Engineering Communication Interface.

Recent engineering industry-based research has identified a number of skill deficiencies in graduating engineers. Emphasis on communication and teamwork informed by attributes of self management, problem solving and mutual accountability have been recognized as important needs by The Engineering Accreditation Commission of ABET of the United States and are now required in undergraduate course material. The Engineering College at the American University of Sharjah has recognised this reality with the development of a course in language enhancement and professional communication centred on engineering multidisciplinary projects (EMDPs). This paper will outline four innovative practices that together inform this course; team-building, teamwork management, collaborative problem solving, resource management. Brief illustrative descriptions of: team-building through the use of the Belbin Team Role Inventory; management of teamwork development via planning and documentation; personnel and collaborative problem solving and interactive information sources hosted via a LibGuide will elaborate these innovative practices.
Slowly-rotating curved acoustic black holes: Quasinormal modes, Hawking-Unruh radiation, and quasibound states

Astrophysical black holes are generally surrounded by accretion disks, galactic matter and the om-nipresent cosmic microwave background radiation, thus allowing for the concurrent propagation of both gravitational and sound waves. Recently, acoustic black holes were embedded in Schwarzschild spacetime allowing for the coexistence of event and acoustic horizons. Here, we obtain a class of perturbative solutions to the ﬁeld equations of the relativistic Gross-Pitaevskii and Yang-Mills theories, which describe sound waves propagating on a curved slowly-rotating acoustic black hole, akin to Lense-Thirring spacetime. We investigate the quasinormal mode frequencies, Hawking-Unruh radiation, and quasibound states. Our novel metric mimics the gravitational ﬁeld of astrophysical compact objects in the limiting case of slow rotation, and therefore could, in principle, shed more light into the underlying classical and quantum physics of black holes through analog acoustic probes.
A Minimal Dark Matter Model for Muon g-2 with Scalar Lepton Partners up to the TeV Scale

The E989 experiment at the Fermi National Laboratory reported a 4. 2σ discrepancy between the measured magnetic dipole moment of the muon, and its prediction in the Standard Model (SM). In this study, we address the anomaly by considering a minimal and generic extension to the SM which also provides for a dark matter (DM) candidate. The extra states in this framework are: a SM singlet Majorana fermion, referred to as the Bino, playing the role of DM; and muonic scalars, referred to as sleptons. The couplings between the sleptons, SM muons and the Bino can account for the muon g − 2 anomaly if the scalar muon partners, or smuons, mix chirality. On the other hand, the DM relic density is satisfied primarily through coannihilation effects involving the Bino and the lighter sleptons. The viable parameter space of our model includes regions with relatively light coannihilating particles, similar to what has been found in previous scans of the Minimal Supersymmetric Standard Model (MSSM). Relaxing the assumption of minimal flavor violation typically assumed in the MSSM, we see that scenarios with sizable smuon mixing and large mass splittings between the smuons can satisfy both the muon g−2 anomaly and the DM relic density for coannihilating particle masses up to and beyond the TeV scale. When we specify the origin of the left-right smuon mixing to be trilinear couplings between the smuons and the SM Higgs boson, the constraints on these scenarios arising from perturbative unitarity and electroweak vacuum stability confine the coannihilating particle masses to be . 1 TeV. We demonstrate that next generation direct detection experiments are only marginally sensitive to the viable parameter space of our model and, thus, a future lepton collider could be the essential probe necessary to distinguish our model from other BSM solutions to the muon g − 2 anomaly.
Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Domain adaptive object detection (DAOD) aims to improve the generalization ability of detectors when the training and test data are from different domains. Considering the significant domain gap, some typical methods, e.g., CycleGAN-based methods, adopt the intermediate domain to bridge the source and target domains progressively. However, the CycleGAN-based intermediate domain lacks the pix- or instance-level supervision for object detection, which leads to semantic differences. To address this problem, in this paper, we introduce a Frequency Spectrum Augmentation Consistency (FSAC) framework with four different low-frequency filter operations. In this way, we can obtain a series of augmented data as the intermediate domain. Concretely, we propose a two-stage optimization framework. In the first stage, we utilize all the original and augmented source data to train an object detector. In the second stage, augmented source and target data with pseudo labels are adopted to perform the self-training for prediction consistency. And a teacher model optimized using Mean Teacher is used to further revise the pseudo labels. In the experiment, we evaluate our method on the single- and compound- target DAOD separately, which demonstrate the effectiveness of our method.
Role of Focusing Distance in Picosecond Laser-Induced Cu Plasma Spectra

To study the effects of focusing distance on the characteristics of copper plasma, a picosecond laser is utilized to ablate a pure copper plate to generate a plasma spectrum. Following numerous experiments on the subject, three significant factors are determined: lens focal length, pulse energy, and the lens-to-sample distance. These factors are employed to analyze the spectral intensity, plasma temperature, and electron density in the local thermodynamic equilibrium (LTE) and optically thin condition. Due to the shielding effects of mixed plasma, the strongest spectral intensity is achieved in the prefocused case, no matter how much beam irradiance is employed. The more intensive the beam irradiance is, the more the optimal position is distant from the focal point. The variation of plasma temperature and electron density showed a peak in the prefocused case, which is consistent with the trend of spectral intensity. For the case of extremely high irradiance (on the focus), the shielding effects become seriously, and the resultant above three factors decreased sharply. When a longer-focal-length lens is employed, the spectral intensity exhibited an obvious bimodal trend. In the prefocused case, a longer-focal-length lens is helpful to eliminate the effects of the roughness of the target surface compared with a shorter one. Finally, the assumed LTE is validated by McWhirter relation, plasma relaxation time, and diffusion length, and the optically thin condition was also validated by spectral intensity ratio. We hope that this work could be an important reference for the future design of highly optimized experiments for Calibration-Free Laser-Induced Breakdown Spectroscopy (CF-LIBS).
An equivalent circuit for calculation of interturn voltage distribution of stator windings in the presence of slot discharges

In modern epoxy-mica insulated rotating machines inter-turn insulation breakdown and slot discharges has been the two major cause of electrical failure. In this paper, a distributed high frequency equivalent circuit is proposed which is capable of simulating turn to ground, inter-turn and screen point potentials along the machine winding. Screen potentials may be large enough to cause discharges across the slot wall. The equivalent circuit is based on distributed resistance, inductance and capacitance type ladder network. Transient responses of the machine winding for steep fronted impulse, surge step input, PWM waveform, and steady state high frequency oscillation are presented. Effects of corona screen resistivity and location of loose earth contact on corona screen potentials are also shown in the paper. The proposed model can be very useful in the design of machine insulation.
TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning

Zero-shot learning (ZSL) tackles the novel class recognition problem by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is typically represented by attribute descriptions shared between different classes, which act as strong priors for localizing object attributes that represent discriminative region features, enabling significant and sufficient visual-semantic interaction for advancing ZSL. Existing attention-based models have struggled to learn inferior region features in a single image by solely using unidirectional attention, which ignore the transferable and discriminative attribute localization of visual features for representing the key semantic knowledge for effective knowledge transfer in ZSL. In this paper, we propose a cross attribute-guided Transformer network, termed TransZero++, to refine visual features and learn accurate attribute localization for key semantic knowledge representations in ZSL. Specifically, TransZero++ employs an attribute <inline-formula><tex-math notation="LaTeX">$\rightarrow$</tex-math><alternatives><mml:math><mml:mo>→</mml:mo></mml:math><inline-graphic xlink:href="chen-ieq1-3229526.gif"/></alternatives></inline-formula> visual Transformer sub-net (AVT) and a visual <inline-formula><tex-math notation="LaTeX">$\rightarrow$</tex-math><alternatives><mml:math><mml:mo>→</mml:mo></mml:math><inline-graphic xlink:href="chen-ieq2-3229526.gif"/></alternatives></inline-formula> attribute Transformer sub-net (VAT) to learn attribute-based visual features and visual-based attribute features, respectively. By further introducing feature-level and prediction-level semantical collaborative losses, the two attribute-guided transformers teach each other to learn semantic-augmented visual embeddings for key semantic knowledge representations via semantical collaborative learning. Finally, the semantic-augmented visual embeddings learned by AVT and VAT are fused to conduct desirable visual-semantic interaction cooperated with class semantic vectors for ZSL classification. Extensive experiments show that TransZero++ achieves the new state-of-the-art results on three golden ZSL benchmarks and on the large-scale ImageNet dataset. The project website is available at: <uri>https://shiming-chen.github. io/TransZero-pp/TransZero-pp.html</uri>.
HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images

Existing state-of-the-art methods for Video Object Segmentation (VOS) learn low-level pixel-to-pixel correspondences between frames to propagate object masks across video. This requires a large amount of densely annotated video data, which is costly to annotate, and largely redundant since frames within a video are highly correlated. In light of this, we propose HODOR: a novel method that tackles VOS by effectively leveraging annotated static images for understanding object appearance and scene context. We encode object instances and scene information from an image frame into robust high-level descriptors which can then be used to re-segment those objects in different frames. As a result, HODOR achieves state-of-the-art performance on the DAVIS and YouTube-VOS benchmarks compared to existing methods trained without video annotations. With out any architectural modification, HODOR can also learn from video context around single annotated video frames by utilizing cyclic consistency, whereas other methods rely on dense, temporally consistent annotations. Source code: https://github.com/Ali2500/HODOR.