Miracle-dz/UniFAR-FTUs huggingface dataset:
TRSA-Net: Task Relation Spatial Co-Attention for Joint Segmentation, Quantification and Uncertainty Estimation on Paired 2D Echocardiography

Clinical workflow of cardiac assessment on 2D echocardiography requires both accurate segmentation and quantification of the Left Ventricle (LV) from paired apical 4-chamber and 2-chamber. Moreover, uncertainty estimation is significant in clinically understanding the performance of a model. However, current research on 2D echocardiography ignores this vital task while joint segmentation with quantification, hence motivating the need for a unified optimization method. In this paper, we propose a multitask model with Task Relation Spatial co-Attention (referred as TRSA-Net) for joint segmentation, quantification, and uncertainty estimation on paired 2D echo. TRSA-Net achieves multitask joint learning by novelly exploring the spatial correlation between tasks. The task relation spatial co-attention learns the spatial mapping among task-specific features by non-local and co-excitation, which forcibly joints embedded spatial information in the segmentation and quantification. The Boundary-aware Structure Consistency (BSC) and Joint Indices Constraint (JIC) are integrated into the multitask learning optimization objective to guide the learning of segmentation and quantification paths. The BSC creatively promotes structural similarity of predictions, and JIC explores the internal relationship between three quantitative indices. We validate the efficacy of our TRSA-Net on the public CAMUS dataset. Extensive comparison and ablation experiments show that our approach can achieve competitive segmentation performance and highly accurate results on quantification.
Prevalence and predictors of ICD‐11 posttraumatic stress disorder and complex PTSD in young people

The prevalence, construct validity, risk factors and psychopathological correlates associated with ICD‐11 posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD) as measured by the International Trauma Questionnaire for Children and Adolescents (ITQ‐CA) were assessed in a sample of young people from Northern Ireland.
Study on the influence of nozzle structural parameters of straight-swirling integrated jet on flow field characteristics

The straight-swirling integrated jet has advantages of both the straight jet and the swirling jet. To further investigate the influence of structural parameters and rock breaking characteristics of the straight-swirling integrated jet on its flow field, the numerical simulation method is used to make a research on several structural parameters: the diameter of the impeller center hole, the angle and the area of the impeller slot, the length and the convergence angle of mixing chamber, and so on. The results reveal that the axial velocity attenuation rule is influenced by the diameter of the impeller center hole and theangle of the impeller slot; the distribution of the tangential velocity is influenced by the diameter of the impeller center hole, the angle and the area of the impeller slot, the convergence angle of the mixing chamber, and the spray distance. Compared with the results of rock breaking tests, those are found that the lower axial velocity attenuation gradient is, the longer effective spray distance it has. The higher axial velocity it has, the longer drilling depth it achieves. The higher tangential velocity and larger effective area it has, the larger hole diameter it achieves. The study would provide a reference for jet bit structure design in radial horizontal drilling.
Co-crystallization of Amoxicillin Trihydrate and Potassium Clavulanate Provides a Promising Approach for Preparation of Sustained-Release Microspheres

This work aimed to prepare sustained-release microspheres for amoxicillin trihydrate and potassium clavulanate. Co-crystals of amoxicillin trihydrate and potassium clavulanate were prepared using three different techniques, including supercritical fluid technology. Full characterization was performed for the prepared co-crystals, including molecular dynamic simulation. Next, the co-crystals were microencapsulated with ethylcellulose using the emulsion solvent evaporation method in spherical microspheres. Physicochemical characterizations for the prepared co-crystal were performed using FTIR, DSC, and PXRD. Finally, scanning electron microscopy was used to assess the morphology of the prepared microspheres. Physicochemical studies showed the solid-state interaction between amoxicillin trihydrate and potassium clavulanate in the prepared co-crystals. The total energy suggested differences between the three methods of co-crystal preparations suggesting some structural changes have occurred with better stabilization at supercritical fluid technology. Encapsulation of the co-crystals was successfully performed using ethylcellulose polymer. The in vitro release studies revealed sustained-release profiles for the co-crystal microspheres. Potassium clavulanate was released at a lower rate from the crystal microspheres prepared using co-crystals than the release in microspheres of potassium clavulanate alone. The empirical Higuchi model best fitted the in vitro release profile for amoxicillin trihydrate-potassium clavulanate co-crystal microspheres.
A neutralizing antibody target in early HIV-1 infection was recapitulated in rhesus macaques immunized with the transmitted/founder envelope sequence

Transmitted/founder (T/F) HIV-1 envelope proteins (Envs) from infected individuals that developed neutralization breadth are likely to possess inherent features desirable for vaccine immunogen design. To explore this premise, we conducted an immunization study in rhesus macaques (RM) using T/F Env sequences from two human subjects, one of whom developed potent and broad neutralizing antibodies (Z1800M) while the other developed little to no neutralizing antibody responses (R66M) during HIV-1 infection. Using a DNA/MVA/protein immunization protocol, 10 RM were immunized with each T/F Env. Within each T/F Env group, the protein boosts were administered as either monomeric gp120 or stabilized trimeric gp140 protein. All vaccination regimens elicited high titers of antigen-specific IgG, and two animals that received monomeric Z1800M Env gp120 developed autologous neutralizing activity. Using early Env escape variants isolated from subject Z1800M as guides, the serum neutralizing activity of the two immunized RM was found to be dependent on the gp120 V5 region. Interestingly, the exact same residues of V5 were also targeted by a neutralizing monoclonal antibody (nmAb) isolated from the subject Z1800M early in infection. Glycan profiling and computational modeling of the Z1800M Env gp120 immunogen provided further evidence that the V5 loop is exposed in this T/F Env and was a dominant feature that drove neutralizing antibody targeting during infection and immunization. An expanded B cell clonotype was isolated from one of the neutralization-positive RM and nmAbs corresponding to this group demonstrated V5-dependent neutralization similar to both the RM serum and the human Z1800M nmAb. The results demonstrate that neutralizing antibody responses elicited by the Z1800M T/F Env in RM converged with those in the HIV-1 infected human subject, illustrating the potential of using immunogens based on this or other T/F Envs with well-defined immunogenicity as a starting point to drive breadth.
A synthetic lethality screen reveals ING5 as a genetic dependency of catalytically dead Set1A/COMPASS in mouse embryonic stem cells

Significance Pluripotency and development can be governed at the level of epigenetics. Growing lines of evidence underscore the importance of the Set1A/complex of proteins associated with SET1 (COMPASS) histone 3 lysine 4 methyltransferase complex in embryogenesis and neurodevelopment. We show that the catalytic SET domain of Set1A is essential for mouse embryogenesis; however, having this domain extends the viability of embryos compared to complete loss of Set1A. We additionally show that Ing5, a core component of several complexes involved in histone acetylation, can functionally interact with Set1AΔSET in regulating ESC viability and developmental gene expression. Insights into their physiological activity and regulation will assist our understanding of their dysfunction in disease and ultimately facilitate the discovery of new targets for future therapy.
A Method of Determining Suitable Probability for Irrigation Project Design

Rainfall frequency analysis is used commonly to determine probability of irrigation water requirement, and to choose typical years for irrigation project design. But there are some disadvantages in this method. To overcome these disadvantages, the results of frequency analysis of net irrigation requirement are recommended to determine the probability in this paper. A case study shows that the new method is more valid in considering rainfall distribution.
Wind-Induced Security of High-Speed Trains on the Ground

In order to study the running security of high-speed trains under strong crosswinds,an aerodynamic model and a vehicle system dynamic model for computing the wind-induced vibration of the high-speed trains were established based on the theories of aerodynamics and multi-body system dynamics.With the models,the characteristics of crosswind aerodynamic loads acting on the train body under different wind angles,different wind speeds,and different running speeds were calculated.Then,according to the standard of Testing of High-Speed Electric Multiple Unit on Completion of Construction,the operational safety of the head car,middle car,and tail car of the train were analyzed using the operational safety indicators of the derailment coefficient,the load reduction rate,the axle lateral force,and the wheel-rail vertical force.The results show that the security of the head car is lower than the other two cars in all cases;and when the wind angle is 90° in a cross-wind situation,the security of the head car is the worst.The maximum safe wind speed decreases sharply as the train speed increases: when the train speed is 200 km/h,the maximum safe wind speed is 29.61 m/s,and when the train speed is 400 km/h,the maximum wind speed is 18.87 m/s.
Clustering sequence data with mixture Markov chains with covariates using multiple simplex constrained optimization routine (MSiCOR)

Mixture Markov Model (MMM) is a widely used tool to cluster sequences of events coming from a finite state-space. However the MMM likelihood being multi-modal, the challenge remains in its maximization. Although Expectation-Maximization (EM) algorithm remains one of the most popular ways to estimate the MMM parameters, however convergence of EM algorithm is not always guaranteed. Given the computational challenges in maximizing the mixture likelihood on the constrained parameter space, we develop a pattern search-based global optimization technique which can optimize any objective function on a collection of simplexes, which is eventually used to maximize MMM likelihood. This is shown to outperform other related global optimization techniques. In simulation experiments, the proposed method is shown to outperform the expectation-maximization (EM) algorithm in the context of MMM estimation performance. The proposed method is applied to cluster Multiple sclerosis (MS) patients based on their treatment sequences of disease-modifying therapies (DMTs). We also propose a novel method to cluster people with MS based on DMT prescriptions and associated clinical features (covariates) using MMM with covariates. Based on the analysis, we divided MS patients into 3 clusters. Further cluster-specific summaries of relevant covariates indicate patient differences among the clusters.
Deep learning of quantum entanglement from incomplete measurements

The quantification of the entanglement present in a physical system is of paramount importance for fundamental research and many cutting-edge applications. Now, achieving this goal requires either a priori knowledge on the system or very demanding experimental procedures such as full state tomography or collective measurements. Here, we demonstrate that, by using neural networks, we can quantify the degree of entanglement without the need to know the full description of the quantum state. Our method allows for direct quantification of the quantum correlations using an incomplete set of local measurements. Despite using undersampled measurements, we achieve a quantification error of up to an order of magnitude lower than the state-of-the-art quantum tomography. Furthermore, we achieve this result using networks trained using exclusively simulated data. Last, we derive a method based on a convolutional network input that can accept data from various measurement scenarios and perform, to some extent, independently of the measurement device.
Meta Learning for Natural Language Processing: A Survey

Deep learning has been the mainstream technique in the natural language processing (NLP) area. However, deep learning requires many labeled data and is less generalizable across domains. Meta-learning is an arising field in machine learning. It studies approaches to learning better learning algorithms and aims to improve algorithms in various aspects, including data efficiency and generalizability. The efficacy of meta-learning has been shown in many NLP tasks, but there is no systematic survey of these approaches in NLP, which hinders more researchers from joining the field. Our goal with this survey paper is to offer researchers pointers to relevant meta-learning works in NLP and attract more attention from the NLP community to drive future innovation. This paper first introduces the general concepts of meta-learning and the common approaches. Then we summarize task construction settings, applications of meta-learning for various NLP problems and review the development of meta-learning in the NLP community.
Scalable Tail Latency Estimation for Data Center Networks

In this paper, we consider how to provide fast estimates of flow-level tail latency performance for very large scale data center networks. Network tail latency is often a crucial metric for cloud application performance that can be affected by a wide variety of factors, including network load, inter-rack traffic skew, traffic burstiness, flow size distributions, oversubscription, and topology asymmetry. Network simulators such as ns-3 and OMNeT++ can provide accurate answers, but are very hard to parallelize, taking hours or days to answer what if questions for a single configuration at even moderate scale. Recent work with MimicNet has shown how to use machine learning to improve simulation performance, but at a cost of including a long training step per configuration, and with assumptions about workload and topology uniformity that typically do not hold in practice. We address this gap by developing a set of techniques to provide fast performance estimates for large scale networks with general traffic matrices and topologies. A key step is to decompose the problem into a large number of parallel independent single-link simulations; we carefully combine these link-level simulations to produce accurate estimates of end-to-end flow level performance distributions for the entire network. Like MimicNet, we exploit symmetry where possible to gain additional speedups, but without relying on machine learning, so there is no training delay. On large-scale networks where ns-3 takes 11 to 27 hours to simulate five seconds of network behavior, our techniques run in one to two minutes with 99th percentile accuracy within 9% for flow completion times.
Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation

Pose estimation plays a critical role in human-centered vision applications. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-constrained edge devices due to the high computational cost (more than 150 GMACs per frame). In this paper, we study efficient architecture design for real-time multi-person pose estimation on edge. We reveal that HRNet's high-resolution branches are redundant for models at the low-computation region via our gradual shrinking experiments. Removing them improves both efficiency and performance. Inspired by this finding, we design LitePose, an efficient single-branch architecture for pose estimation, and introduce two simple approaches to enhance the capacity of LitePose, including fusion deconv head and large kernel conv. On mobile platforms, LitePose reduces the latency by up to $5.0\times$ without sacrificing performance, compared with prior state-of-the-art efficient pose estimation models, pushing the frontier of real-time multi-person pose estimation on edge. Our code and pretrained models are released at https://github.com/mit-han-lab/litepose.
Crossmodal Representation Learning for Zero-shot Action Recognition

We present a cross-modal Transformer-based frame-work, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR). Our model employs a conceptually new pipeline by which visual representations are learned in conjunction with visual-semantic associations in an end-to-end manner. The model design provides a natural mechanism for visual and semantic representations to be learned in a shared knowledge space, whereby it encourages the learned visual embedding to be discriminative and more semantically consistent. In zero-shot inference, we devise a simple semantic transfer scheme that embeds semantic relatedness information between seen and unseen classes to composite unseen visual prototypes. Accordingly, the discriminative features in the visual structure could be preserved and exploited to alleviate the typical zero-shot issues of information loss, semantic gap, and the hubness problem. Under a rigorous zero-shot setting of not pre-training on additional datasets, the experiment results show our model considerably improves upon the state of the arts in ZSAR, reaching encouraging top-1 accuracy on UCF 101, HMDB51, and ActivityNet benchmark datasets. Code will be made available.11https://github.com/microsoft/ResT
Classical cosmological collider physics and primordial features

Features in the inflationary landscape can inject extra energies to inflation models and produce on-shell particles with masses much larger than the Hubble scale of inflation. This possibility extends the energy reach of the program of cosmological collider physics, in which signals associated with these particles are generically Boltzmann-suppressed. We study the mechanisms of this classical cosmological collider in two categories of primordial features. In the first category, the primordial feature is classical oscillation, which includes the case of coherent oscillation of a massive field and the case of oscillatory features in the inflationary potential. The second category includes any sharp feature in the inflation model. All these classical features can excite unsuppressed quantum modes of other heavy fields which leave observational signatures in primordial non-Gaussianities, including the information about the particle spectra of these heavy degrees of freedom.
NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithms

Crack detection plays a key role in automated pavement inspection. Although a large number of algorithms have been developed in recent years to further boost performance, there are still remaining challenges in practice, due to the complexity of pavement images. To further accelerate the development and identify the remaining challenges, this paper conducts a comparison study to evaluate the performance of the state of the art crack detection algorithms quantitatively and objectively. A more comprehensive annotated pavement crack dataset (NHA12D) that contains images with different viewpoints and pavements types is proposed. In the comparison study, crack detection algorithms were trained equally on the largest public crack dataset collected and evaluated on the proposed dataset (NHA12D). Overall, the U-Net model with VGG-16 as backbone has the best all-around performance, but models generally fail to distinguish cracks from concrete joints, leading to a high false-positive rate. It also found that detecting cracks from concrete pavement images still has huge room for improvement. Dataset for concrete pavement images is also missing in the literature. Future directions in this area include filling the gap for concrete pavement images and using domain adaptation techniques to enhance the detection results on unseen datasets.
Strength Analysis of EMU Coupler Joint Bolts Based on VDI 2230-2003

For bolt contact nonlinear problems,the higher requirements for safety and reliability of the bolts are proposed,and ANSYS is used to calculate the contact nonlinear static strength of a certain coupler mounting base and its joint bolts. The bolt strength is evaluate based on VDI 2230-2003,and the results is compared with those of the traditional mechanical design handbook. It is concluded that the results of the two methods all meet the strength requirements,and the axial tensile stress of the former is 72% of that of the latter,but both have the same shear stress.
Fermionic Bogomolǹyi-Prasad-Sommerfield Wilson loops in four-dimensional $\mathcal{N}=2$ superconformal gauge theories

We construct for the first time Drukker-Trancanelli (DT) type fermionic Bogomolǹyi-Prasad-Sommerfield (BPS) Wilson loops in four-dimensional \mathcal{N}=2𝒩=2 superconformal SU(N)\times SU(N)SU(N)×SU(N) quiver theory and \mathcal{N}=4𝒩=4 super Yang-Mills theory. The connections of these fermionic BPS Wilson loops have a supermatrix structure. We construct timelike BPS Wilson lines in Minkowski spacetime and circular BPS Wilson loops in Euclidean space. These Wilson loops involve dimensionful parameters. For generic values of parameters, they preserve one real (complex) supercharge in Lorentzian (Euclidean) signature. Supersymmetry enhancement for Wilson loops happens when the parameters satisfy certain constraints. The nature of such loops is quite different from the Wilson loop operators involving fermions constructed previously in the literature on four-dimensional gauge theories. We hope that further investigations of such new Wilson loops will explore deep structures in both the gauge theories and gauge/gravity dualities.
Few-Shot Musical Source Separation

Deep learning-based approaches to musical source separation are often limited to the instrument classes that the models are trained on and do not generalize to separate unseen instruments. To address this, we propose a few-shot musical source separation paradigm. We condition a generic U-Net source separation model using few audio examples of the target instrument. We train a few-shot conditioning encoder jointly with the U-Net to encode the audio examples into a conditioning vector to configure the U-Net via feature-wise linear modulation (FiLM). We evaluate the trained models on real musical recordings in the MUSDB18 and MedleyDB datasets. We show that our proposed few-shot conditioning paradigm outperforms the base-line one-hot instrument-class conditioned model for both seen and unseen instruments. To extend the scope of our approach to a wider variety of real-world scenarios, we also experiment with different conditioning example characteristics, including examples from different recordings, with multiple sources, or negative conditioning examples.