Miracle-dz/UniFAR-FTUs huggingface dataset:
Preclinical Studies of Immunogenity, Protectivity, and Safety of the Combined Vector Vaccine for Prevention of the Middle East Respiratory Syndrome

The Middle East Respiratory Syndrome (MERS) is an acute inflammatory disease of the respiratory system caused by the MERS-CoV coronavirus. The mortality rate for MERS is about 34.5%. Due to its high mortality rate, the lack of therapeutic and prophylactic agents, and the continuing threat of the spread of MERS beyond its current confines, developing a vaccine is a pressing task, because vaccination would help limit the spread of MERS and reduce its death toll. We have developed a combined vector vaccine for the prevention of MERS based on recombinant human adenovirus serotypes 26 and 5. Studies of its immunogenicity have shown that vaccination of animals (mice and primates) induces a robust humoral immune response that lasts for at least six months. Studies of the cellular immune response in mice after vaccination showed the emergence of a specific CD4+ and CD8+ T cell response. A study of the vaccine protectivity conducted in a model of transgenic mice carrying the human DPP4 receptor gene showed that our vaccination protected 100% of the animals from the lethal infection caused by the MERS-CoV virus (MERS-CoV EMC/2012, 100LD50 per mouse). Studies of the safety and tolerability of the developed vaccine in rodents, rabbits, and primates showed a good safety profile and tolerance in animals; they revealed no contraindications for clinical testing.
Measurement-induced resetting in open quantum systems

We put forward a novel approach to study the evolution of an arbitrary open quantum system under a resetting process. Using the framework of renewal equations, we find a universal behavior for the mean first return time that goes beyond unitary dynamics and Markovian measurements. Our results show a non-trivial behavior of the mean switching times with the mean measurement time $\tau$, which permits tuning $\tau$ for minimizing the mean transition time between states. We complement our results with a numerical analysis and we benchmark the results against the corresponding analytical study for low dimensional systems under unitary \textit{and} open system dynamics.
Efficient End-to-End Speech Recognition Using Performers in Conformers

On-device end-to-end speech recognition poses a high requirement on model efficiency. Most prior works improve the efficiency by reducing model sizes. We propose to reduce the complexity of model architectures in addition to model sizes. More specifically, we reduce the floating-point operations in conformer by replacing the transformer module with a performer. The proposed attention-based efficient end-to-end speech recognition model yields competitive performance on the LibriSpeech corpus with 10 millions of parameters and linear computation complexity. The proposed model also outperforms previous lightweight end-to-end models by about 20% relatively in word error rate.
LncRNA TUG1 Promotes Growth and Metastasis of Cholangiocarcinoma Cells by Inhibiting miR-29a

Background As a highly malignant tumor, cholangiocarcinoma poses a serious threat to human life and health, so exploring the mechanisms of its development and progression at a molecular level is of great significance to the diagnosis and treatment of the disease. Objective This study was aimed at investigating the effects and related mechanisms of LncRNA TUG1 on cholangiocarcinoma cells. Methods Cholangiocarcinoma tissues and adjacent tissues (n=82 each), human cholangiocarcinoma cell lines (RBE, QBC939, HuH28), and a human normal biliary epithelial cell line (HIBE) were collected. miR-29a-mimics, miR-29a-inhibitor, miR-NC, si-TUG1, pcDNA3.1 TUG1, and NC were transfected into the cholangiocarcinoma cells. qRT-PCR was performed to detect TUG1 and miR-29a expression in the cholangiocarcinoma tissues and cells. Western blotting (WB) was conducted to detect the expression of Bax, Caspase-3, and Bcl-2 in the cells. CCK-8 assay, Transwell, and flow cytometry were carried out to detect cell proliferation, invasion, and apoptosis. Dual luciferase reporter gene assay (DLRGA) was performed to confirm the correlation of TUG1 with miR-29a. Results TUG1 was highly expressed while miR-29a was poorly expressed in cholangiocarcinoma cells. TUG1 expression was negatively correlated with miR-29a expression, and TUG1 had a relatively high diagnostic value for cholangiocarcinoma. Cell experiments showed that inhibiting TUG1 expression or up-regulating miR-29a expression could inhibit cholangiocarcinoma cells from proliferation and invasion, and promote their apoptosis, while up-regulating TUG1 or inhibiting miR-29a could promote the proliferation and invasion but inhibit the apoptosis. Rescue experiment showed that overexpressing miR-29a could reverse the effects of high TUG1 expression on cholangiocarcinoma cells. DLRGA confirmed that there was a regulatory relationship between TUG1 and miR-29a. Conclusion TUG1 is highly expressed in cholangiocarcinoma tissues. It can promote the growth and metastasis of cholangiocarcinoma cells by inhibiting miR-29a, so it may be a new target for diagnosing and treating cholangiocarcinoma.
Quantum tomography of noisy ion-based qudits

Quantum tomography makes it possible to obtain a comprehensive information about certain logical elements of a quantum computer. In this regard, it is a promising tool for debugging quantum computers. The practical application of tomography, however, is still limited by systematic measurement errors. Their main source are errors in the quantum state preparation and measurement procedures. In this work, we investigate the possibility of suppressing these errors in the case of trapped-ion-based qudits. First, we will show that one can construct a quantum tomography protocol that contains no more than a single quantum operation in each measurement circuit. Such a protocol is more robust to errors than the measurements in mutually unbiased bases, where the number of operations increases in proportion to the square of the qudit dimension. After that, we will demonstrate the possibility of determining and accounting for the state initialization and readout errors. Together, the measures described can significantly improve the accuracy of quantum tomography of real ion-based qudits.
White dwarf-open cluster associations based on Gaia DR2

Context. Fundamental parameters and physical processes leading to the formation of white dwarfs (WDs) may be constrained and refined by discovering WDs in open clusters (OCs). Cluster membership can be utilized to establish the precise distances, luminosities, ages, and progenitor masses of such WDs. Aims. We compile a list of probable WDs that are OC members in order to facilitate WD studies that are impractical or difficult to conduct for Galactic field WDs. Methods. We use recent catalogs of WDs and OCs that are based on the second data release of the Gaia satellite mission (GDR2) to identify WDs that are OC members. This crossmatch is facilitated by the astrometric and photometric data contained in GDR2 and the derived catalogs. Assuming that most of the WD members are of the DA type, we estimate the WD masses, cooling ages, and progenitor masses. Results. We have detected several new likely WD members and reassessed the membership of the literature WDs that had been previously associated with the studied OCs. Several of the recovered WDs fall into the recently reported discontinuity in the initial-final mass relation (IFMR) around Mi ∼ 2.0 M⊙, which allows for tighter constrains on the IFMR in this regime.
Neural Spatio-Temporal Point Processes

We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of discrete events that are localized in continuous time and space. Central to our approach is a combination of recurrent continuous-time neural networks with two novel neural architectures, i.e., Jump and Attentive Continuous-time Normalizing Flows. This approach allows us to learn complex distributions for both the spatial and temporal domain and to condition non-trivially on the observed event history. We validate our models on data sets from a wide variety of contexts such as seismology, epidemiology, urban mobility, and neuroscience.
Mask Proxy Loss for Text-Independent Speaker Recognition

Open-set speaker recognition can be regarded as a metric learning problem, which is to maximize inter-class variance and minimize intra-class variance. Supervised metric learning can be categorized into entity-based learning and proxy-based learning\protect\footnote{Different from the definition in \cite{Proxyanchor}, we adopt the concept of entity-based learning rather than pair-based learning to illustrate the data-to-data relationship. Entity refers to real data point. }. Most of existing metric learning objectives like Contrastive, Triplet, Prototypical, GE2E, etc all belong to the former division, the performance of which is either highly dependent on sample mining strategy or restricted by insufficient label information in the mini-batch. Proxy-based losses mitigate both shortcomings, however, fine-grained connections among entities are either not or indirectly leveraged. This paper proposes a Mask Proxy (MP) loss which directly incorporates both proxy-based relationship and entity-based relationship. We further propose Multinomial Mask Proxy (MMP) loss to leverage the hardness of entity-to-entity pairs. These methods have been applied to evaluate on VoxCeleb test set and reach state-of-the-art Equal Error Rate(EER).
Enabling DER Participation in Frequency Regulation Markets

Distributed energy resources (DERs) are playing an increasing role in ancillary services for the bulk grid, particularly in frequency regulation. In this article, we propose a framework for collections of DERs, combined to form microgrids and controlled by aggregators, to participate in frequency regulation markets. Our approach covers both the identification of bids for the market clearing stage and the mechanisms for the real-time allocation of the regulation signal. The proposed framework is hierarchical, consisting of a top layer and a bottom layer. The top layer consists of the aggregators communicating in a distributed fashion to optimally disaggregate the regulation signal requested by the system operator. The bottom layer consists of the DERs inside each microgrid whose power levels are adjusted so that the tie line power matches the output of the corresponding aggregator in the top layer. The coordination at the top layer requires the knowledge of cost functions, ramp rates, and capacity bounds of the aggregators. We develop meaningful abstractions for these quantities respecting the power flow constraints and taking into account the load uncertainties and propose a provably correct distributed algorithm for optimal disaggregation of regulation signals among the microgrids.
NeuralSim: Augmenting Differentiable Simulators with Neural Networks

Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation parameters that best fit the observed sensor readings. Nonetheless, these analytical models can only predict the dynamical behavior of systems for which they have been designed. In this work, we study the augmentation of a novel differentiable rigid-body physics engine via neural networks that is able to learn nonlinear relationships between dynamic quantities and can thus model effects not accounted for in traditional simulators. Such augmentations require less data to train and generalize better compared to entirely data-driven models. Through extensive experiments, we demonstrate the ability of our hybrid simulator to learn complex dynamics involving frictional contacts from real data, as well as match known models of viscous friction, and present an approach for automatically discovering useful augmentations. We show that, besides benefiting dynamics modeling, inserting neural networks can accelerate model-based control architectures. We observe a ten-fold speedup when replacing the QP solver inside a model-predictive gait controller for quadruped robots with a neural network, allowing us to significantly improve control delays as we demonstrate in real-hardware experiments. We publish code, additional results and videos from our experiments on our project webpage at https://sites.google. com/usc.edu/neuralsim.
Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs

Over the past decade there has been a growing interest in the development of parallel hardware systems for simulating large-scale networks of spiking neurons. Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, different types of spike generators, tools for recording spikes, state variables and parameters, and it supports user-definable models. The numerical solution of the differential equations of the dynamics of the AdEx models is performed through a parallel implementation, written in CUDA-C++, of the fifth-order Runge-Kutta method with adaptive step-size control. In this work we evaluate the performance of this library on the simulation of a cortical microcircuit model, based on LIF neurons and current-based synapses, and on balanced networks of excitatory and inhibitory neurons, using AdEx or Izhikevich neuron models and conductance-based or current-based synapses. On these models, we will show that the proposed library achieves state-of-the-art performance in terms of simulation time per second of biological activity. In particular, using a single NVIDIA GeForce RTX 2080 Ti GPU board, the full-scale cortical-microcircuit model, which includes about 77,000 neurons and 3 · 108 connections, can be simulated at a speed very close to real time, while the simulation time of a balanced network of 1,000,000 AdEx neurons with 1,000 connections per neuron was about 70 s per second of biological activity.
Hierarchical Multitask Learning Approach for BERT

Recent works show that learning contextualized embeddings for words is beneficial for downstream tasks. BERT is one successful example of this approach. It learns embeddings by solving two tasks, which are masked language model (masked LM) and the next sentence prediction (NSP). The pre-training of BERT can also be framed as a multitask learning problem. In this work, we adopt hierarchical multitask learning approaches for BERT pre-training. Pre-training tasks are solved at different layers instead of the last layer, and information from the NSP task is transferred to the masked LM task. Also, we propose a new pre-training task bigram shift to encode word order information. We choose two downstream tasks, one of which requires sentence-level embeddings (textual entailment), and the other requires contextualized embeddings of words (question answering). Due to computational restrictions, we use the downstream task data instead of a large dataset for the pre-training to see the performance of proposed models when given a restricted dataset. We test their performance on several probing tasks to analyze learned embeddings. Our results show that imposing a task hierarchy in pre-training improves the performance of embeddings.
Origins of size effects in initially dislocation-free single-crystal metallic micro- and nanocubes

We report phenomenal yield strengths, up to one fourth of the theoretical strength of silver, recorded in microcompression testing of initially dislocation free silver micro and nanocubes synthesized from a multistep seed growth process. These high strengths and the massive strain bursts that occur upon yield are results of the initially dislocation free single crystal structure of the pristine samples that yield through spontaneous nucleation of dislocations. When the pristine samples are exposed to a focused ion beam to fabricate pillars and then compressed, the dramatic strain burst does not occur, and they yield at a quarter of the strength of their pristine counterparts. Regardless of the defect state of the samples prior to testing, a size effect is apparent, where the yield strength increases as the sample size decreases. Since dislocation starvation and the single arm source mechanisms cannot explain a size effect on yield strength in dislocation free samples, we investigate the dislocation nucleation mechanisms controlling the size effect through careful experimental observations and molecular statics simulations. We find that intrinsic or extrinsic symmetry breakers such as surface defects, edge roundness, external sample shape, or a high vacancy concentration can influence dislocation nucleation, and thus contribute to the size effect on yield strength in initially dislocation-free samples.
On 2‐Selmer groups of twists after quadratic extension

Let E/Q$E/\mathbb {Q}$ be an elliptic curve with full rational 2‐torsion. As d$d$ varies over squarefree integers, we study the behaviour of the quadratic twists Ed$E_d$ over a fixed quadratic extension K/Q$K/\mathbb {Q}$ . We prove that for 100% of twists the dimension of the 2‐Selmer group over K$K$ is given by an explicit local formula, and use this to show that this dimension follows an Erdős–Kac type distribution. This is in stark contrast to the distribution of the dimension of the corresponding 2‐Selmer groups over Q$\mathbb {Q}$ , and this discrepancy allows us to determine the distribution of the 2‐torsion in the Shafarevich–Tate groups of the Ed$E_d$ over K$K$ also. As a consequence of our methods we prove that, for 100%$100\%$ of twists d$d$ , the action of Gal(K/Q)$\operatorname{Gal}(K/\mathbb {Q})$ on the 2‐Selmer group of Ed$E_d$ over K$K$ is trivial, and the Mordell–Weil group Ed(K)$E_d(K)$ splits integrally as a direct sum of its invariants and anti‐invariants. On the other hand, we give examples of thin families of quadratic twists in which a positive proportion of the 2‐Selmer groups over K$K$ have non‐trivial Gal(K/Q)$\operatorname{Gal}(K/\mathbb {Q})$ ‐action, illustrating that these previous results are genuinely statistical phenomena.
EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes

Multimodal large-scale datasets for outdoor scenes are mostly designed for urban driving problems. The scenes are highly structured and semantically different from scenarios seen in nature-centered scenes such as gardens or parks. To promote machine learning methods for nature-oriented applications, such as agriculture and gardening, we propose the multimodal synthetic dataset for Enclosed garDEN scenes (EDEN). The dataset features more than 300K images captured from more than 100 garden models. Each image is annotated with various low/high-level vision modalities, including semantic segmentation, depth, surface normals, intrinsic colors, and optical flow. Experimental results on the state-of-the-art methods for semantic segmentation and monocular depth prediction, two important tasks in computer vision, show positive impact of pre-training deep networks on our dataset for unstructured natural scenes. The dataset and related materials will be available at https://lhoangan.github. io/eden.
Mass–radius relation of intermediate-age disc super star clusters of M82

We present a complete set of structural parameters for a sample of 99 intermediate-age Super Star Cluster (SSCs) in the disk of M82, and carry out a survival analysis using the semi-analytical cluster evolution code EMACSS. The parameters are based on the profile-fitting analysis carried out in a previous work, with the mass-related quantities derived using a mass-to-light ratio for a constant age of 100 Myr. The SSCs follow a power-law mass function with an index ${\alpha}$=1.5, and a log-normal size function with a typical half-light radius, Rh=4.3 pc, which are both comparable with the values for clusters in the Magellanic Clouds, rather than in giant spirals. The majority of the SSCs follow a power-law mass-radius relation with an index of b=0.29${\pm}$0.05. A dynamical analysis of M82 SSCs using EMACSS suggests that 23% of the clusters are tidally-limited, with the rest undergoing expansion at present. Forward evolution of these clusters suggests that the majority would dissolve in ~2 Gyr. However, a group of four massive compact clusters, and another group of five SSCs at relatively large galactocentric distances, are found to survive for a Hubble time. The model-predicted mass, Rh, ${\mu_V}$ and core radius of these surviving SSCs at 12 Gyr are comparable with the corresponding values for the sample of Galactic globular clusters.
MOSAIC on the ELT: High-multiplex Spectroscopy to Unravel the Physics of Stars and Galaxies from the Dark Ages to the Present Day

The powerful combination of the cutting-edge multi-object spectrograph MOSAIC with the world largest telescope, the ELT, will allow us to probe deeper into the Universe than was possible. MOSAIC is an extremely efficient instrument in providing spectra for the numerous faint sources in the Universe, including the very first galaxies and sources of cosmic reionization. MOSAIC has a high multiplex in the NIR and in the VIS, in addition to multi-Integral Field Units (Multi-IFUs) in NIR. As such it is perfectly suited to carry out an inventory of dark matter (from rotation curves) and baryons in the cool-warm gas phases in galactic haloes at z=3-4. MOSAIC will enable detailed maps of the intergalactic medium at z=3, the evolutionary history of dwarf galaxies during a Hubble time, the chemistry directly measured from stars up to several Mpc. Finally, it will measure all faint features seen in cluster gravitational lenses or in streams surrounding nearby galactic halos, providing MOSAIC to be a powerful instrument with an extremely large space of discoveries. The preliminary design of MOSAIC is expected to begin next year, and its level of readiness is already high, given the instrumental studies made by the team.
Chatbots as Conversational Healthcare Services

Chatbots are emerging as a promising platform for accessing and delivering healthcare services. The evidence is in the growing number of publicly available chatbots aiming at taking an active role in the provision of prevention, diagnosis, and treatment services. This article takes a closer look at how these emerging chatbots address design aspects relevant to healthcare service provision, emphasizing the human–AI interaction aspects and the transparency in AI automation and decision making.