Miracle-dz/UniFAR-FTUs huggingface dataset:
Endothelial-to-Mesenchymal Transition in Pulmonary Hypertension

Background— The vascular remodeling responsible for pulmonary arterial hypertension (PAH) involves predominantly the accumulation of &agr;-smooth muscle actin–expressing mesenchymal-like cells in obstructive pulmonary vascular lesions. Endothelial-to-mesenchymal transition (EndoMT) may be a source of those &agr;-smooth muscle actin–expressing cells. Methods and Results— In situ evidence of EndoMT in human PAH was obtained by using confocal microscopy of multiple fluorescent stainings at the arterial level, and by using transmission electron microscopy and correlative light and electron microscopy at the ultrastructural level. Findings were confirmed by in vitro analyses of human PAH and control cultured pulmonary artery endothelial cells. In addition, the mRNA and protein signature of EndoMT was recognized at the arterial and lung level by quantitative real-time polymerase chain reaction and Western blot analyses. We confirmed our human observations in established animal models of pulmonary hypertension (monocrotaline and SuHx). After establishing the first genetically modified rat model linked to BMPR2 mutations (BMPR2&Dgr;140Ex1/+ rats), we demonstrated that EndoMT is linked to alterations in signaling of BMPR2, a gene that is mutated in 70% of cases of familial PAH and in 10% to 40% of cases of idiopathic PAH. We identified molecular actors of this pathological transition, including twist overexpression and vimentin phosphorylation. We demonstrated that rapamycin partially reversed the protein expression patterns of EndoMT, improved experimental PAH, and decreased the migration of human pulmonary artery endothelial cells, providing the proof of concept that EndoMT is druggable. Conclusions— EndoMT is linked to alterations in BPMR2 signaling and is involved in the occlusive vas cular remodeling of PAH, findings that may have therapeutic implications.
Computational Modeling of Transcatheter Aortic Valves

Transcatheter aortic valve replacement (TAVR) is an established therapy alternative to surgical valve replacement in high-risk and intermediate-risk patients with severe aortic stenosis. Currently, although TAVR is an alternative and less-invasive treatment for highrisk and intermediate-risk patients, surgical aortic valves replacement (SAVR) was still considered as the gold standard for low-risk patients. TAVR could potentially be applied to lower-risk younger patients if the indications can be safely expanded to the patients and transcatheter aortic valve (TAV) long-term durability can match with that of surgical bioprostheses. In contrast to surgical aortic valves (SAVs), there have been limited clinical data on the long-term durability of TAV devices. In the absence of enough long-term valve durability data, accurate structural simulations and computational modeling become an integral part of the evaluation. Thus, the objectives of this dissertation were to employ invitro experiments and inverse finite element (FE) analyses to obtain accurate material properties of soft tissue employed in commercial available TAVs and then to implement them in computational simulations to determine leaflet stress and strain distributions for proper assessment of the TAVs long-term durability. Therefore, the main goal of this study was to develop an automated computational framework to minimize the peak stress on the leaflets under and optimize the TAV leaflet shape under physiological loading conditions.
Learning to compare image patches via convolutional neural networks

In this paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems. To encode such a function, we opt for a CNN-based model that is trained to account for a wide variety of changes in image appearance. To that end, we explore and study multiple neural network architectures, which are specifically adapted to this task. We show that such an approach can significantly outperform the state-of-the-art on several problems and benchmark datasets.
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs. berkeley.edu/~rbg/rcnn.
Dynamic Evaluation of Neural Sequence Models

We present methodology for using dynamic evaluation to improve neural sequence models. Models are adapted to recent history via a gradient descent based mechanism, causing them to assign higher probabilities to re-occurring sequential patterns. Dynamic evaluation outperforms existing adaptation approaches in our comparisons. Dynamic evaluation improves the state-of-the-art word-level perplexities on the Penn Treebank and WikiText-2 datasets to 51.1 and 44.3 respectively, and the state-of-the-art character-level cross-entropies on the text8 and Hutter Prize datasets to 1.19 bits/char and 1.08 bits/char respectively.
Seeing Through Noise: Visually Driven Speaker Separation And Enhancement

Isolating the voice of a specific person while filtering out other voices or background noises is challenging when video is shot in noisy environments. We propose audio-visual methods to isolate the voice of a single speaker and eliminate unrelated sounds. First, face motions captured in the video are used to estimate the speaker's voice, by passing the silent video frames through a video-to-speech neural network-based model. Then the speech predictions are applied as a filter on the noisy input audio. This approach avoids using mixtures of sounds in the learning process, as the number of such possible mixtures is huge, and would inevitably bias the trained model. We evaluate our method on two audio-visual datasets, GRID and TCD- TIMIT, and show that our method attains significant SDR and PESQ improvements over the raw video-to-speech predictions, and a well-known audio-only method.
Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries

Estimating vaccination uptake is an integral part of ensuring public health. It was recently shown that vaccination uptake can be estimated automatically from web data, instead of slowly collected clinical records or population surveys. All prior work in this area assumes that features of vaccination uptake collected from the web are temporally regular. We present the first ever method to remove this assumption from vaccination uptake estimation: our method dynamically adapts to temporal fluctuations in time series web data used to estimate vaccination uptake. We show our method to outperform the state of the art compared to competitive baselines that use not only web data but also curated clinical data. This performance improvement is more pronounced for vaccines whose uptake has been irregular due to negative media attention (HPV-1 and HPV-2), problems in vaccine supply (DiTeKiPol), and targeted at children of 12 years old (whose vaccination is more irregular compared to younger children).
The University of Edinburgh’s Neural MT Systems for WMT17

This paper describes the University of Edinburgh's submissions to the WMT17 shared news translation and biomedical translation tasks. We participated in 12 translation directions for news, translating between English and Czech, German, Latvian, Russian, Turkish and Chinese. For the biomedical task we submitted systems for English to Czech, German, Polish and Romanian. Our systems are neural machine translation systems trained with Nematus, an attentional encoder-decoder. We follow our setup from last year and build BPE-based models with parallel and back-translated monolingual training data. Novelties this year include the use of deep architectures, layer normalization, and more compact models due to weight tying and improvements in BPE segmentations. We perform extensive ablative experiments, reporting on the effectivenes of layer normalization, deep architectures, and different ensembling techniques.
Device Authentication Codes based on RF Fingerprinting using Deep Learning

In this paper, we propose Device Authentication Code (DAC), a novel method for authenticating IoT devices with wireless interface by exploiting their radio frequency (RF) signatures. The proposed DAC is based on RF fingerprinting, information theoretic method, feature learning, and discriminatory power of deep learning. Specifically, an autoencoder is used to automatically extract features from the RF traces, and the reconstruction error is used as the DAC and this DAC is unique to the device and the particular message of interest. Then Kolmogorov-Smirnov (K-S) test is used to match the distribution of the reconstruction error generated by the autoencoder and the received message, and the result will determine whether the device of interest belongs to an authorized user. We validate this concept on two experimentally collected RF traces from six ZigBee and five universal software defined radio peripheral (USRP) devices, respectively. The traces span a range of Signalto- Noise Ratio by varying locations and mobility of the devices and channel interference and noise to ensure robustness of the model. Experimental results demonstrate that DAC is able to prevent device impersonation by extracting salient features that are unique to any wireless device of interest and can be used to identify RF devices. Furthermore, the proposed method does not need the RF traces of the intruder during model training yet be able to identify devices not seen during training, which makes it practical.
Hispidin and Lepidine E: two Natural Compounds and Folic acid as Potential Inhibitors of 2019-novel coronavirus Main Protease (2019-nCoVMpro), molecular docking and SAR study.

2019-nCoVis a novel coronavirus was isolated and identified in 2019 in Wuhan, China. On 17 February and according to the world health organization, a number of 71 429 confirmed cases worldwide, among them 2162 new cases recorded in the last 24 hours. One month later the confirmed cases jumped to 179111, with 11525new cases in the last 24 hours, with 7426total deaths. There is no drug or vaccine for humanand animal coronavirus.The inhibition of 3CL hydrolase enzyme provides a promising therapeutic principle for developing treatments against CoViD-19.The 3CLpro (Mpro) known for involving in counteracting the host innate immune response.Thiswork presents the inhibitory effect of some natural compounds against 3CL hydrolase enzyme, and explain the main interactions in inhibitor-enzyme complex. Molecular docking study carried out using Autodock Vina. By screening several molecules, we identified three candidate agents that inhibit the main protease of coronavirus. Hispidin, lepidine E,and folic acid bound tightly in the enzyme, strong hydrogen bonds have been formed (1.69-1.80Å) with the active site residues.This study provides a possible therapeutic strategy for CoViD-19.
Fermionic versus bosonic behavior of confined Wigner molecules

We assess whether a confined Wigner molecule constituted by $2N$ fermions behaves as $N$ bosons or $2N$ fermions. Following the work by C. K. Law [Phys. Rev. A \textbf{71}, 034306 (2005)] and Chudzicki et al. [Phys. Rev. Lett. \textbf{104}, 070402 (2010)] we discuss the physical meaning and the reason why a large amount of entanglement is needed in order to ensure a bosonic composite behavior. By applying a composite boson ansatz, we found that a Wigner molecule confined in two dimensional traps presents a bosonic behavior induced by symmetry. The two-particle Wigner molecule ground state required by the composite boson ansatz was obtained within the harmonic approximation in the strong interacting regime. Our approach allows us to address few-particle states (widely studied within a variety of theoretical and numerical techniques) as well as a large number of particles (difficult to address due to computational costs). For a large number of particles, we found strong fermionic correlations exposed by the suppression of particle fluctuations. For a small number of particles, we show that the wave function calculated within the composite boson ansatz captures the Friedel-Wigner transition. The latter is shown in a regime in which strong correlations due to the Pauli exclusion principle arise, therefore, we conclude that the coboson ansatz reproduces the many particle physics of a confined Wigner molecule, even in the presence of strong deviations of the ideal bosonic behavior due to fermionic correlations.
On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond

Variational autoencoders (VAEs) combine latent variables with amortized variational inference, whose optimization usually converges into a trivial local optimum termed posterior collapse, especially in text modeling. By tracking the optimization dynamics, we observe the encoder-decoder incompatibility that leads to poor parameterizations of the data manifold. We argue that the trivial local optimum may be avoided by improving the encoder and decoder parameterizations since the posterior network is part of a transition map between them. To this end, we propose Coupled-VAE, which couples a VAE model with a deterministic autoencoder with the same structure and improves the encoder and decoder parameterizations via encoder weight sharing and decoder signal matching. We apply the proposed Coupled-VAE approach to various VAE models with different regularization, posterior family, decoder structure, and optimization strategy. Experiments on benchmark datasets (i.e., PTB, Yelp, and Yahoo) show consistently improved results in terms of probability estimation and richness of the latent space. We also generalize our method to conditional language modeling and propose Coupled-CVAE, which largely improves the diversity of dialogue generation on the Switchboard dataset.
Graph-Structured Referring Expression Reasoning in the Wild

Grounding referring expressions aims to locate in an image an object referred to by a natural language expression. The linguistic structure of a referring expression provides a layout of reasoning over the visual contents, and it is often crucial to align and jointly understand the image and the referring expression. In this paper, we propose a scene graph guided modular network (SGMN), which performs reasoning over a semantic graph and a scene graph with neural modules under the guidance of the linguistic structure of the expression. In particular, we model the image as a structured semantic graph, and parse the expression into a language scene graph. The language scene graph not only decodes the linguistic structure of the expression, but also has a consistent representation with the image semantic graph. In addition to exploring structured solutions to grounding referring expressions, we also propose Ref-Reasoning, a large-scale real-world dataset for structured referring expression reasoning. We automatically generate referring expressions over the scene graphs of images using diverse expression templates and functional programs. This dataset is equipped with real-world visual contents as well as semantically rich expressions with different reasoning layouts. Experimental results show that our SGMN not only significantly outperforms existing state-of-the-art algorithms on the new Ref-Reasoning dataset, but also surpasses state-of-the-art structured methods on commonly used benchmark datasets. It can also provide interpretable visual evidences of reasoning.
Cosmology-insensitive estimate of IGM baryon mass fraction from five localized fast radio bursts

Five fast radio bursts (FRBs), including three apparently non-repeating ones FRB 180924, FRB 181112, and FRB 190523, and two repeaters, FRB 121102 and FRB 180916.J0158+65, have already been localized so far. We apply a method developed recently by us (Li et al. 2019) to these five localized FRBs to give a cosmology-insensitive estimate of the fraction of baryon mass in the IGM, $f_{\rm IGM}$. Using the measured dispersion measure (DM) and luminosity distance $d_{\rm L}$ data (inferred from the FRB redshifts and $d_{\rm L}$ of type Ia supernovae at the same redshifts) of the five FRBs, we constrain the local $f_{\rm IGM} = 0.84^{+0.16}_{-0.22}$ with no evidence of redshift dependence. This cosmology-insensitive estimate of $f_{\rm IGM}$ from FRB observations is in excellent agreement with previous constraints using other probes. Moreover, using the three apparently non-repeating FRBs only we get a little looser but consistent result $f_{\rm IGM} = 0.74^{+0.24}_{-0.18}$. In these two cases, reasonable estimations for the host galaxy DM contribution (${\rm DM_{host}}$) can be achieved by modelling it as a function of star formation rate. The constraints on both $f_{\rm IGM}$ and ${\rm DM_{host}}$ are expected to be significantly improved with the rapid progress in localizing FRBs.
Investigating the relationship between cosmic curvature and dark energy models with the latest supernova sample

We investigate the relationship between the cosmic curvature and the model of dark energy (hereafter DE) with the recent Type Ia supernovae (hereafter SNe Ia) data, i.e., the Pantheon sample including 1048 SNe Ia with $0.01 < z < 2.3$. We obtain the measurements of the dimensionless spatial curvature density today, i.e., $\Omega_{k0} = -0.062^{+0.189}_{-0.169}, -0.004^{+0.228}_{-0.134}, 0.127^{+0.280}_{-0.276}$ and $0.422^{+0.213}_{-0.338}$ at 68\% confidence level (CL), respectively, in the scenarios of $\Lambda$CDM, $\phi$CDM (i.e., scalar field dark energy), $\omega$CDM and $\omega_0\omega_a$CDM models. In the scenario of $\Lambda$CDM model, a closed universe is preferred by the Pantheon sample, which is consistent with that from the Planck CMB spectra. However, the uncertainty of $\Omega_{k0}$ from the Pantheon SNe sample is about 8 times larger than that from the Planck data, so the former one supports a closed universe at a much lower CL than that from the latter one. An open unverse is supported by the Pantheon sample at $\sim$32\% and $\sim$78\% CLs, respectively, in the $\omega$CDM and $\omega_0\omega_a$CDM models. Among these models, the $\phi$CDM model is the one which supports the flat universe most strongly. It shows that $\Omega_{k0}$ is significantly dependent on the adopted model of dark energy, and there is a negative correlation between $\Omega_{k0}$ and the equation of state of DE.
Higher chromatic Thom spectra via unstable homotopy theory

We investigate implications of an old conjecture in unstable homotopy theory related to the Cohen-Moore-Neisendorfer theorem and a conjecture about the $\mathbf{E}_{2}$-topological Hochschild cohomology of certain Thom spectra (denoted $A$, $B$, and $T(n)$) related to Ravenel's $X(p^n)$. We show that these conjectures imply that the orientations $\mathrm{MSpin}\to \mathrm{ko}$ and $\mathrm{MString}\to \mathrm{tmf}$ admit spectrum-level splittings. This is shown by generalizing a theorem of Hopkins and Mahowald, which constructs $\mathrm{H}\mathbf{F}_p$ as a Thom spectrum, to construct $\mathrm{BP}\langle{n-1}\rangle$, $\mathrm{ko}$, and $\mathrm{tmf}$ as Thom spectra (albeit over $T(n)$, $A$, and $B$ respectively, and not over the sphere). This interpretation of $\mathrm{BP}\langle{n-1}\rangle$, $\mathrm{ko}$, and $\mathrm{tmf}$ offers a new perspective on Wood equivalences of the form $\mathrm{bo} \wedge C\eta \simeq \mathrm{bu}$: they are related to the existence of certain EHP sequences in unstable homotopy theory. This construction of $\mathrm{BP}\langle{n-1}\rangle$ also provides a different lens on the nilpotence theorem. Finally, we prove a $C_2$-equivariant analogue of our construction, describing $\underline{\mathrm{H}\mathbf{Z}}$ as a Thom spectrum.
MPNet: Masked and Permuted Pre-training for Language Understanding

BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models. Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for pre-training to address this problem. We argue that XLNet does not leverage the full position information of a sentence and thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g., BERT, XLNet, RoBERTa) under the same model setting. We release the code and pre-trained model in GitHub\footnote{\url{this https URL}}.
Candidate fossil groups in the CFHTLS: a probabilistic approach

Context. Fossil groups (FGs) have been discovered 25 years ago, and are now defined as galaxy groups with an X-ray luminosity higher than $ 10^{42}\,h_{50}^{-2} $ erg s−1 and a brightest group galaxy brighter than the other group members by at least two magnitudes. However, the scenario of their formation remains controversial. Aims. We propose here a probabilistic analysis of FGs, extracted from the large catalog of candidate groups and clusters previously detected in the CFHTLS survey based on photometric redshifts to investigate their position in the cosmic web and probe their environment. Methods. Based on spectroscopic and photometric redshifts, we estimated the probability of galaxies to belong to a galaxy structure, and by imposing the condition that the brightest group galaxy is at least brighter than the others by two magnitudes, we computed the probability for a given galaxy structure to be a FG. We analyzed the mass distribution of these candidate FGs, and estimated their distance to the filaments and nodes of the cosmic web in which they are embedded. Results. We find that structures with masses lower than 2.4 × 1014 M⊙ have the highest probabilities of being fossil groups (PFG). Overall, structures with PFG ≥ 50% are located close to the cosmic web filaments (87% are located closer than 1 Mpc to their nearest filament). They are preferentially four times more distant from their nearest node than from their nearest filament. Conclusions. We confirm that FGs have low masses and are rare. They seem to reside closely to cosmic filaments and do not survive in nodes. Being in a poor environment might therefore be the driver of FG formation because the number of nearby galaxies is not sufficient to compensate for the cannibalism of the central group galaxy.