Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Ultrafast Ultrasound Imaging of Cerebral Perfusion correlates with Ischemic Stroke outcomes and responses to treatment in Mice

In the field of ischemic cerebral injury, precise characterization of neurovascular hemodynamic is required to select candidates for reperfusion treatments. It is thus admitted that advanced imaging-based approaches would be able to better diagnose and prognose those patients and would contribute to better clinical care. Current imaging modalities like MRI allow a precise diagnostic of cerebral injury but suffer from limited availability and transportability. The recently developed ultrafast ultrasound could be a powerful tool to perform emergency imaging and long term follow-up of cerebral perfusion, which could, in combination with MRI, improve imaging solutions for neuroradiologists. Methods: In this study, in a model of in situ thromboembolic stroke in mice, we compared a control group of non-treated mice (N=10) with a group receiving the gold standard pharmacological stroke therapy (N=9). We combined the established tool of magnetic resonance imaging (7T MRI) with two innovative ultrafast ultrasound methods, ultrafast Doppler and Ultrasound Localization Microscopy, to image the cerebral blood volumes at early and late times after stroke onset and compare with the formation of ischemic lesions. Results: Our study shows that ultrafast ultrasound can be used through the mouse skull to monitor cerebral perfusion during ischemic stroke. In our data, the monitoring of the reperfusion following thrombolytic within the first 2 h post stroke onset matches ischemic lesions measured 24 h. Moreover, similar results can be made with Ultrasound Localization Microscopy which could make it applicable to human patients in the future. Conclusion: We thus provide the proof of concept that in a mouse model of thromboembolic stroke with an intact skull, early ultrafast ultrasound can be indicative of responses to treatment and cerebral tissue fates following stroke. It brings new tools to study ischemic stroke in preclinical models and is the first step prior translation to the clinical settings.
Hierarchical Graph Attention Network for Visual Relationship Detection

Visual Relationship Detection (VRD) aims to describe the relationship between two objects by providing a structural triplet shown as . Existing graph-based methods mainly represent the relationships by an object-level graph, which ignores to model the triplet-level dependencies. In this work, a Hierarchical Graph Attention Network (HGAT) is proposed to capture the dependencies on both object-level and triplet-level. Object-level graph aims to capture the interactions between objects, while the triplet-level graph models the dependencies among relation triplets. In addition, prior knowledge and attention mechanism are introduced to fix the redundant or missing edges on graphs that are constructed according to spatial correlation. With these approaches, nodes are allowed to attend over their spatial and semantic neighborhoods' features based on the visual or semantic feature correlation. Experimental results on the well-known VG and VRD datasets demonstrate that our model significantly outperforms the state-of-the-art methods.
IEOR8100: Economics, AI, and Optimization Lecture Note 7: Search in Extensive-Form Games

We previously saw how to compute a Nash equilibrium of a two-player zero-sum extensive-form game (EFG) by using dilated distance-generating functions or the CFR framework. We also saw that even if computing gradients gt = Ayt is too time-consuming we can still run algorithms using gradient estimates constructed via sampling. However, for some real-world games such as twoplayer no-limit Texas hold’em, this is still not enough. The game tree in this game has roughly 10170 nodes, and the strategy space is much too large to even write down strategy iterates. Faced with this situation, we need to make even coarser approximations to our problem. One major innovation for solving large-scale poker games was the use of real-time search. Traditionally, poker AIs were created by precomputing an approximate Nash equilibrium for some extremely coarsened representation of the full game using e.g. CFR+. Then, that offline strategy was simply employed during play. In real-time search, the precomputed Nash equilibrium approximation is refined in real time for subgames encountered during live play. This allows the AI to reason in much more detail, especially towards the end of the game, where the encountered subtree is manageable in size. In order to understand how search works in EFGs, we will first show how it works in the simpler setting of perfect-information EFGs, where there are no information sets, and so players know exactly which node they are currently at. Search in perfect-information EFGs has historically been extremely successful, it was used in AI milestones on Chess and Go.
Effect of Dexamethasone in Hospitalized Patients with COVID-19: Preliminary Report

Background: Coronavirus disease 2019 (COVID-19) is associated with diffuse lung damage. Corticosteroids may modulate immune-mediated lung injury and reducing progression to respiratory failure and death. Methods: The Randomised Evaluation of COVID-19 therapy (RECOVERY) trial is a randomized, controlled, open-label, adaptive, platform trial comparing a range of possible treatments with usual care in patients hospitalized with COVID-19. We report the preliminary results for the comparison of dexamethasone 6 mg given once daily for up to ten days vs. usual care alone. The primary outcome was 28-day mortality. Results: 2104 patients randomly allocated to receive dexamethasone were compared with 4321 patients concurrently allocated to usual care. Overall, 454 (21.6%) patients allocated dexamethasone and 1065 (24.6%) patients allocated usual care died within 28 days (age-adjusted rate ratio [RR] 0.83; 95% confidence interval [CI] 0.74 to 0.92; P<0.001). The proportional and absolute mortality rate reductions varied significantly depending on level of respiratory support at randomization (test for trend p<0.001): Dexamethasone reduced deaths by one-third in patients receiving invasive mechanical ventilation (29.0% vs. 40.7%, RR 0.65 [95% CI 0.51 to 0.82]; p<0.001), by one-fifth in patients receiving oxygen without invasive mechanical ventilation (21.5% vs. 25.0%, RR 0.80 [95% CI 0.70 to 0.92]; p=0.002), but did not reduce mortality in patients not receiving respiratory support at randomization (17.0% vs. 13.2%, RR 1.22 [95% CI 0.93 to 1.61]; p=0.14). Conclusions: In patients hospitalized with COVID-19, dexamethasone reduced 28-day mortality among those receiving invasive mechanical ventilation or oxygen at randomization, but not among patients not receiving respiratory support.
Labor Market Effects of Local Spread of COVID-19

This paper explores the short-term local labor market impact of the spread of COVID-19 in the United States, using the Current Employment Statistics survey and the Current Population Survey microdata. It uses the longitudinal aspect of both these surveys to measure changes in employment for business establishments and household members. I match the survey respondent to the measured local incidence of confirmed COVID-19 cases, using confidential information on county of location, to estimate the impact of the local incidence of the virus, after controlling for multiple measures of government intervention. I find the greatest declines in employment in counties with higher incidence of COVID-19. These effects vary by industry: leisure / hospitality and other services have large declines in employment relative to the effect of the incidence of the virus, while the employment decline in construction and transportation and warehousing depends more on the local incidence of the virus. Finance / insurance, a very telework-friendly industry, is unaffected by the incidence of the virus. These short-term employment effects have implications for future employment patterns as government restrictions are relaxed and business owners begin to decide whether to open their businesses while the virus is still active in the United States. JEL codes: J01, J21, J63
Detailed Investigation of Quantitative Trait Loci (QTLs) Involved in Superior Very- High-Gravity (VHG) Fermentation Performance

Bioethanol is one of the most important renewable fuels contributing to the reduction of negative environmental impacts generated by the worldwide utilization of fossil fuels [1]. One versatile technology in process optimization for bioethanol production is Very-High-Gravity (VHG) fermentation which offers great savings in water and energy requirements through fermentation of substrate containing higher sugar concentrations which results in increased final ethanol concentration. [2]. However, the high concentrations of sugar in the beginning of the fermentation expose yeast cells to osmotic stress leading to sluggish fermentations [3]. The goal of this work is to identify superior alleles that can improve the fermentation performance of industrial strains under VHG conditions. Previously, an osmotolerant strain (X6003) was selected as superior strain and Ethanol Red was selected as the inferior strain to be improved for VHG fermentation. Quantitative trait locus (QTL) mapping was achieved by Pooled-segregant whole-genome sequencing whereby 10 QTLs that might contain genes that play a role in superior osmotolerance were identified. In this thesis, two of those QTLs were studied in detail. With this purpose, strains were constructed to be used in 'bulk' reciprocal hemizygosity analysis (bRHA). For this, two isogenic strains with the background from both parent strains were constructed in which a particular DNA fragment was deleted in either the chromosome coming from the superior, or from the inferior strain. These strains were subjected to VHG fermentation in order to compare their phenotype and evaluate if the genes from one genetic background are advantageous over those from the other. DCK1 was identified as a causative gene, since its allele from X6003 conferred a better fermentation performance in VHG conditions than the one from Ethanol Red.
Structural, physiological and metabolic integrated responses of two tomato (Solanum lycopersicum L.) cultivars during leaf rehydration

The aim of this work was to characterize the recovery responses of two tomato cultivars, Rio Grande (RG) and Pera Quibor (PQ), to rehydration after water deficit. Recovery responses were measured in terms of timing and magnitude, and analysed as indicative of tolerance. The parameters evaluated were: soil water potential (soil Ψw) and leaf water potential (leaf Ψw); leaf osmotic potential (leaf Ψs); leaf relative water content (LRWC); stomatal conductance (gs); Rubisco activity; protein content; fresh mass (FM) and dry mass (DM); leaf area (LA); specific leaf area (SLA) and leaf density (D). Different types of responses were observed: as common responses of both cultivars, full recovery of soil Ψw, Ψs, and LRWC were found regardless of stress level and length of rehydration time. Recovery of protein was full in PQ and partial in RG. Full recovery of gs and fresh mass and partial recovery of Ψw and leaf area was observed in both cultivars regardless of the level of stress but regarding rehydration time. Full recovery of Rubisco activity was found regarding level of stress and rehydration time. As different responses between cultivars, full recovery of DM was found in PQ and of SLA and leaf D in RG regardless of the level of stress but regarding rehydration time. Partial recovery of SLA and leaf D was found in PQ regarding both level of stress and rehydration time. The delayed recovery of leaf structural changes promoted full recovery of DM and protein in PQ. The correlations between physiological and metabolic parameters and observed sclerophylly indices, reveal integrated tolerance mechanisms intended to protect plant cultivars from intermittent changes in water conditions.
Recasting “Substantial Equivalence”: Transatlantic Governance of GM Food

When an intense public controversy erupted around agricultural biotechnology in the late 1990s, critics found more opportunities to challenge risk-assessment criteria and test methods for GM products. In relation to GM food, they criticised the concept of “substantial equivalence”, which EU and US regulators had adopted as the basis for a harmonised “science-based” approach to risk assessment. Scientific uncertainty was framed in different ways by competing policy agendas. “Substantial equivalence” was contested and was eventually recast to accommodate some criticisms. To explain how the concept changed, this paper links two analytical perspectives. “Regulatory science” perspectives illuminate how the “scientification of politics” and “politicisation of science” led to shifts in the boundary between science and policy. “Governance” perspectives illuminate how the “collective problem” for policy was redefined to provide a new common ground for some stakeholders. Overall “substantial equivalence” was recast to govern the social conflict and address legitimacy problems of regulatory procedures.
UTILIZATION OF CARBOHYDRATES BY BEAUVERIA BASSIANA ISOLATES OBTAINED FROM FOREST PESTS

Carbohydrate utilization profiles of ten isolates of the entomopathogenic fungus Beauveria bassiana (Bals.-Criv.) Vuillemin were studied and compared to assist in determining their phenotypic characterization. Isolates were obtained in pure cultures from dead individuals of several forest pests collected from different regions in Bulgaria. Studies on utilization profiles were based on the acidification of twenty carbohydrates. The results indicate that sucrose, maltose and trehalose were assimilated at a high degree compared to esculin, arabinose and dul- citol. According to the results of a cluster analysis of the carbohydrate utilization profiles, the B. bassiana isolates were divided into two larger groupings. All isolates in the first larger cluster were obtained from the coleopteran insects - Stenomax aeneus, Ips typographus, I. sexdentatus and Dryocoetes autographus. Isolates from the other cluster were obtained from the lepidopteran larvae of Thaumetopoea pityocampa and Lymantria dispar, and from an adult of the coleopteran species Hylurgops palliatus. We determined that each B. bassiana isolate exhibited a different and specific carbohydrate utilization profile but differences at the p-level < 0.05 were significant among some of them. The most distinguishable was the isolate 560Bb obtained from T. pityocampa. Differences between the isolate 560Bb and the other nine B. bassiana isolates were highly significant at the p-level < 0.005. Isolate 433Bb obtained from a dead adult of I. typogra- phus was significantly different from five of the studied isolates at the p-level < 0.05.
Assessing the role of the transcription factor FOXC1 in the expression and regulation of the Adherens junction protein N-Cadherin during corneal endothelium development.

The proper organization and differentiation of the anterior segment is pivotal for normal eye development. Neural crest-derived POM cells are key contributors to correct anterior segment formation, differentiating to form the monolayered corneal endothelium. Mice with homozygous null mutations in the forkhead transcription factor gene, Foxc1, fail to develop a proper corneal endothelium stabilized by adherens junctions, with the endothelium adhering to the lens, preventing anterior chamber separation. The aim of this study was to evaluate the interaction between Foxc1 and the adherens junction protein, N-cadherin, as well as an associated gene, Msx1, during key stages in corneal endothelium development. Foxc1 was over-expressed in E12.5 and E13.5 POM cells and qPCR was carried out to determine the effect of Foxc1 on N-cadherin and Msx1 gene expression. Data showed over-expression of Foxc1 in wildtype E12.5 and E13.5 POM cells to cause significant fluctuations in N-cadherin and Msx1 expression (p < 0.05). POM cells were then transfected with a Foxc1 knock-down plasmid or the Foxc1 overexpression plasmid to evaluate the effect of Foxc1 on N-cadherin protein expression by Western blot analysis, however, these results were inconsistent with the gene expression analyses with no significant differences in N-cadherin expression detected. N-cadherin protein expression and localization was then further assessed by means of immunocytochemistry (ICC) and confocal microscopy in monolayer and hanging-drop POM cell cultures. Both qPCR and confocal microscopy data showed consistency, indicating increased amounts of N-cadherin in E12.5 cells relative to E13.5 cells, with membrane-bound N-cadherin showing a clear lattice-work pattern in hanging drop culture. Foxc1 over-expression/knock-down studies on E12.5 and E13.5 POM cells together suggest that N-cadherin is transcriptionally regulated by Foxc1 and that Foxc1 has a threshold level at which it is able to exert control over N-cadherin in POM cells. Foxc1 expression is therefore essential in establishing N-cadherin adhesion junctions in the corneal endothelium. Preliminary data also suggests that Msx1 may directly interact with Foxc1 in POM cells, however, further studies must be undertaken to verify and establish the effects of Foxc1/N-cadherin/ Msx1 interaction in the development of a cohesive, integrated corneal endothelium and functional anterior segment.
Learning of Discrete Graphical Models with Neural Networks

Graphical models are widely used in science to represent joint probability distributions with an underlying conditional dependence structure. The inverse problem of learning a discrete graphical model given i.i. d samples from its joint distribution can be solved with near-optimal sample complexity using a convex optimization method known as Generalized Regularized Interaction Screening Estimator (GRISE). But the computational cost of GRISE becomes prohibitive when the energy function of the true graphical model has higher order terms. We introduce NN-GRISE, a neural net based algorithm for graphical model learning, to tackle this limitation of GRISE. We use neural nets as function approximators in an interaction screening objective function. The optimization of this objective then produces a neural-net representation for the conditionals of the graphical model. NN-GRISE algorithm is seen to be a better alternative to GRISE when the energy function of the true model has a high order with a high degree of symmetry. In these cases, NN-GRISE is able to find the correct parsimonious representation for the conditionals without being fed any prior information about the true model. NN-GRISE can also be used to learn the underlying structure of the true model with some simple modifications to its training procedure. In addition, we also show a variant of NN-GRISE that can be used to learn a neural net representation for the full energy function of the true model.
Fast Generation of Large-scale Structure Density Maps via Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a recent advancement in unsupervised machine learning. They are a cat-and-mouse game between two neural networks: (1) a discriminator network which learns to validate whether a sample is real or fake compared to a training set and (2) a generator network which learns to generate data that appear to belong to the training set. Both networks learn from each other until training is complete and the generator network is able to produce samples that are indistinguishable from the training set. We find that GANs are well-suited for fast generation of novel 3D density maps that are indistinguishable from those obtained from N-body simulations. In a matter of seconds, a fully trained GAN can generate thousands of density maps at different epochs in the history of the universe. These GAN-generated maps can then be used to study the evolution of large-scale structure over time.
Production of protein nanoparticles for food and drug delivery system.

Proteins nanoparticles are one of the new methods for food delivery systems. The protein nanoparticles which represent promising carriers for delivery are fabricated based on different methods. Synthetic protein nanostructure acts as surrogate mimics such as viruses and plasmid for food and drug delivery system. The benefits of protein nanoparticles include non-toxicity, stability for long duration, nonantigenicity and biodegradability. The purpose of this paper was to explain different ways of fabrication of nanoparticles in the unique range which is suitable for the food and drug delivery system
Hierarchical Reinforcement Learning for Deep Goal Reasoning: An Expressiveness Analysis

Hierarchical DQN (h-DQN) is a two-level architecture of feedforward neural networks where the meta level selects goals and the lower level takes actions to achieve the goals. We show tasks that cannot be solved by h-DQN, exemplifying the limitation of this type of hierarchical framework (HF). We describe the recurrent hierarchical framework (RHF), generalizing architectures that use a recurrent neural network at the meta level. We analyze the expressiveness of HF and RHF using context-sensitive grammars. We show that RHF is more expressive than HF. We perform experiments comparing an implementation of RHF with two HF baselines; the results corroborate our theoretical findings.
The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2

We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on typologically diverse morphological inflection and Task 2 on unsupervised morphological paradigm completion. The former consists of generating morphological inflections from a lemma and a set of morphosyntactic features describing the target form. The latter requires generating entire paradigms for a set of given lemmas from raw text alone. We model morphological inflection as a sequence-to-sequence problem, where the input is the sequence of the lemma’s characters with morphological tags, and the output is the sequence of the inflected form’s characters. First, we apply a transformer model to the task. Second, as inflected forms share most characters with the lemma, we further propose a pointer-generator transformer model to allow easy copying of input characters.
The infalling photon, the infalling particle, and the observer at rest near the horizon of a black hole

When a massive test particle or a photon fall radially into a black hole, their energy, as measured by a static observer located very close to the horizon, diverges. In introductory courses on general relativity, this fact gives rise to questions about the reality of this divergence and its eventual effect on the geometry of the black hole. We address these concerns and show that, eventually, it is the observer at rest who may induce corrections to the metric, unless its mass is crucially small when located near the horizon.
Exploiting Weight Redundancy in CNNs: Beyond Pruning and Quantization

Pruning and quantization are proven methods for improving the performance and storage efficiency of convolutional neural networks (CNNs). Pruning removes near-zero weights in tensors and masks weak connections between neurons in neighbouring layers. Quantization reduces the precision of weights by replacing them with numerically similar values that require less storage. In this paper, we identify another form of redundancy in CNN weight tensors, in the form of repeated patterns of similar values. We observe that pruning and quantization both tend to drastically increase the number of repeated patterns in the weight tensors. We investigate several compression schemes to take advantage of this structure in CNN weight data, including multiple forms of Huffman coding, and other approaches inspired by block sparse matrix formats. We evaluate our approach on several well-known CNNs and find that we can achieve compaction ratios of 1.4x to 3.1x in addition to the saving from pruning and quantization.