Miracle-dz/UniFAR-FTUs huggingface dataset:
Inverse Design of Built Environment by a Fast Fluid Dynamics-based Genetic Algorithm

It is essential to further design built environments with improved thermal comfort level, air quality, and reduced energy consumption of the HVAC system. Recently, researchers integrated computational fluid dynamics (CFD) with optimization algorithms to design the desired built environments. The CFD-based genetic algorithm, which is developed by imitating the evolution theory, is able to identify the global optima. However, the method has high requirements for the computational resources since a single design would take numbers of CFD simulations. Therefore, it is necessary to accelerate the CFD-based genetic algorithm, which would extend its application in the inverse design of built environment. A direct way to accelerate the CFD-based genetic algorithm is to find a substitute for the CFD simulations. The integration of fast fluid dynamics (FFD) and genetic algorithm seems to be able to accelerate the inverse design without losing the accuracy. Therefore, this investigation developed a FFD-based genetic algorithm and implemented the model in OpenFOAM (Open Field Operation and Manipulation), which is an open source CFD program. This study compared the FFD-based genetic algorithm with CFD-based genetic algorithm on accuracy and efficiency in the inverse design of thermal comfort and air quality in an office.
UvA-DARE (Digital Academic Repository) Fluorescence Microscopy Visualization of Contacts Between Objects Fluorescence Microscopy Visualization of Contacts Between Objects**

: The area of contact between two objects was detected by using the strong enhancement of the fluorescence of rigidochromic probe molecules attached to one of the surfaces. Confinement of the molecules suppresses nonradiative decay and turns on the fluorescence. The approach is demonstrated by imaging of the contact area of a plastic sphere in contact with a flat glass surface. Our results agree excellently with the prediction of Hertz(cid:2)s classical theory based on elastic defor-mation. contact mechanics 1882 of On the contact of elastic solids by Hertz. For all mechanical systems, mechanics contact case for this experiment. In summary, the present approach offers a unique method to directly observe the detailed structure of the contact area between two surfaces. We obtain diffraction-limited resolution in the imaging plane, but the resolution in the axial direction is determined by the thickness of the monolayer of dye molecules on the flat glass surface (roughness < 1 nm). We are currently investigating the application of this new method to different problems in contact mechanics.
Di-Peptide-Modified Gemini Surfactants as Gene Delivery Vectors: Exploring the Role of the Alkyl Tail in Their Physicochemical Behavior and Biological Activity

The aim of this work was to elucidate the structure-activity relationship of new peptide-modified gemini surfactant-based carriers. Glycyl-lysine modified gemini surfactants that differ in the length and degree of unsaturation of their alkyl tail were used to engineer DNA nano-assemblies. To probe the optimal nitrogen to phosphate (N/P) ratio in the presence of helper lipid, in vitro gene expression and cell toxicity measurements were carried out. Characterization of the nano-assemblies was accomplished by measuring the particle size and surface charge. Morphological characteristics and lipid organization were studied by small angle X-ray scattering technique. Lipid monolayers were studied using a Langmuir-Blodgett trough. The highest activity of glycyl-lysine modified gemini surfactants was observed with the 16-carbon tail compound at 2.5 N/P ratio, showing a 5- to 10-fold increase in the level of reporter protein compared to the 12 and 18:1 carbon tail compounds. This ratio is significantly lower compared to the previously studied gemini surfactants with alkyl or amino- spacers. In addition, the 16-carbon tail compound exhibited the highest cell viability (85%). This high efficiency is attributed to the lowest critical micelle concentration of the 16-tail gemini surfactant and a balanced packing of the nanoparticles by mixing a saturated and unsaturated lipid together. At the optimal N/P ratio, all nanoparticles exhibited an inverted hexagonal lipid assembly. The results show that the length and nature of the tail of the gemini surfactants play an important role in determining the transgene efficiency of the delivery system. We demonstrated here that the interplay between the headgroup and the nature of tail is specific to each series, thus in the process of rational design, the contribution of the latter should be assessed in the appropriate context.
Innovative Approach in Acoustic Simulation of Timber Walls

The acoustic insulation performance of a glulam system (timber-framed wall) is of paramount importance in sustainable lightweight constructions. Nevertheless, traditional models like impedance coupling or double-leaf theory cannot offer robust results comparable to measurements. Even Finite Element (FE) simulations may not provide good results, due to high variables computations models and the resultant error propagation. For these reasons, in this paper the Transfer Matrix Modelling (TMM) method has been adopted tor the acoustic analysis of glulam timber structures in order to understand its advantages and limitations. The sound insulation of glulam walls will be simulated using a decoupled approach where beams will be considered as linear sound bridges.
On the number of the discrete spectrum of two-particle discrete Schröodinger operators

We consider a family of discrete Schrödinger operators hd(k), where k is the two-particle quasi-momentum varying in Td = (−π, π]d, associated to a system of two-particles on the d dimensional lattice Zd, d ≥ 1. The CwikelLieb-Rozenblum (CLR)-type estimates are written for hd(k) when the Fermi surface E−1 k (em(k)) of the associated dispersion relation is a one point set at em(k), the bottom of the essential spectrum. Moreover, when the Fermi surface E−1 k (em(k)) is of dimension d−1 or d−2, we obtain the necessary and su cient conditions for the existence of in nite discrete spectrum of hd(k), while in the case dimE−1 k (em(k)) ≤ d− 3, the discrete spectrum of h d(k) is nite.
Updating Quantitative Synthesis

We present an overview of analyses undertaken to evaluate the robustness (or fragility) of systematic review and meta-analysis findings to changes in assumptions or methodological decision about the data and methods used for evidence synthesis. We discuss stability analyses, which evaluate the robustness of conclusions to different methodological choices. Stability analyses pertain to the selection of data to be synthesized (e.g., leave-one-out analyses); the specification of models for evidence synthesis (e.g., the use of a discrete within-study likelihood or a normal approximation); and the estimators and techniques for estimating model parameters (e.g., the use different estimators for quantifying between-study heterogeneity under the same statistical model). We also discuss sensitivity analyses, which evaluate the impact of different assumptions about the parameters of a particular statistical model on conclusions. In the context of systematic reviews, sensitivity analyses are used for handling missing study-level data, the selective dissemination of study results, or the estimation of unidentifiable or weakly identifiable model parameters. We identify challenges in the interpretation of stability and sensitivity analyses in evidence synthesis and offer recommendations for their use in applied work.
ASSESSING GOVERNMENT POLICY FOR TARGETED REFORMS: THE CASE OF AFRICAN AGRICULTURE BY MANITRA

Assessing how the policies of taxing agricultural export to boost government revenues while subsidizing food production to improve food security have affected the African agriculture is a requisite for investment and fiscal policy reforms. I estimate the substitution and endowment effects of the mix of government taxes and subsidies on the mix of agricultural commodities in selected sub-Saharan African countries. The commodities are split into cash commodity, importable food, and non-tradable food categories. Result show that changes in the ratio of taxes on any two commodity categories lead to commodity substitution but the cross-effects are counterintuitive and distributed unevenly. A higher tax increase on a cash commodity relative to tax on importable food causes relatively more harms to the production of importable food than to the production of the cash commodity. The policy effects through substitution among commodities are relatively small; the bulk of the effects, about 60-to 70% of the total effects on output levels, are due to the endowment effects. Sub-Saharan Africa ‘s quest to transform its agriculture sector must thus consider that the relative level of assistance matters most. More important, the priority before assisting any targeted subsector would be to increase the amount of investment resources for the whole sector; reshuffling resources without increasing them may provoke unintended consequences.
Methanoplanus limicola DSM 2279T

Markus Göker, Megan Lu, Anne Fiebig, Matt Nolan, Alla Lapidus, Hope Tice, Tijana Glavina Del Rio, Jan-Fang Cheng, Cliff Han, Roxanne Tapia, Lynne A. Goodwin, Sam Pitluck, Konstantinos Liolios, Konstantinos Mavromatis, Ioanna Pagani, Natalia Ivanova, Natalia Mikhailova, Amrita Pati, Amy Chen, Krishna Palaniappan, Miriam Land, Shanmugam Mayilraj, Manfred Rohde, John C. Detter, Boyke Bunk, Stefan Spring, Reinhard Wirth, Tanja Woyke, James Bristow, Jonathan A. Eisen, Victor Markowitz, Philip Hugenholtz, Nikos C. Kyrpides, Hans-Peter Klenk
Vascular Depression: Re-evaluated on the Base of Brain Metabolism in a Sample of Patients With “Organic Brain Syndromes”

In the DSM 5 associated features supporting diagnoses of major or mild vascular Neurocognitive Disorders sign “personality and mood changes, abulia, depression, and emotional lability” as common symptoms in this category. In the ICD 10 “organic affective, anxious, dissociative, emotionally labile (asthenic), and mild cognitive disorders (F06.3-06.7)” were not corresponding with DSM-IV-TR. Do clinicians recognize a line between organic and functional genesis debating about the diagnostic significance of White Matter Hyperintensities in MRI/CT as only structural imaging? The WMHs as signals of compromised ependyms in relatively intact small vessels point out varying contributions of blood-brain-barrier and brain-CSF interface disturbances. Both anatomical borders are transporting, i.e., glucose and lactate from blood into the brain, and exporting via glymphatic-lymphatic fluid transport. Are glucose and lactate in brain the best biomarkers? By cluster, principal, and discriminant function analysis, patients with organic brain syndromes were grouped and correlated with results of cerebral rates of oxygen, glucose, and lactate. The sample had to be divided clinically in degenerative, vascular, alcohol, toxic, and other etiologies. Excluded was “brain damage” (gross focal lesions after stroke, trauma, inflammation etc.), obviously treatable metabolic or infectious conditions, seizure related disorders, full-blown delirium tremens, and dementia as advanced stages whether degenerative or vascular genesis. Distinct personality and behavioral changes (N = 52), and dementia (N = 45), against depressive (N = 42) and delusional patients (N = 8) (p < 0.0000) correlated with increased per cent changes compared with normal of CMR O2, lactate and the lactate-glucose-index in the former and decreased in the latter (p(u) < 0.001). The vascular etiologic impact of depressives (n = 23/42) is in the second syndrome as vascular depression without cognitive impairment as believed before (11/42) in the same group. The patients should not identified as “endogenous–neurotic” as the treatment is much different. our study suggests that brain lactate is an excellent biomarker of the sleep-wake cycle and increases further during sleep-deprivation, because brain lactate is inversely correlated with glymphatic-lymphatic clearance. This analysis provides fundamental new insight into brain energy metabolism by demonstrating that glucose that is not fully oxidized can be exported as lactate via glymphatic-lymphatic fluid transport (Abstract page 1).
JCB_200810002 1275..1284

The Rockefeller University Press $30.00 J. Cell Biol. Vol. 185 No. 7 1275–1284 www.jcb. org/cgi/doi/10.1083/jcb.200810002 JCB 1275 Correspondence to Cheng Zhu: cheng.zhu@bme.gatech. edu Abbreviations used in this paper: AFM, atomic force microscopy; FN, fibronectin; GPIb, glycoprotein Ib; m51, membrane 51; PZT, piezoelectric translator; tr51, truncated 51; VWF, von Willebrand factor. Introduction
Indexing Severity of Diabetic Foot InfectionWith

OBJECTIVEdManagement of diabetic foot infection (DFI) has been hampered by limited means of accurately classifying disease severity. New hybrid nuclear/computed tomography (CT) imaging techniques elucidate a combination of wound infection parameters not previously evaluated as outcome prognosticators. Our aim is to determine if a novel standardized hybrid image– based scoring system, Composite Severity Index (CSI), has prognostic value in DFI.
JCB_201403080 1..10

The Rockefeller University Press $30.00 J. Cell Biol. Vol. 207 No. 3 365–374 www.jcb. org/cgi/doi/10.1083/jcb.201403080 JCB 365 *J.M. Olsen and M. Sato contributed equally to this paper. Correspondence to Tore Bengtsson: tore.bengtsson@su.se Abbreviations used in this paper: [3H]2DG, 2-deoxy-d-[1-3H]-glucose; 8-Br-cAMP, 8-bromoadenosine-cAMP; BAT, brown adipose tissue; GLUT4, glucose transporter 4; hMADS, human multipotent adipose-derived stem; mTOR, mechanistic target of rapamycin; mTORC1, mTOR complex 1; mTORC2, mTOR complex 2. Introduction
Immunotherapy of hepatocellular carcinoma

Current therapies for advanced hepatocellular carcinoma (HCC) are marginally effective and exacerbate underlying liver disease. The ability of immunotherapy to elicit nontoxic, systemic, long-lived anti-tumor activity makes it particularly well-suited for use in the setting of HCC. While therapeutic benefit has been achieved in early clinical trials, the efficacy of immune-based therapies is limited by several unique properties of HCC, most notably the inherently tolerogenic character of the liver in both healthy and diseased (chronically-infected or tumor-bearing) states. Therapeutic regimens that both counteract these immunosuppressive mechanisms and amplify tumor-specific immunity are expected to profoundly improve clinical outcomes for HCC patients.
Cavity collapse near slot geometries

Abstract The collapse of a gas or vapour bubble near a solid boundary produces a jet directed towards the boundary. High surface pressure and shear stress induced by this jet can damage, or clean, the surface. More complex geometries will result in changes in collapse behaviour, in particular the direction of the jet. The majority of prior research has focused on simple flat boundaries or cases with limited complexity. There is currently very little known about how complex geometries affect bubble collapse. We numerically and experimentally investigate how a slot in a flat boundary affects the jet direction for a single bubble. We use a boundary element model to predict how the jet direction depends on key geometric parameters and show that the results collapse to a single curve when the parameters are normalised appropriately. We then experimentally validate the predictions using laser-induced cavitation and compare the experimental results to the predicted dependencies. This research reveals a tendency for the jet to be directed away from a slot and shows that the jet direction is independent of slot height for slots of sufficient height.
Towards rigorous validation of energy optimisation experiments

The optimisation of software energy consumption is of growing importance across all scales of modern computing, i.e., from embedded systems to data-centres. Practitioners in the field of Search-Based Software Engineering and Genetic Improvement of Software acknowledge that optimising software energy consumption is difficult due to noisy and expensive fitness evaluations. However, it is apparent from results to date that more progress needs to be made in rigorously validating optimisation results. This problem is pressing because modern computing platforms have highly complex and variable behaviour with respect to energy consumption. To compare solutions fairly we propose in this paper a new validation approach called R3-validation which exercises software variants in a rotated-round-robin order. Using a case study, we present an in-depth analysis of the impacts of changing system states on software energy usage, and we show how R3-validation mitigates these. We compare it with current validation approaches across multiple devices and operating systems, and we show that it aligns best with actual platform behaviour.
TensorProjection Layer: A Tensor-Based Dimensionality Reduction Method in CNN

In this paper, we propose a dimensionality reduction method applied to tensor-structured data as a hidden layer (we call it TensorProjection Layer) in a convolutional neural network. Our proposed method transforms input tensors into ones with a smaller dimension by projection. The directions of projection are viewed as training parameters associated with our proposed layer and trained via a supervised learning criterion such as minimization of the cross-entropy loss function. We discuss the gradients of the loss function with respect to the parameters associated with our proposed layer. We also implement simple numerical experiments to evaluate the performance of the TensorProjection Layer.
Large Arabic Twitter Dataset on COVID-19

The 2019 coronavirus disease (COVID-19), emerged late December 2019 in China, is now rapidly spreading across the globe. At the time of writing this paper, the number of global confirmed cases has passed two millions and half with over 180,000 fatalities. Many countries have enforced strict social distancing policies to contain the spread of the virus. This have changed the daily life of tens of millions of people, and urged people to turn their discussions online, e.g., via online social media sites like Twitter. In this work, we describe the first Arabic tweets dataset on COVID-19 that we have been collecting since January 1st, 2020. The dataset would help researchers and policy makers in studying different societal issues related to the pandemic. Many other tasks related to behavioral change, information sharing, misinformation and rumors spreading can also be analyzed.
PANDORA Talks: Personality and Demographics on Reddit

Personality and demographics are important variables in social sciences and computational sociolinguistics. However, datasets with both personality and demographic labels are scarce. To address this, we present PANDORA, the first dataset of Reddit comments of 10k users partially labeled with three personality models and demographics (age, gender, and location), including 1.6k users labeled with the well-established Big 5 personality model. We showcase the usefulness of this dataset on three experiments, where we leverage the more readily available data from other personality models to predict the Big 5 traits, analyze gender classification biases arising from psycho-demographic variables, and carry out a confirmatory and exploratory analysis based on psychological theories. Finally, we present benchmark prediction models for all personality and demographic variables.
Varying without varying: reparameterizations, diffeomorphisms, general covariance, Lie derivatives, and all that

The standard way of deriving Euler–Lagrange (EL) equations given a point particle action is to vary the trajectory and set the first variation of the action to zero. However, if the action is (i) reparameterization invariant, and (ii) generally covariant, I show that one may derive the EL equations by suitably nullifying the variation through a judicious coordinate transformation. The net result of this is that the curve remains fixed, while all other geometrical objects in the action undergo a change, given precisely by the Lie derivatives along the variation vector field. This, then, is the most direct and transparent way to elucidate the connection between general covariance, diffeomorphism invariance, and Lie derivatives, without referring to covariant derivative. I highlight the geometric underpinnings and generality of above ideas by applying them to simplest of field theories, keeping the discussion at a level easily accessible to advanced undergraduates. As non-trivial applications of these ideas, I (i) derive the geodesic deviation equation using first order diffeomorphisms, and (ii) demonstrate how they can highlight the connection between canonical and metric stress–energy tensors in field theories.
Recognizing Spatial Configurations of Objects with Graph Neural Networks

Deep learning algorithms can be seen as compositions of functions acting on learned representations encoded as tensor-structured data. However, in most applications those representations are monolithic, with for instance one single vector encoding an entire image or sentence. In this paper, we build upon the recent successes of Graph Neural Networks (GNNs) to explore the use of graph-structured representations for learning spatial configurations. Motivated by the ability of humans to distinguish arrangements of shapes, we introduce two novel geometrical reasoning tasks, for which we provide the datasets. We introduce novel GNN layers and architectures to solve the tasks and show that graph-structured representations are necessary for good performance.
A single image deep learning approach to restoration of corrupted remote sensing products

Remote sensing images are used for a variety of analyses, from agricultural monitoring, to disaster relief, to resource planning, among others. The images can be corrupted due to a number of reasons, including instrument errors and natural obstacles such as clouds. We present here a novel approach for reconstruction of missing information in such cases using only the corrupted image as the input. The Deep Image Prior methodology eliminates the need for a pre-trained network or an image database. It is shown that the approach easily beats the performance of traditional single-image methods.
Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection

Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training. However, major challenges remain: (1) differentiation of object instances can be ambiguous; (2) detectors tend to focus on discriminative parts rather than entire objects; (3) without ground truth, object proposals have to be redundant for high recalls, causing significant memory consumption. Addressing these challenges is difficult, as it often requires to eliminate uncertainties and trivial solutions. To target these issues we develop an instance-aware and context-focused unified framework. It employs an instance-aware self-training algorithm and a learnable Concrete DropBlock while devising a memory-efficient sequential batch back-propagation. Our proposed method achieves state-of-the-art results on COCO (12.1% AP, 24.8% AP50), VOC 2007 (54.9% AP), and VOC 2012 (52.1% AP), improving baselines by great margins. In addition, the proposed method is the first to benchmark ResNet based models and weakly supervised video object detection. Refer to our project page for code, models, and more details: https://github.com/NVlabs/wetectron.