Miracle-dz/UniFAR-FTUs huggingface dataset:
Task Admission Control and Boundary Analysis of Cognitive Cloud Data Centers

A novel cloud data center (DC) model is studied here with cognitive capabilities for real-time (or online) flow compared to the batch tasks. Here, a DC can determine the cost of using resources and an online user or the user with batch tasks may decide whether or not to pay for getting the services. The online service tasks have a higher priority in getting the service over batch tasks. Both types of tasks need a certain number of virtual machines (VM). By targeting on the maximization of total discounted reward, an optimal policy for admitting task tasks is finally verified to be a state-related control limit policy. Next, a lower and an upper bound for such an optimal policy are derived, respectively, for the estimation and utilization in reality. Finally, a comprehensive set of experiments on the various cases to validate this proposed model and the solution is conducted. As a demonstration, the machine learning method is adopted to show how to obtain the optimal values by using a feed-forward neural network model. The results achieved in this paper will be expectedly utilized in various cloud data centers with cognitive characteristics in an economically optimal strategy.
Kernel regression in high dimension: Refined analysis beyond double descent

In this paper, we provide a precise characterize of generalization properties of high dimensional kernel ridge regression across the under- and over-parameterized regimes, depending on whether the number of training data $n$ exceeds the feature dimension $d$. By establishing a novel bias-variance decomposition of the expected excess risk, we show that, while the bias is independent of $d$ and monotonically decreases with $n$, the variance depends on $n,d$ and can be unimodal or monotonically decreasing under different regularization schemes. Our refined analysis goes beyond the double descent theory by showing that, depending on the data eigen-profile and the level of regularization, the kernel regression risk curve can be a double-descent-like, bell-shaped, or monotonic function of $n$. Experiments on synthetic and real data are conducted to support our theoretical findings.
Developmental and Reproductive Performance of the Gypsy Moth, Lymantria dispar (L.) (Lepidoptera: Lymantriidae), on Selected Hosts Common to Mid-Atlantic and Southern Forests

Larvae of the gypsy moth, Lymantria dispar, were reared on selected deciduous and coniferous species. Survival, larval and pupal developmental period, pupal weight, and adult fecundity of individuals reared on red oak, white oak, willow oak, sweet gum, red maple, synthetic diet, Virginia pine, loblolly pine, and white pine were recorded. All larvae reared on the conifer species perished, whereas mortality on all other species except red maple was low. Sweet gum-reared larvae had the shortest developmental period, highest pupal weights, and largest mean fecundity.
COVIDomaly: A Deep Convolutional Autoencoder Approach for Detecting Early Cases of COVID-19

As of September 2020, the COVID-19 pandemic continues to devastate the health and well-being of the global population. With more than 33 million confirmed cases and over a million deaths, global health organizations are still a long way from fully containing the pandemic. This pandemic has raised serious questions about the emergency preparedness of health agencies, not only in terms of treatment of an unseen disease, but also in identifying its early symptoms. In the particular case of COVID-19, several studies have indicated that chest radiography images of the infected patients show characteristic abnormalities. However, at the onset of a given pandemic, such as COVID-19, there may not be sufficient data for the affected cases to train models for their robust detection. Hence, supervised classification is ill-posed for this problem because the time spent in collecting large amounts of infected peoples' data could lead to the loss of human lives and delays in preventive interventions. Therefore, we formulate this problem within a one-class classification framework, in which the data for healthy patients is abundantly available, whereas no training data is present for the class of interest (COVID-19 in our case). To solve this problem, we present COVIDomaly, a convolutional autoencoder framework to detect unseen COVID-19 cases from the chest radiographs. We tested two settings on a publicly available dataset (COVIDx) by training the model on chest X-rays from (i) only healthy adults, and (ii) healthy and other non-COVID-19 pneumonia, and detected COVID-19 as an anomaly. After performing 3-fold cross validation, we obtain a pooled ROC-AUC of 0.7652 and 0.6902 in the two settings respectively. These results are very encouraging and pave the way towards research for ensuring emergency preparedness in future pandemics, especially the ones that could be detected from chest X-rays.
Disentangling the effects of sex, life history and genetic background in Atlantic salmon: growth, heart and liver under common garden conditions

Livestock domestication has long been a part of agriculture, estimated to have first occurred approximately 10 000 years ago. Despite the plethora of traits studied, there is little understanding of the possible impacts domestication has had on internal organs, which are key determinants of survival. Moreover, the genetic basis of observed associated changes in artificial environments is still puzzling. Here we examine impacts of captivity on two organs in Atlantic salmon (Salar salar) that have been domesticated for approximately 50 years: heart and liver, in addition to growth. We studied multiple families of wild, domesticated, F1 and F2 hybrid, and backcrossed strains of S. salar in replicated common garden tanks during the freshwater and marine stages of development. Heart and liver weight were investigated, along with heart morphology metrics examined in just the wild, domesticated and F1 hybrid strains (heart height and width). Growth was positively linked with the proportion of the domesticated strain, and recombination in F2 hybrids (and the potential disruption of co-adapted gene complexes) did not influence growth. Despite the influence of domestication on growth, we found no evidence for domestication-driven divergence in heart or liver morphology. However, sexual dimorphism was detected in heart morphology, and after controlling for body size, females exhibited significantly larger heart weight and heart width when compared with males. Wild females also had an increased heart height when compared with wild males, and this was not observed in any other strain. Females sampled in saltwater showed significantly larger heart height with rounder hearts, than saltwater males. Collectively, these results demonstrate an additive basis of growth and, despite a strong influence of domestication on growth, no clear evidence of changes in heart or liver morphology associated with domestication was identified.
Universal norms and the Fargues-Fontaine curve.

We study the module of universal norms associated with a de Rham $p$-adic Galois representation in a perfectoid field extension. In particular, we compute precisely this module when the Hodge-Tate weights of a representation are greater than or equal to $0$. This generalises a result by Coates and Greenberg for Abelian varieties, and partially answers a question of theirs. Our method relies on the classification of vector bundles over the Fargues-Fontaine curve.
Predisposition of Neonatal Maternal Separation to Visceral Hypersensitivity via Downregulation of Small-Conductance Calcium-Activated Potassium Channel Subtype 2 (SK2) in Mice

Background Visceral hypersensitivity is a common occurrence of gastrointestinal diseases such as irritable bowel syndrome (IBS), wherein early-life stress (ELS) may have a high predisposition to the development of visceral hypersensitivity in adulthood, with the specific underlying mechanism still elusive. Herein, we assessed the potential effect of small-conductance calcium-activated potassium channel subtype 2 (SK2) in the spinal dorsal horn (DH) on the pathogenesis of visceral hypersensitivity induced by maternal separation (MS) in mice. Methods Neonatal mice were subjected to the MS paradigm, an established ELS model. In adulthood, the visceral pain threshold and the abdominal withdrawal reflex (AWR) were measured with an inflatable balloon. The elevated plus maze, open field test, sucrose preference test, and forced swim test were employed to evaluate the anxiety- and depression-like behaviors. The expression levels of SK2 in the spinal DH were determined by immunofluorescence and western blotting. The mRNA of SK2 and membrane palmitoylated protein 2 (MPP2) were determined by quantitative real-time polymerase chain reaction (qRT-PCR). Electrophysiology was applied to evaluate the neuronal firing rates and SK2 channel-mediated afterhyperpolarization current (IAHP). The interaction between MPP2 and SK2 was validated by coimmunoprecipitation. Results In contrast to the naïve mice, ethological findings in MS mice revealed lowered visceral pain threshold, more evident anxiety- and depression-like behaviors, and downregulated expression of membrane SK2 protein and MPP2 protein. Moreover, electrophysiological results indicated increased neuronal firing rates and decreased IAHP in the spinal DH neurons. Nonetheless, intrathecal injection of the SK2 channel activator 1-ethyl-2-benzimidazolinone (1-EBIO) in MS mice could reverse the electrophysiological alterations and elevate the visceral pain threshold. In the naïve mice, administration of the SK2 channel blocker apamin abated IAHP and elevated spontaneous neuronal firing rates in the spinal DH neurons, reducing the visceral pain threshold. Finally, disruption of the MPP2 expression by small interfering RNA (siRNA) could amplify visceral hypersensitivity in naïve mice. Conclusions ELS-induced visceral pain and visceral hypersensitivity are associated with the underfunction of SK2 channels in the spinal DH.
Testing a Data-driven Active Region Evolution Model with Boundary Data at Different Heights from a Solar Magnetic Flux Emergence Simulation

A data-driven active region evolution (DARE) model has been developed to study the complex structures and dynamics of solar coronal magnetic fields. The model is configured with a typical coronal environment of tenuous gas governed by strong magnetic field, and thus its lower boundary is set at the base of the corona, but driven by magnetic fields observed in the photosphere. A previous assessment of the model using data from a flux emergence simulation (FES) showed that the DARE failed to reproduce the coronal magnetic field in the FES, which is attributed to the fact that the photospheric data in the FES has a very strong Lorentz force and therefore spurious flows are generated in the DARE model. Here we further test the DARE by using three sets of data from the FES sliced at incremental heights, which correspond to the photosphere, the chromosphere, and the base of the corona. It is found that the key difference in the three sets of data is the extent of the Lorentz force, which makes the data-driven model perform very differently. At the two higher levels above the photosphere, the Lorentz force decreases substantially, and the DARE model attains results in much better agreement with the FES, confirming that the Lorentz force in the boundary data is a key issue affecting the results of the DARE model. However, unlike the FES data, the photospheric field from SDO/HMI observations has recently been found to be very close to force-free. Therefore, we suggest that it is still reasonable to use the photospheric magnetic field as an approximation of the field at the coronal base to drive the DARE model.
Determining the Number of Factors in High-Dimensional Generalized Latent Factor Models

As a generalization of the classical linear factor model, generalized latent factor models are useful for analyzing multivariate data of different types, including binary choices and counts. This paper proposes an information criterion to determine the number of factors in generalized latent factor models. The consistency of the proposed information criterion is established under a high-dimensional setting where both the sample size and the number of manifest variables grow to infinity, and data may have many missing values. An error bound is established for the parameter estimates, which plays an important role in establishing the consistency of the proposed information criterion. This error bound improves several existing results and may be of independent theoretical interest. We evaluate the proposed method by a simulation study and an application to Eysenck’s personality questionnaire.
Support-set bottlenecks for video-text representation learning

The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples that are known to be related, such as text and video from the same sample, and pushes away the representations of all other pairs. We posit that this last behaviour is too strict, enforcing dissimilar representations even for samples that are semantically-related -- for example, visually similar videos or ones that share the same depicted action. In this paper, we propose a novel method that alleviates this by leveraging a generative model to naturally push these related samples together: each sample's caption must be reconstructed as a weighted combination of other support samples' visual representations. This simple idea ensures that representations are not overly-specialized to individual samples, are reusable across the dataset, and results in representations that explicitly encode semantics shared between samples, unlike noise contrastive learning. Our proposed method outperforms others by a large margin on MSR-VTT, VATEX and ActivityNet, for video-to-text and text-to-video retrieval.
Studies on variability in physico-chemical characters of different jamun (Syzygium cumini) accessions from Goa

Jamun is found scattered throughout Goa. A survey was carried out in the North Goa district of the state to identify the elite genotypes among its population. The ripe fruits from 18 selected trees were analyzed for physico-chemical traits like fruit weight, length, width, pulp and seed content, pulp: seed ratio, TSS (°Brix), titrable acidity and carbohydrates in terms of total sugars and reducing sugars. The study revealed that there was a wide variation among its accessiocs. Individual fruit weight ranged from 3.42 to 13.67 g; length 3.31 to 5.26 cm; girth 5.21 to 9.82 cm; length: width ratio 1.44 to 2.3 and pulp percentage 58.57 to 84.55. There was a wide variation in chemical characters also. Total soluble solids (°Brix) varied from 12.0 to 26.8; titrable acidity 0.59 to 1.63%; total sugars 6.87 to 25.31% and sugar: acid ratio from 15.39 to 27.92. In general, OGI was found to be promising among all accessions studied, based on its desirable physico-chemical characters.
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction

Self-supervised monocular depth estimation has emerged as a promising method because it does not require groundtruth depth maps during training. As an alternative for the groundtruth depth map, the photometric loss enables to provide self-supervision on depth prediction by matching the input image frames. However, the photometric loss causes various problems, resulting in less accurate depth values compared with supervised approaches. In this paper, we propose SAFENet that is designed to leverage semantic information to overcome the limitations of the photometric loss. Our key idea is to exploit semantic-aware depth features that integrate the semantic and geometric knowledge. Therefore, we introduce multi-task learning schemes to incorporate semantic-awareness into the representation of depth features. Experiments on KITTI dataset demonstrate that our methods compete or even outperform the state-of-the-art methods. Furthermore, extensive experiments on different datasets show its better generalization ability and robustness to various conditions, such as low-light or adverse weather.
Treatment of Pneumococcal Infection by Using Engineered Human C-Reactive Protein in a Mouse Model

C-reactive protein (CRP) binds to several species of bacterial pathogens including Streptococcus pneumoniae. Experiments in mice have revealed that one of the functions of CRP is to protect against pneumococcal infection by binding to pneumococci and activating the complement system. For protection, however, CRP must be injected into mice within a few hours of administering pneumococci, that is, CRP is protective against early-stage infection but not against late-stage infection. It is assumed that CRP cannot protect if pneumococci got time to recruit complement inhibitor factor H on their surface to become complement attack-resistant. Since the conformation of CRP is altered under inflammatory conditions and altered CRP binds to immobilized factor H also, we hypothesized that in order to protect against late-stage infection, CRP needed to change its structure and that was not happening in mice. Accordingly, we engineered CRP molecules (E-CRP) which bind to factor H on pneumococci but do not bind to factor H on any host cell in the blood. We found that E-CRP, in cooperation with wild-type CRP, was protective regardless of the timing of administering E-CRP into mice. We conclude that CRP acts via two different conformations to execute its anti-pneumococcal function and a model for the mechanism of action of CRP is proposed. These results suggest that pre-modified CRP, such as E-CRP, is therapeutically beneficial to decrease bacteremia in pneumococcal infection. Our findings may also have implications for infections with antibiotic-resistant pneumococcal strains and for infections with other bacterial species that use host proteins to evade complement-mediated killing.
Numerical Simulation of Solid Combustion in Microporous Particles

In the present study, direct numerical simulations for smoldering in simplified geometries are performed for multicomponent and dilatable flows. The reactant gas is passing inside an array of permeable and microporous cylinders. The chemical reaction takes place within the grains. For the sake of simplicity, the smoldering is treated as a single step chemical reaction. We define a Darcy-Brinkman model to deal with the multi-scale nature of this problem. Varying the flow rate, and carbon content, we can investigate the response of our model, and try to compare with existing solutions or available experimental investigations. Thus, the ability of the Darcy Brinkman approach to capture such situations of porous particles combustion will be checked. The numerical model sensitivity to the inner grain permeability is also investigated.
Sleep Duration and Stroke: A Mendelian Randomization Study

Study Objectives: To clarify the effects of sleep duration on stroke and stroke subtypes, we adopted a Mendelian randomization (MR) approach to evaluate their causal relationship. Methods: A genome-wide association study including 446,118 participants from UK biobank was used to identify instruments for short sleep, long sleep and sleep duration. Summary-level data for all stroke, ischemic stroke, intracerebral hemorrhage, and their subtypes were obtained from meta-analyses conducted by the MEGASTROKE consortium. MR analyses were performed using the inverse-variance-weighted method, weighted median estimator, MR pleiotropy residual sum and outlier (MR-PRESSO) test, and MR-Egger regression. Sensitivity analyses were further performed using leave-one-out analysis, MR-PRESSO global test and Cochran's Q test to verify the robustness of our findings. Results: By two-sample MR, we didn't find causal associations between sleep duration and risk of stroke. However, in the subgroup analysis, we found weak evidence for short sleep in increasing risk of cardio-embolic stroke (odds ratio [OR], 1.33; 95% confidence interval [CI], 1.11–1.60; P = 0.02) and long sleep in increasing risk of large artery stroke [OR, 1.41; 95% CI, 1.02–1.95; P = 0.04]. But the associations were not significant after Bonferroni correction for multiple comparisons. Conclusions: Our study suggests that sleep duration is not causally associated with risk of stroke and its subtypes.
Highly Conductive P-Type MAPbI3 Films and Crystals via Sodium Doping

To regulate the optical and electrical properties of the crystals and films of the intrinsic methylammonium lead iodide (CH3NH3PbI3), we dope them with sodium (Na) by selecting sodium iodide (NaI) as a dopant source. The highly conductive p-type sodium-doped CH3NH3PbI3 (MAPbI3: Na) perovskite single crystals and thin films are successfully grown using the inverse temperature crystallization (ITC) method and antisolvent spin-coating (ASC) method, respectively. With the increase of Na+ doping concentration, the grain size of the film increases, the surface becomes smoother, and the crystallinity improves. Hall effect results demonstrate that both the MAPbI3: Na thin films and single crystals change their quasi-insulating intrinsic conductivity to a highly conductive p-type conductivity. The room-temperature photoluminescence (PL) peaks of doped MAPbI3 films slightly blue shift, while the photocarriers' lifetime becomes longer. The optical fingerprints of the doped levels in MAPbI3: Na perovskites can be identified by temperature-dependent PL. Obvious fingerprints of Na-related acceptor (A0X) levels in the doped MAPbI3: Na were observed at 10 K. These results suggest that sodium doping is an effective way to grow highly conductive p-type MAPbI3 perovskites.
The Challenges of Microbial Control of Mosquito-Borne Diseases Due to the Gut Microbiome

Mosquitoes are one of the deadliest animals on earth because of their ability to transmit a wide range of human pathogens. Traditional mosquito control methods use chemical insecticides, but with dwindling long-term effectiveness and negative effects on the environment, microbial forms of control have become common alternatives. The insecticide Bacillus thuringiensis subspecies israelensis (Bti) is the most popular of these alternatives, although it can also have direct effects on lowering environmental biodiversity and indirect effects on food-web relationships in the ecosystems where it is deployed. In addition, microbial control agents that impede pathogen development or transmission from mosquito to human are under investigation, including Wolbachia and Asaia, but unexpected interactions with mosquito gut bacteria can hinder their effectiveness. Improved characterization of mosquito gut bacterial communities is needed to determine the taxa that interfere with microbial controls and their effectiveness in wild populations. This mini-review briefly discusses relationships between mosquito gut bacteria and microbial forms of control, and the challenges in ensuring their success.