Miracle-dz/UniFAR-FTUs huggingface dataset:
Reference-Conditioned Super-Resolution by Neural Texture Transfer

With the recent advancement in deep learning, we have witnessed a great progress in single image super-resolution. However, due to the significant information loss of the image downscaling process, it has become extremely challenging to further advance the state-of-the-art, especially for large upscaling factors. This paper explores a new research direction in super resolution, called reference-conditioned super-resolution, in which a reference image containing desired high-resolution texture details is provided besides the low-resolution image. We focus on transferring the high-resolution texture from reference images to the super-resolution process without the constraint of content similarity between reference and target images, which is a key difference from previous example-based methods. Inspired by recent work on image stylization, we address the problem via neural texture transfer. We design an end-to-end trainable deep model which generates detail enriched results by adaptively fusing the content from the low-resolution image with the texture patterns from the reference image. We create a benchmark dataset for the general research of reference-based super-resolution, which contains reference images paired with low-resolution inputs with varying degrees of similarity. Both objective and subjective evaluations demonstrate the great potential of using reference images as well as the superiority of our results over other state-of-the-art methods.
A Novel Polyaminocarboxylate Compound To Treat Murine Pulmonary Aspergillosis by Interfering with Zinc Metabolism

ABSTRACT Aspergillus fumigatus can cause pulmonary aspergillosis in immunocompromised patients and is associated with a high mortality rate due to a lack of reliable treatment options. This opportunistic pathogen requires zinc in order to grow and cause disease. Novel compounds that interfere with fungal zinc metabolism may therefore be of therapeutic interest. We screened chemical libraries containing 59,223 small molecules using a resazurin assay that compared their effects on an A. fumigatus wild-type strain grown under zinc-limiting conditions and on a zinc transporter knockout strain grown under zinc-replete conditions to identify compounds affecting zinc metabolism. After a first screen, 116 molecules were selected whose inhibitory effects on fungal growth were further tested by using luminescence assays and hyphal length measurements to confirm their activity, as well as by toxicity assays on HeLa cells and mice. Six compounds were selected following a rescreening, of which two were pyrazolones, two were porphyrins, and two were polyaminocarboxylates. All three groups showed good in vitro activity, but only one of the polyaminocarboxylates was able to significantly improve the survival of immunosuppressed mice suffering from pulmonary aspergillosis. This two-tier screening approach led us to the identification of a novel small molecule with in vivo fungicidal effects and low murine toxicity that may lead to the development of new treatment options for fungal infections by administration of this compound either as a monotherapy or as part of a combination therapy.
Dual Relay Selection for Cooperative NOMA With Distributed Space Time Coding

We consider a two-user multi-relay cooperative non-orthogonal multiple access (NOMA) network with distributed space–time coding. Two dual-relay selection strategies are proposed for cooperative NOMA, namely, two-stage dual relay selection with fixed power allocation (DRS-FPA) and two-stage dual relay selection with dynamic power allocation (DRS-DPA). Furthermore, lower and upper bounds on the outage probability for the DRS-FPA scheme and the exact outage probability for the DRS-DPA scheme are obtained in closed-form, respectively. Numerical results show that the proposed two-stage DRS schemes not only yield better outage performance than the existing single relay selection schemes without sacrificing spectral efficiency, but can also achieve full diversity gain.
PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences

We introduce the problem of PAC rank elicitation, which consists of sorting a given set of options based on adaptive sampling of stochastic pairwise preferences. More specifically, we assume the existence of a ranking procedure, such as Copeland's method, that determines an underlying target order of the options. The goal is to predict a ranking that is sufficiently close to this target order with high probability, where closeness is measured in terms of a suitable distance measure. We instantiate this setting with combinations of two different distance measures and ranking procedures. For these instantiations, we devise efficient strategies for sampling pairwise preferences and analyze the corresponding sample complexity. We also present first experiments to illustrate the practical performance of our methods.
Atomic emission spectroscopy of electrically triggered exploding nanoparticle analytes on graphene/SiO2/Si substrate

The authors report a device technology that promises chip-scale atomic emission spectroscopy operating in air ambient at room temperature with low voltage pulses. Analytes are placed on top of a graphene/SiO2/Si substrate and are atomized for atomic luminescence under electrical excitation. Here, the graphene is designed to serve as an electron-transparent conducting electrode. Under proper voltage pulses applied, the thin insulating layer (10-nm SiO2) breaks down and forms nanoscale leakage channels. Electrons induced at the SiO2/Si interface then emit into the void channels and ballistically travel into the anode (graphene) side. Kinetic electrons are injected into analytes inducing explosions of analyte material. This explosive fragmentation produces atoms in various excited states. The excited atoms then relax producing characteristic luminescence.
Moral Judgment in Old Age: Evidence for an Intent-to-Outcome Shift

Younger (21–39 years) and older (63–90 years) adults were presented with scenarios illustrating either harmful or helpful actions. Each scenario provided information about the agent’s intention, either neutral or valenced (harmful/helpful), and the outcome of his or her action, either neutral or valenced. Participants were asked to rate how morally good or bad the agent’s action was. In judging harmful actions, older participants relied less on intentions and more on outcomes compared to younger participants. This age-related difference was associated with a decline in older adults’ theory of mind abilities. However, we did not find evidence of any significant age-related difference in the evaluations of helpful actions. We argue that the selective association of aging with changes in the evaluation of harmful but not helpful actions may be due also to motivational factors and highlight some implications of the present findings for judicial systems.
The asymptotics of semi-supervised learning in discriminative probabilistic models

Semi-supervised learning aims at taking advantage of unlabeled data to improve the efficiency of supervised learning procedures. For discriminative models however, this is a challenging task. In this contribution, we introduce an original methodology for using unlabeled data through the design of a simple semi-supervised objective function. We prove that the corresponding semi-supervised estimator is asymptotically optimal. The practical consequences of this result are discussed for the case of the logistic regression model.
Macrophage Transactivation for Chemokine Production Identified as a Negative Regulator of Granulomatous Inflammation Using Agent-Based Modeling

Cellular activation in trans by interferons, cytokines, and chemokines is a commonly recognized mechanism to amplify immune effector function and limit pathogen spread. However, an optimal host response also requires that collateral damage associated with inflammation is limited. This may be particularly so in the case of granulomatous inflammation, where an excessive number and/or excessively florid granulomas can have significant pathological consequences. Here, we have combined transcriptomics, agent-based modeling, and in vivo experimental approaches to study constraints on hepatic granuloma formation in a murine model of experimental leishmaniasis. We demonstrate that chemokine production by non-infected Kupffer cells in the Leishmania donovani-infected liver promotes competition with infected KCs for available iNKT cells, ultimately inhibiting the extent of granulomatous inflammation. We propose trans-activation for chemokine production as a novel broadly applicable mechanism that may operate early in infection to limit excessive focal inflammation.
Bipartite Ranking through Minimization of Univariate Loss

Minimization of the rank loss or, equivalently, maximization of the AUC in bipartite ranking calls for minimizing the number of disagreements between pairs of instances. Since the complexity of this problem is inherently quadratic in the number of training examples, it is tempting to ask how much is actually lost by minimizing a simple univariate loss function, as done by standard classification methods, as a surrogate. In this paper, we first note that minimization of 0/1 loss is not an option, as it may yield an arbitrarily high rank loss. We show, however, that better results can be achieved by means of a weighted (cost-sensitive) version of 0/1 loss. Yet, the real gain is obtained through margin-based loss functions, for which we are able to derive proper bounds, not only for rank risk but, more importantly, also for rank regret. The paper is completed with an experimental study in which we address specific questions raised by our theoretical analysis.
Photocatalytic Activation of TiO2 Biomaterials by UV and X-Rays

TiO2 films of varying mineralogical and microstructural characteristics were fabricated on sand-blasted Ti6Al4V plates by anodisation in 2 M sulphuric acid (H2SO4) and 2 M phosphoric acid (H3PO4) at 120 V and 300 mA/cm2 for 10 min and 15 min, respectively. The film formed by anodisation in H2SO4 consisted of both anatase and rutile while the film formed by anodisation in H3PO4 consisted only of rutile. This inconsistency is attributed to the presence of anatase below the level of detection in the sample anodised in the H3PO4, which consisted of a thinner TiO2 anodised film. SEM images demonstrated that H2SO4 resulted in arcing and resultant porosity while H3PO4 did not. Profilometry revealed that the former was rougher than the latter and that the latter was nearly the same roughness as the sand-blasted plate. These observations are consistent with the conclusion that H3PO4 formed a thinner anodised film and that the greater thickness from H2SO4 resulted in asperity formation, which enhanced arcing and densification. Although the anatase-rutile mixture and the greater roughness of the sample anodised in H2SO4 could be expected to have yielded superior performance, the fact that it did not is attributed to the greater bulk density and associated lower surface area of the TiO2 matrix. Preliminary cell culture tests showed that human osteoblast-like cells (MG63) were attached effectively on smooth anodised films (on polished plates) after 4 h of incubation while cell proliferation was confluent after 2 days. The major finding of the present work is that X-radiation in clinical doses (<200 cGy) is sufficient to cause degradation of organic species via photocatalysis.
Assessment of family planning services in Kenya: Evidence from the 2004 Kenya Service Provision Assessment Survey.

This study focused on factors associated with the readiness of Kenyan health facilities to provide quality and appropriate care to family planning clientele; the degree to which health care providers foster informed selection of an appropriate contraceptive method; and the extent to which clients perceive services to be of high quality. Data was obtained from the 2004 Kenya Service Provision Assessment. The composite indicators scores for facility readiness were generally low and many facilities lacked simple items like visual aids guidelines towels speculum etc. There were marked differences in facility readiness by region facility type and managing authority. Provider service provision scores were generally high but the only important difference was by region. Client satisfaction was dependent on the facility type managing authority sex of the provider and the waiting time to receive services. Clients were more likely to be satisfied with female rather than male providers. Clients were less satisfied in Nyanza although the facilities were more ready with high-performing providers. In contrast North Eastern Province had less ready facilities but high client satisfaction and high provider performance. Health centre clinics and dispensaries need to be revamped to appropriate standards so as to include all basic elements of family planning service provision. North Eastern Province with motivated workers highly satisfied clients but poor facilities deserves proper attention. Facilities in Nairobi need improvements in staff supervision and retraining. There is need to educate the clientele on the availability of appropriate services within the government facilities.
Representation Tradeoffs for Hyperbolic Embeddings

Hyperbolic embeddings offer excellent quality with few dimensions when embedding hierarchical data structures like synonym or type hierarchies. Given a tree, we give a combinatorial construction that embeds the tree in hyperbolic space with arbitrarily low distortion without using optimization. On WordNet, our combinatorial embedding obtains a mean-average-precision of 0.989 with only two dimensions, while Nickel et al.'s recent construction obtains 0.87 using 200 dimensions. We provide upper and lower bounds that allow us to characterize the precision-dimensionality tradeoff inherent in any hyperbolic embedding. To embed general metric spaces, we propose a hyperbolic generalization of multidimensional scaling (h-MDS). We show how to perform exact recovery of hyperbolic points from distances, provide a perturbation analysis, and give a recovery result that allows us to reduce dimensionality. The h-MDS approach offers consistently low distortion even with few dimensions across several datasets. Finally, we extract lessons from the algorithms and theory above to design a PyTorch-based implementation that can handle incomplete information and is scalable.
Modelling the Abundances of Two Major Culicoides (Diptera: Ceratopogonidae) Species in the Niayes Area of Senegal

In Senegal, considerable mortality in the equine population and hence major economic losses were caused by the African horse sickness (AHS) epizootic in 2007. Culicoides oxystoma and Culicoides imicola, known or suspected of being vectors of bluetongue and AHS viruses are two predominant species in the vicinity of horses and are present all year-round in Niayes area, Senegal. The aim of this study was to better understand the environmental and climatic drivers of the dynamics of these two species. Culicoides collections were obtained using OVI (Onderstepoort Veterinary Institute) light traps at each of the 5 sites for three nights of consecutive collection per month over one year. Cross Correlation Map analysis was performed to determine the time-lags for which environmental variables and abundance data were the most correlated. C. oxystoma and C. imicola count data were highly variable and overdispersed. Despite modelling large Culicoides counts (over 220,000 Culicoides captured in 354 night-traps), using on-site climate measures, overdispersion persisted in Poisson, negative binomial, Poisson regression mixed-effect with random effect at the site of capture models. The only model able to take into account overdispersion was the Poisson regression mixed-effect model with nested random effects at the site and date of capture levels. According to this model, meteorological variables that contribute to explaining the dynamics of C. oxystoma and C. imicola abundances were: mean temperature and relative humidity of the capture day, mean humidity between 21 and 19 days prior a capture event, density of ruminants, percentage cover of water bodies within a 2 km radius and interaction between temperature and humidity for C. oxystoma; mean rainfall and NDVI of the capture day and percentage cover of water bodies for C. imicola. Other variables such as soil moisture, wind speed, degree days, land cover or landscape metrics could be tested to improve the models. Further work should also assess whether other trapping methods such as host-baited traps help reduce overdispersion.
Probiotics, prematurity and neurodevelopment: follow-up of a randomised trial

Objective To determine the impact of one probiotics combination on the neurodevelopment of very preterm children at 2–5 years corrected gestational age (CA). Design Follow-up study of survivors of a double-blinded, placebo-controlled, randomised trial of probiotic effects on late-onset sepsis in very preterm infants that found reduced necrotising enterocolitis. Setting 10 tertiary perinatal centres in Australia and New Zealand. Patients 1099 very preterm infants born <32 weeks’ gestation and weighing <1500 g. Intervention Probiotics (Bifidobacterium infantis, Streptococcus thermophilus and Bifidobacterium lactis) or placebo administered from birth until discharge home or term CA, whichever came sooner. Main outcome measures Major neurodevelopmental impairment comprised any of moderate/severe cerebral palsy (Gross Motor Function Classification System score 2–5), motor impairment (Bayley-III Motor Composite Scale <–2SD or Movement Assessment Battery for Children <15th centile if >42 months’ CA), cognitive impairment (Bayley-III Composite Cognitive or Language Scales <–2SD or Wechsler Preschool and Primary Scale of Intelligence Full Scale Intelligence Quotient <–2SD if >42 months’ CA), blindness or deafness. Results Outcome data were available for 735 (67%) participants, with 71 deaths and 664/1028 survivors assessed at a mean age of 30 months. Survival free of major neurodevelopmental impairment was comparable between groups (probiotics 281 (75.3%) vs placebo 271 (74.9%); relative risk 1.01 (95% CI 0.93 to 1.09)). Rates of deafness were lower in probiotic-treated children (0.6% vs 3.4%). Conclusion Administration of the probiotics combination Bifidobacterium infantis, Streptococcus thermophilus and Bifidobacterium lactis to very preterm babies from soon after birth until discharge home or term CA did not adversely affect neurodevelopment or behaviour in early childhood. Trial registration number Australia and New Zealand Clinical Trials Register (ANZCTR): ACTRN012607000144415.
Dynamic Action Repetition for Deep Reinforcement Learning

One of the long standing goals of Artificial Intelligence (AI) is to build cognitive agents which can perform complex tasks from raw sensory inputs without explicit supervision. Recent progress in combining Reinforcement Learning objective functions and Deep Learning architectures has achieved promising results for such tasks. An important aspect of such sequential decision making problems, which has largely been neglected, is for the agent to decide on the duration of time for which to commit to actions. Such action repetition is important for computational efficiency, which is necessary for the agent to respond in real-time to events (in applications such as self-driving cars). Action Repetition arises naturally in real life as well as simulated environments. The time scale of executing an action enables an agent (both humans and AI) to decide the granularity of control during task execution. Current state of the art Deep Reinforcement Learning models, whether they are off-policy or on-policy, consist of a framework with a static action repetition paradigm, wherein the action decided by the agent is repeated for a fixed number of time steps regardless of the contextual state while executing the task. In this paper, we propose a new framework - Dynamic Action Repetition which changes Action Repetition Rate (the time scale of repeating an action) from a hyper-parameter of an algorithm to a dynamically learnable quantity. At every decision-making step, our models allow the agent to commit to an action and the time scale of executing the action. We show empirically that such a dynamic time scale mechanism improves the performance on relatively harder games in the Atari 2600 domain, independent of the underlying Deep Reinforcement Learning algorithm used.
Hyperparameters and tuning strategies for random forest

The random forest (RF) algorithm has several hyperparameters that have to be set by the user, for example, the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples that a node must contain, and the number of trees. In this paper, we first provide a literature review on the parameters' influence on the prediction performance and on variable importance measures. It is well known that in most cases RF works reasonably well with the default values of the hyperparameters specified in software packages. Nevertheless, tuning the hyperparameters can improve the performance of RF. In the second part of this paper, after a presenting brief overview of tuning strategies, we demonstrate the application of one of the most established tuning strategies, model‐based optimization (MBO). To make it easier to use, we provide the tuneRanger R package that tunes RF with MBO automatically. In a benchmark study on several datasets, we compare the prediction performance and runtime of tuneRanger with other tuning implementations in R and RF with default hyperparameters.
Conformational sampling of membranes by Akt controls its activation and inactivation

Significance Akt is a paradigmatic lipid-activated kinase, which is frequently hyperactivated in human cancer. In the absence of PI(3,4,5)P3 or PI(3,4)P2, Akt is maintained in an inactive conformation by an inhibitory interaction between its membrane-binding PH domain and its kinase domain. Here, we describe the conformational changes associated with its binding to PI(3,4,5)P3, leading to disruption of the inhibitory PH−kinase interface, and its consequent activation by protein kinases. Intriguingly, we find that reversal of those conformational changes promotes its inactivation by protein phosphatases. The activation of Akt is thereby restricted to discrete membrane locations, and it is rapidly inactivated upon dissociation. We propose a model in which activation, substrate phosphorylation, and inactivation of Akt are tightly coupled to the membrane. The protein kinase Akt controls myriad signaling processes in cells, ranging from growth and proliferation to differentiation and metabolism. Akt is activated by a combination of binding to the lipid second messenger PI(3,4,5)P3 and its subsequent phosphorylation by phosphoinositide-dependent kinase 1 and mechanistic target of rapamycin complex 2. The relative contributions of these mechanisms to Akt activity and signaling have hitherto not been understood. Here, we show that phosphorylation and activation by membrane binding are mutually interdependent. Moreover, the converse is also true: Akt is more rapidly dephosphorylated in the absence of PIP3, an autoinhibitory process driven by the interaction of its PH and kinase domains. We present biophysical evidence for the conformational changes in Akt that accompany its activation on membranes, show that Akt is robustly autoinhibited in the absence of PIP3 irrespective of its phosphorylation, and map the autoinhibitory PH−kinase interface. Finally, we present a model for the activation and inactivation of Akt by an ordered series of membrane binding, phosphorylation, dissociation, and dephosphorylation events.