Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Bioenergy in Achieving the Carbon Neutrality Target in Finland by 2035—A Case Study of Student Surveyed at University in Finland

The largest share of renewable energy in Finland comes from bioenergy. In 2019, bioenergy accounted for 82% (416 PJ, 116 TWh) of renewable energy in Finland. This study assesses the potential for increasing bioenergy in energy production by 2035 and what role it will play in achieving the carbon neutrality target in Finland. The role of different energy sources in the energy system was examined using existing scenarios developed for The National Long-Term Strategy. Two alternative low-emission scenarios have been developed to last until 2050 to meet the 2035 carbon neutrality target. In 2035, the amount of bioenergy has risen to 520 550 PJ (144 153 TWh), which is about 70% of renewable energy consumption. This means, that the bioenergy resource has been fully deployed and the relative share of bioenergy in renewables has decreased slightly. The study also included a survey to university students to map out how likely a carbon neutrality target is to be considered by 2035. University students were unsure of achieving the carbon neutrality target by 2035. The schedule was considered challenging especially in the transport sector. Bioenergy was also seen as still playing an important role, especially in heat production. Achieving significant emission reductions will require significant electrification in all energy use sectors, as fossil fuels cannot be sustainably replaced by bioenergy on a sufficiently large scale.
Ensemble Kernel Methods for Dynamic Portfolio Valuation

—In this paper, the potential of ensemble learning methods for dynamic portfolio valuation is assessed. First a portfolio is modelled and a baseline estimator is created, both strongly guided by the results of Boudabsa and Filipovic [1]. Then a number of aggregating and boosting techniques are evaluated and compared to the baseline performance. The reults show that although some ensemble methods perform well in lower dimensional cases, they seem to be unable to reduce the error signiﬁcantly in the high-dimensional case.
Impacts of the Saharan air layer on the physical properties of the Atlantic tropical cyclone cloud systems: 2003–2019

Abstract. It is generally known that the tropical cyclone (TC) cloud systems (TCCS) in the North Atlantic region frequently occur during boreal summer, while the Saharan dust outbreaks concurrently. The Sahara air layer (SAL), an elevated layer containing Saharan dry air and mineral dust, makes crucial impacts on the generation and evolution of TCs. However, the effects of SAL on the physical (macro and micro) characteristics of the Atlantic TCCS have not been well constrained, and the interaction mechanisms between them still need further investigation. In this study, our primary interest is to distinguish the various effects of SAL on different intensities of TCs, and further find out the probable causes of the varied feedback. Therefore, we attempt to identify whether and how the effects of the SAL play a positive or negative role on the TCCS, and to draw a qualitative conclusion of how SAL affects the various intensities of the TCs. This paper focuses on the 70 TC samples from July to September in the years of 2003–2019 to investigate the physical effects of SAL on three intensities of TCs, i.e.: the tropical depression (TD), tropical storm (TS), and hurricane (HU). The results show that SAL has a positive impact on the macro properties of HU but significantly suppresses the TD. It appears that the SAL attributes little to the variation of ice cloud effective radius (CERi) for TS, whereas CERi changes significantly and differentially for TD and HU. When affected by SAL, the probability density function (PDF) curve of CERi generally shifts to the smaller value for TD, but the PDF curve becomes flatten for HU. Our analysis indicates that the various responses of TCCS to SAL are determined by the combined effects of dry air masses, the dust aerosols as ice nuclei, as well as the thermodynamic and moisture conditions. Based on the observation data analysis, a concept scheme description has been concluded to deepen our recognition of the effects of SAL on the TCCS.
ANTIMICROBIAL ANALYSIS OF DIFFERENT PARTS EXTRACT IN DIFFERENT SOLVENT SYSTEM OF A WASTE WEED - CALOTROPIS PROCERA.

Â Objective: In the current study, we have focused on the major secondary metabolite containing parts such as flower, leaf, and root for phytochemical extraction with three different solvent systems to make a comparative study against three virulent bacteria species which are capable of intestinal infection, pneumonia, skin infections, and food poisoning.Methods: Antimicrobial activity of ethanol, methanol, and chloroform extracts from bark, leaves and roots of Calotropis procera, was examined against three virulent bacteria species: Escherichia coli, Staphylococcus aureus, and Bacillus subtilis using disc diffusion method.Results: The ethanol extract of leaf showed significant activity against S. aureus with a zone of inhibition ranging from 14 to 20 mm for S. aureus. The ethanol extract of flower was effective against E. coli with maximum 18 mm. Ethanol extract of root showed significant activity against S. aureus. Methanol extract of leaves showed moderate activity against S. aureus with a zone of inhibition ranging from 14 to 20 mm. Methanol extract of root showed significant activity against S. aureus with a zone of inhibition ranging from 12 to 22 mm. Methanol extract of flowers showed activity against E. coli with a zone of inhibition ranging from 11 to 20 mm. The chloroform extract of leaves showed significant activity against S. aureus. Chloroform extract of flower showed activity with zone of inhibition ranging from 11 to 17 mm for S. aureus chloroform extract of root showed activity against E. coli with zone of inhibition ranging from 9 to 17 mm.Conclusion: From the above study, it can be concluded that the activity of the plant extract may be due to the secondary metabolites or broad-spectrum antibiotic compounds present in it.
Correlation Robust Inﬂuence Maximization: Supplementary Material

The aim of this work is to address the possible pitfalls to the independence assumption in a social network, as used in the study of influence maximization. As discussed previously, how an idea, product, or piece of news makes its way through a network could very well be impacted by natural social biases, thus connecting parts of a social network in ways that could have been unforeseen. The methodology presented thus attempts to make this possibility a consideration during the selection of seed set, and hence find “influential" members to a network regardless of whatever underlying correlations may exist. This potentially can reduce the impact of biases that the independence assumption may cause.
Learning Convolutional Neural Networks for Age Estimation

In this work we put together a new dataset in the field of age estimation, the Wikipeople database, consisting of 217800 samples. The whole process of getting the database is described in detail so it is possible to follow up in the future and extend the dataset in the same way as was created. The correspondence of the resulting labels to the truth is statistically evaluated. Further, the age estimation task was formulated as classification problem and a set of
Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters

A detailed understanding of how and when SARS-CoV-2 transmission occurs is crucial for designing effective prevention measures. Other than contact tracing, genome sequencing provides information to help infer who infected whom. However, the effectiveness of the genomic approach in this context depends on both (high enough) mutation and (low enough) transmission rates. Today, the level of resolution that we can obtain when describing SARS-CoV-2 outbreaks using just genomic information alone remains unclear. In order to answer this question, we sequenced 49 SARS-CoV-2 patient samples from ten local clusters for which partial epidemiological information was available, and inferred transmission history using genomic variants. Importantly, we obtained high-quality genomic data, sequencing each sample twice and using unique barcodes to exclude cross-sample contamination. Phylogenetic and cluster analyses showed that consensus genomes were generally sufficient to discriminate among independent transmission clusters. However, levels of intrahost variation were low, which prevented in most cases the unambiguous identification of direct transmission events. After filtering out recurrent variants across clusters, the genomic data were generally compatible with the epidemiological information but did not support specific transmission events over possible alternatives. We estimated the effective transmission bottleneck size to be 1-2 viral particles for sample pairs whose donor-recipient relationship was likely. Our analyses suggest that intrahost genomic variation in SARS-CoV-2 might be generally limited and that homoplasy and recurrent errors complicate identifying shared intrahost variants. Reliable reconstruction of direct SARS-CoV-2 transmission based solely on genomic data seems hindered by a slow mutation rate, potential convergent events, and technical artifacts. Detailed contact tracing seems essential in most cases to study SARS-CoV-2 transmission at high resolution.
3D bioprinting of stem cell-laden cardiac patch: A promising alternative for myocardial repair

Stem cell-laden three-dimensional (3D) bioprinted cardiac patches offer an alternative and promising therapeutic and regenerative approach for ischemic cardiomyopathy by reversing scar formation and promoting myocardial regeneration. Numerous studies have reported using either multipotent or pluripotent stem cells or their combination for 3D bioprinting of a cardiac patch with the sole aim of restoring cardiac function by faithfully rejuvenating the cardiomyocytes and associated vasculatures that are lost to myocardial infarction. While many studies have demonstrated success in mimicking cardiomyocytes' behavior, improving cardiac function and providing new hope for regenerating heart post-myocardial infarction, some others have reported contradicting data in apparent ways. Nonetheless, all investigators in the field are speed racing toward determining a potential strategy to effectively treat losses due to myocardial infarction. This review discusses various types of candidate stem cells that possess cardiac regenerative potential, elucidating their applications and limitations. We also brief the challenges of and an update on the implementation of the state-of-the-art 3D bioprinting approach to fabricate cardiac patches and highlight different strategies to implement vascularization and augment cardiac functional properties with respect to electrophysiological similarities to native tissue.
Hitting a Wall: An Ambiguous Case of Wallenberg Syndrome

Wallenberg syndrome is the most common stroke of the posterior circulation. Diagnosis of Wallenberg syndrome is often overlooked as initial MRI may show no visible lesion. We present an atypical case of Wallenberg syndrome in which the initial MRI of the brain was normal. Our patient is a 65-year-old male who was brought in by emergency medical services complaining of right-sided facial droop, slurred speech, and left-sided weakness for one day. Physical examination showed decreased left arm and leg strength compared to the right side, decreased left facial temperature sensations, decreased left arm and leg temperature sensations, and difficulty sitting upright with an associated leaning towards the left side. An initial magnetic resonance imaging (MRI) of the brain with and without contrast revealed no abnormality. In light of such a high suspicion for stroke based on the patient’s neurologic deficits, a repeat MRI of the brain was performed three days later and exposed a small focus of bright signal (hyperintensity) on T2-weighted fluid-attenuated inversion recovery and diffusion-weighted imaging (DWI) in the left posterior medulla. Wallenberg syndrome, also known as lateral medullary syndrome or posterior inferior cerebellar artery syndrome, is a constellation of symptoms caused by posterior vascular accidents. The neurological deficits associated with this disease are due to damage of the lateral medulla, inferior cerebellar peduncle, nucleus of trigeminal nerve, nucleus and fibers of vagus and glossopharyngeal nerves, descending sympathetic tract, spinothalamic tract, and/or vestibular nuclei. MRI with DWI is the gold standard to confirm the diagnosis. Wallenberg syndrome has the potential to leave patients extremely debilitated. Early detection, management, and rehabilitation are critical for improving post-stroke recovery.
Brain-on-a-Chip: Characterizing the next generation of advanced in vitro platforms for modeling the central nervous system

The complexity of the human brain creates significant, almost insurmountable challenges for neurological drug development. Advanced in vitro platforms are increasingly enabling researchers to overcome these challenges, by mimicking key features of the brain's composition and functionality. Many of these platforms are called “Brains-on-a-Chip”—a term that was originally used to refer to microfluidics-based systems containing miniature engineered tissues, but that has since expanded to describe a vast range of in vitro central nervous system (CNS) modeling approaches. This Perspective seeks to refine the definition of a Brain-on-a-Chip for the next generation of in vitro platforms, identifying criteria that determine which systems should qualify. These criteria reflect the extent to which a given platform overcomes the challenges unique to in vitro CNS modeling (e.g., recapitulation of the brain's microenvironment; inclusion of critical subunits, such as the blood–brain barrier) and thereby provides meaningful added value over conventional cell culture systems. The paper further outlines practical considerations for the development and implementation of Brain-on-a-Chip platforms and concludes with a vision for where these technologies may be heading.
Isolated Cardiac Sarcoidosis with High-Grade Heart Block: Utilization of New Diagnostic Guidelines

Cardiac sarcoidosis can present with heart failure and conduction disease. This is a case of a 58-year-old male who presented for dyspnea, edema, and varying degrees of heart block. Using new updated diagnostic guidelines and multimodal cardiac imaging, he was diagnosed with isolated cardiac sarcoidosis.
Efficient algorithms for collecting the statistics of large-scale IP address data

Compiling the statistics of large-scale IP address data is an essential task in network traffic measurement. The statistical results are used to evaluate the potential impact of user behaviors on network traffic. This requires algorithms that are capable of storing and retrieving a high volume of IP addresses within time and memory constraints. In this paper, we present two efficient algorithms for collecting the statistics of large-scale IP addresses that balance time efficiency and memory consumption. The proposed solutions take into account the sparse nature of the statistics of IP addresses while building the hash function and maintain a dynamic balance among layered memory blocks. There are two layers in the first proposed method, each of which contains a limited number of memory blocks. Each memory block contains 256 elements of size $256 \times 8$ bytes for a 64-bit system. In contrast to built-in hash mapping functions, the proposed solution completely avoids expensive hash collisions while retaining the linear time complexity of hash-based solutions. Moreover, the mechanism dynamically determines the hash index length according to the range of IP addresses, and can balance the time and memory constraints. In addition, we propose an efficient parallel scheme to speed up the collection of statistics. The experimental results on several synthetic datasets show that the proposed method substantially outperforms the baselines with respect to time and memory space efficiency.
Gravitational anomalies, axions and a string-inspired running vacuum model in Cosmology

I review a string-inspired cosmological model with gravitational anomalies in its early epochs, which is based on fields from the (bosonic) massless gravitational multiplet of strings, in particular gravitons and Kalb Ramond (KR), string-model independent, axions (the dilaton is assumed constant). I show how condensation of primordial gravitational waves, which are generared at the very early eras immediately after the big bang, can lead to inflation of the so called running vacuum model (RVM) type, without external inflatons. The role of the slow-roll field is played by the KR axion, but it does not drive inflation. The non-linearities in the anomaly terms do. Chiral fermionic matter excitations appear at the end of this RVM inflation, as a result of the decay of the RVM vacuum, and are held responsible for the cancellation of the primordial gravitational anomalies. Chiral anomalies, however, survive in the post-inflationary epochs, and can lead to the generation of a non perturbative mass for the KR axion, which could thus play the role of dark matter in this Universe. As a result of the condensed gravitational anomaly, there is a Lorentz-invariance violating KR axion background, which remains undiluted during the RVM inflation, and can lead to baryogenesis through leptogenesis in the radiation era, in models with sterile right-handed neutrinos. I also discuss the phenomenology of the model in the modern era, paying particular attention to linking it with a version of RVM, called type II RVM, which arguably can alleviate observed tensions in the current-epoch cosmological data.
Toward Human-Level Artificial Intelligence

In this paper, we present our research on programming human-level artiﬁcial intelligence (HLAI), including 1) a deﬁnition of HLAI, 2) an environment to develop and test HLAI, and 3) a cognitive architecture for HLAI. The term AI is used in a broad meaning, and HLAI is not clearly deﬁned. I claim that the essence of Human-Level Intelligence to be the capability to learn from others’ experiences via language. The key is that the event described by language has the same effect as if the agent experiences it ﬁrsthand for the update of the behavior policy. To develop and test models with such a capability, we are developing a simulated environment called SEDRo. There is a 3D Home, and a mother character takes care of the baby (the learning agent) and teaches languages. The environment provides comparable experiences to that of a human baby from birth to one year. Finally, I propose a cognitive architecture of HLAI called Modulated Heterarchical Prediction Memory (mHPM). In mHPM, there are three components: a universal module that learns to predict the next vector given the sequence of vector signals, a heterarchical network of those modules, and a reward-based modulation of learning. mHPM models the workings of the neocortex but the innate auxiliary units such hippocampus, reward system, instincts, and amygdala play critical roles, too.
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation

Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when bias is amplified over time as a few popular items are repeatedly over-represented in recommendation lists. This phenomenon can be viewed as a recommendation feedback loop: the system repeatedly recommends certain items at different time points and interactions of users with those items will amplify bias towards those items over time. This issue has been extensively studied in the literature on model-based or neighborhood-based recommendation algorithms, but less work has been done on online recommendation models such as those based on multi-armed Bandit algorithms. In this paper, we study exposure bias in a class of well-known bandit algorithms known as Linear Cascade Bandits. We analyze these algorithms on their ability to handle exposure bias and provide a fair representation for items and suppliers in the recommendation results. Our analysis reveals that these algorithms fail to treat items and suppliers fairly and do not sufficiently explore the item space for each user. To mitigate this bias, we propose a discounting factor and incorporate it into these algorithms that controls the exposure of items at each time step. To show the effectiveness of the proposed discounting factor on mitigating exposure bias, we perform experiments on two datasets using three cascading bandit algorithms and our experimental results show that the proposed method improves the exposure fairness for items and suppliers.
P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly Screening

Anomaly detection plays a pivotal role in numerous real-world scenarios, such as industrial automation and manufacturing intelligence. Recently, variational inference-based anomaly analysis has attracted researchers' and developers' attention. It aims to model the defect-free distribution so that anomalies can be classified as out-of-distribution samples. Nevertheless, there are two disturbing factors that need us to prioritize: (i) the simplistic prior latent distribution inducing limited expressive capability; (ii) the strong probability distance notion results in collapsed features. In this paper, we propose a novel Patch-wise Wasserstein AutoEncoder (P-WAE) architecture to alleviate those challenges. In particular, a patch-wise variational inference model coupled with solving the jigsaw puzzle is designed, which is a simple yet effective way to increase the expressiveness of the latent manifold. This makes using the model on high-dimensional practical data possible. In addition, we leverage a weaker measure, sliced-Wasserstein distance, to achieve the equilibrium between the reconstruction fidelity and generalized representations. Comprehensive experiments, conducted on the MVTec AD dataset, demonstrate the superior performance of our proposed method.
A simple approach to measuring thick organic films using the XPS inelastic background

In this work, we explore the possibility of finding a simple mathematical function that describes XPS background intensities as a function of energy and depth of emission. We describe the shape of the global structure of the background in XPS survey spectra for organic overlayer films with thicknesses greater than ~10 nm. In this situation, the shape of XPS backgrounds becomes statistical, and the detailed structure of, for example, energy‐loss functions and elastic‐scattering cross sections can be disregarded. Using Irganox 1010 as a model organic overlayer on silicon dioxide, gold, and silver substrates, a consistent description of the XPS background can be found, and overlayer thicknesses greater than 50 nm can be measured in the case of gold substrates. Furthermore, by using a suitable subset of these spectra, it is possible to separate the influence of overlayer thickness, electron kinetic energy, and intensity on the global shape of the spectrum. We show that it is possible to fully describe the shape of calibrated XPS survey spectra with reasonable accuracy for samples that have organic overlayers using only binding energy and sensitivity factor data. The same functions are shown to provide a reasonable description of the XPS spectra of silicon with an oxide overlayer.