Miracle-dz/UniFAR-FTUs huggingface dataset:
Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo

Resource provisioning in multi-tenant stream processing systems faces the dual challenges of keeping resource utilization high (without over-provisioning), and ensuring performance isolation. In our common production use cases, where streaming workloads have to meet latency targets and avoid breaching service-level agreements, existing solutions are incapable of handling the wide variability of user needs. Our framework called Cameo uses fine-grained stream processing (inspired by actor computation models), and is able to provide high resource utilization while meeting latency targets. Cameo dynamically calculates and propagates priorities of events based on user latency targets and query semantics. Experiments on Microsoft Azure show that compared to state-of-the-art, the Cameo framework: i) reduces query latency by 2.7X in single tenant settings, ii) reduces query latency by 4.6X in multi-tenant scenarios, and iii) weathers transient spikes of workload.
Cardiac Output in Swimming California Sea Lions, Zalophus californianus

Cardiac output was determined by the thermodilution technique in three California sea lions while resting and while swimming. Metabolic rates increased sevento ninefold above resting rates during maximal exercise. While the sea lions were at rest, stroke volume was also determined by simultaneously counting heart rate during cardiac output determinations. At rest, cardiac output (2.5-3.0 mL kg⁻¹s⁻¹) and stroke volume (2 mL kg⁻¹) were similar to those of harbor seals and terrestrial mammals of similar mass. During exercise, mean cardiac output increased linearly with work load and surface/submerged intervals were short and frequent. The exercise capacity of swimming sea lions appears similar to that of harbor seals, but the exercise response resembles that of terrestrial mammals more than that of harbor seals.
A new experimental phenomenological method to explore the subjective features of psychological phenomena: its application to binocular rivalry

Abstract The subjective features of psychological phenomena have been studied intensively in experimental science in recent years. Although various methods have been proposed to identify subjective features of psychological phenomena, there are elusive subjective features such as the spatiotemporal structure of experience, which are difficult to capture without some additional methodological tools. We propose a new experimental method to address this challenge, which we call the contrast-based experimental phenomenological method (CEP). CEP proceeds in four steps: (i) front-loading phenomenology, (ii) online second-personal interview, (iii) questionnaire survey, and (iv) hypotheses testing. It differs from other experimental phenomenological methods in that it takes advantage of phenomenal contrasts in collecting phenomenological data. In this paper, we verify the validity and productivity of this method by applying it to binocular rivalry (BR). The study contributes to empirical research on BR in three respects. First, it provides additional evidence for existing propositions about the subjective features of BR: e.g. the proposition that the temporal dynamics of the experience depend upon subject-dependent parameters such as attentional change. Second, it deepens our understanding of the spatiotemporal structures of the transition phase of BR. Third, it elicits new research questions about depth experience and individual differences in BR. The presence of such contributions demonstrates the validity and productivity of CEP.
Flexible Process Model Mapping using Relaxation Labeling

Computing a mapping between two process models is a crucial technique, since it enables reasoning and operating across processes, like providing a similarity score between two processes, or merging different process variants to generate a consolidated process model. In this paper we present a new flexible technique for process model mapping, based on the relaxation labeling constraint satisfaction algorithm. The technique can be instantiated so that different modes are devised, depending on the context. For instance, it can be adapted to the case where one of the mapped process models is incomplete, or it can be used to ground an adaptable similarity measure between process models. The approach has been implemented inside the open platform NLP4BPM, providing a visualization of the performed mappings and computed similarity scores. The experimental results witness the flexibility and usefulness of the technique proposed.
Blockchain-enabled secure communication for drone delivery: a case study in COVID-like scenarios

COVID-19 made the world stop, with people trapped inside their homes and governments trying to restrict the public movement. However, to accomplish this, one big problem that emerged and outscored everything else was catering to the day to day necessary items of the people without human involvement. In this regard, we propose a blockchain-enabled secure communication framework for delivering the goods in COVID-19 like scenarios by leveraging the drones that are available with commercial retail providers. The blockchain scheme is used to create smart contracts to build the trust of buyers and sellers on the framework as the payments are made through the smart contract executions. The blockchain based order processing ensures the integrity and authenticity of the information. Moreover, a communication model is presented along with the order, delivery and payment phases. The results prove the effectiveness of the proposed scheme by evaluating it based on gas price, transaction time, and mining time.
Steering Interbranch Polariton Exchange via Coherent and Incoherent Dynamics

Controlling light-matter based quantum systems in the strong coupling regime allows for exploring quantum simulation of many-body physics in nowadays architectures. The atom-field interaction in a cavity QED network provides control and scalability for quantum information processing. Here, we propose the control of single- and two-body Jaynes-Cummings systems in a non-equilibrium scenario, which allows us to establish conditions for the coherent interchange of polariton species. Furthermore, the incoherent interchange triggered by cavity and atomic losses, exhibits a detuning-dependent asymmetry in the absorption spectrum. Finally, our findings are featured in the framework of the Superfluid-Mott Insulator quantum phase transition.
Justification of the asymptotic coupled mode approximation of out-of-plane gap solitons in Maxwell equations

In periodic media gap solitons with frequencies inside a spectral gap but close to a spectral band can be formally approximated by a slowly varying envelope ansatz. The ansatz is based on the linear Bloch waves at the edge of the band and on effective coupled mode equations (CMEs) for the envelopes. We provide a rigorous justification of such CME asymptotics in two-dimensional photonic crystals described by the Kerr nonlinear Maxwell system. We use a Lyapunov–Schmidt reduction procedure and a nested fixed point argument in the Bloch variables. The theorem provides an error estimate in H2(R2) between the exact solution and the envelope approximation. The results justify the formal and numerical CME-approximation in Dohnal and Dörfler, [2013 Multiscale Model. Simul. 11 162–191].
Exploration of Sorafenib Influences on Gene Expression of Hepatocellular Carcinoma

Purpose The aim of this study was to develop a comprehensive differential gene profile for hepatocellular carcinoma (HCC) patients treated with sorafenib. Methods The RNA sequencing data and miRNA sequencing data of 114 HCC patients treated with sorafenib only and 326 HCC control patients treated without any chemotherapeutic drugs were studied using differential expression, functional enrichment, and protein–protein interaction analysis. Results Compared with HCC patients without any chemotherapy drugs, the sorafenib-treated patients develop 66 differentially expressed genes (DEGs), including 12 upregulated genes and 54 downregulated genes. Additionally, three differentially expressed miRNAs (DEMs) also show specific expression pattern. With further analysis, five primary genes including HTR2C, TRH, AGTR2, MCHR2, and SLC6A2 as well as three miRNAs (hsa-miR-4445, hsa-miR-466, and hsa-miR-2114) have been suggested as the potential targets for sorafenib. The specific gene expression of five genes has been validated in clinical HCC patients by ELISA method. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses indicate several significantly enriched biological processes (BPs), cellular components (CCs), and molecular functions (MFs). Conclusion Since sorafenib is becoming increasingly important in HCC treatment clinically, this study will help us understand the potential targets and eliminate diverse existing side effects of it as well as explore several potential clinical biomarkers with comprehensive analysis of differential gene expression profile.
Exfoliated Pyrite (FeS2)- a non-van der Waals 2D Ferromagnet

Two-dimensional (2D) magnetism has been highly sought after since the discovery of graphene, the first and most studied of all the 2D materials. However, experimental evidence for ferromagnetic ordering in isotropic 2D crystals has been missing until a three-atoms thick CrI3 was shown to retain ferromagnetic ordering at finite temperatures down to a monolayer. However, 2D ferromagnetic materials discovered so far are having a transition temperature which is much lower than room temperature and hence are not useful for device applications. Here, we demonstrate successful isolation of a non-van der Waals 2D material derived from naturally occurring pyrite mineral (FeS2) by means of liquid phase exfoliation in organic solvent. It is evident from the magnetic measurements that 2D FeS2 is ferromagnetic with long-range magnetic ordering extending even up to the room-temperatures, whereas bulk pyrite precursor is weakly paramagnetic. Transmission electron microscopy together with Raman spectroscopy and XPS measurements confirm the crystallinity and purity of 2D FeS2. Atomic force microscopy affirms the 2D nature of exfoliated flakes. Theoretical density functional theory calculations corroborate the experimental findings along with the stability of exfoliated 2D sheets which is of prime importance in the case of non-van der Waals exfoliation.
Cognitive Learning-Aided Multi-Antenna Communications

Cognitive communications have emerged as a promising solution to enhance, adapt, and invent new tools and capabilities that transcend conventional wireless networks. Deep learning (DL) is critical in enabling essential features of cognitive systems because of its fast prediction performance, adaptive behavior, and model-free structure. These features are especially significant for multi-antenna wireless communications systems, which generate and handle massive data. Multiple antennas may provide multiplexing, diversity, or antenna gains that improve the capacity, bit error rate, or the signal-to-interference-plus-noise ratio, respectively. In practice, multi-antenna cognitive communications encounter challenges in terms of data complexity and diversity, hardware complexity, and wireless channel dynamics. DL solutions such as federated learning, transfer learning, and online learning tackle these problems at various stages of communications processing, including multi-channel estimation, hybrid beamforming, user localization, and sparse array design. This article provides a synopsis of various DL-based methods to impart cognitive behavior to multi-antenna wireless communications for improved robustness and adaptation to the environmental changes while providing satisfactory spectral efficiency and computation times. We discuss DL design challenges from the perspective of data, learning and transceiver architectures. In particular, we suggest quantized learning models, data/model parallelization, and distributed learning methods to address the aforementioned challenges.
Non-Equilibrium Steady State of the Lieb-Liniger model: multiple-integral representation of the time evolved many-body wave-function

We continue our study of the emergence of Non-Equilibrium Steady States in quantum integrable models focusing on the expansion of a Lieb-Liniger gas for arbitrary repulsive interaction. As a first step towards the derivation of the asymptotics of observables in the thermodynamic and large distance and time limit, we derive an exact multiple integral representation of the time evolved many-body wave-function. Starting from the known but complicated expression for the overlaps of the initial state of a geometric quench, which are derived from the Slavnov formula for scalar products of Bethe states, we eliminate the awkward dependence on the system size and distinguish the Bethe states into convenient sectors. These steps allow us to express the rather impractical sum over Bethe states as a multiple rapidity integral in various alternative forms. Moreover, we examine the singularities of the obtained integrand and calculate the contribution of the multivariable kinematical poles, which is essential information for the derivation of the asymptotics of interest.
Sequencing Analysis and Identification of the Primary Peptide Component of the Dialyzable Leukocyte Extract “Transferon Oral”: The Starting Point to Understand Its Mechanism of Action

“Transferon Oral” is a peptide-derived product with immunomodulatory properties obtained from the lysis and dialysis of human buffy coat. Its active pharmaceutical ingredient, generically known as Dialyzable Leucocyte Extract, is a mixture of peptide populations with reproducible proportions among batches. “Transferon Oral” modulates IFN-γ, TNF-α, and IL-6 and increases the survival rate in a herpes infection murine model when oropharyngeally (ORO) administered, which correlate with clinical observations where “Transferon Oral” is used as a therapeutic auxiliary in inflammatory diseases. Notwithstanding, how a peptide-derived product elicits systemic modulation of cytokines when ORO administered remains unclear. To shed light on the pharmacology of “Transferon Oral” its peptide components must be known. Ten “Transferon Oral” batches were sequenced by mass spectrometry and the intact peptides were identified. The most abundant peptides were the monomeric human Ubiquitin (Ub), a globular low-molecular mass protein, and an Ub variant which lacks the two-terminal Gly (Ub-GG). Recombinant Ub prevented murine death when ORO administered in a herpes infection murine model. Besides, the percentage of survival increased in groups treated with Transferon Oral+Ub and decreased in groups treated with Ub-depleted “Transferon Oral” respect to the group treated with “Transferon Oral” only. Our findings indicate that the biological properties of “Transferon Oral” are partially associated to the Ub content. They suggest that Ub may activate its extracellular receptor (CXCR-4) in the stomach eliciting systemic immunomodulatory effects via vagus nerve. This is the first report that identifies an active component of “Transferon Oral” with the potential for the development of oral peptide immunomodulators.
ESHRE PGT Consortium data collection XVI–XVIII: cycles from 2013 to 2015†

Abstract STUDY QUESTION What are the trends and developments in preimplantation genetic testing (PGT) in 2013–2015 as compared to previous years? SUMMARY ANSWER The main trends observed in the retrospective data collections 2013–2015, representing valuable data on PGT activity in (mainly) Europe, are the increased application of trophectoderm biopsy at the cost of cleavage stage biopsy and the continuing expansion of comprehensive testing technology in PGT for chromosomal structural rearrangements and for aneuploidies (PGT-SR and PGT-A). WHAT IS KNOWN ALREADY Since it was established in 1997, the ESHRE PGT Consortium has been collecting data from international PGT centres. To date, 15 data sets and an overview of the first 10 years of data collections have been published. STUDY DESIGN, SIZE, DURATION Collection of (mainly) European data by the PGT Consortium for ESHRE. The data for PGT cycles performed between 1 January 2013 and 31 December 2015 were provided by participating centres on a voluntary basis. For the collection of cycle, pregnancy and baby data, separate, pre-designed MS Excel tables were used. PARTICIPANTS/MATERIALS, SETTING, METHODS Data were submitted by 59, 60 and 59 centres respectively for 2013, 2014 and 2015 (full PGT Consortium members). Records with incomplete or inconsistent data were excluded from the calculations. Corrections, calculations, figures and tables were made by expert co-authors. MAIN RESULTS AND THE ROLE OF CHANCE For data collection XVI/XVII/XVIII, 59/60/59 centres reported data on 8164/9769/11 120 cycles with oocyte retrieval: 5020/6278/7155 cycles for PGT-A, 2026/2243/2661 cycles for PGT for monogenic/single gene defects, 1039/1189/1231 cycles for PGT-SR and 79/59/73 cycles for sexing for X-linked diseases. From 2013 until 2015, the uptake of biopsy at the blastocyst stage was mainly observed in cycles for PGT-A (from 23% to 36%) and PGT-SR (from 22% to 36%), alongside the increased application of comprehensive testing technology (from 66% to 75% in PGT-A and from 36% to 58% in PGT-SR). LIMITATIONS, REASONS FOR CAUTION The findings apply to the 59/60/59 participating centres and may not represent worldwide trends in PGT. Data were collected retrospectively and no details of the follow-up on PGT pregnancies and babies born were provided. WIDER IMPLICATIONS OF THE FINDINGS Being the largest data collection on PGT worldwide, detailed information about ongoing developments in the field is provided. STUDY FUNDING/COMPETING INTEREST(S) The study has no external funding and all costs are covered by ESHRE. There are no competing interests declared. TRIAL REGISTRATION NUMBER N/A.
Impact of Maladaptive Daydreaming on Grade Point Average (GPA) and the Association Between Maladaptive Daydreaming and Generalized Anxiety Disorder (GAD)

This study demonstrates the prevalence of the maladaptive daydreaming (MD) and generalized anxiety disorder (GAD) among Saudi medical students. It also illustrates the association of MD with both GAD and the students’ grade point average (GPA). MD was assessed by using the 14-item Maladaptive Daydreaming Scale (MDS), and GAD was assessed by using both the 7-item Generalized Anxiety Disorder (GAD-7) questionnaire and the Penn State Worry Questionnaire (PSWQ). Our data estimate the prevalence of MD among the students to be 70%, GAD prevalence to be 80%, and 55% when using both GAD-7 and PSWQ, respectively. A statistically significant decline in the GPA was apparent among maladaptive daydreamers (MDers). A statistically significant rise in the scores of GAD-7 and PSWQ among MDers compared to non-MDers was found. To sum up, our findings demonstrate a high number of MDers and GAD sufferers among our subjects, and it appears that MD is associated with both GAD and GPA.
Glucose-dependent insulinotropic polypeptide (GIP) activation of protein kinase B (PKB) and its contribution to pancreatic B-cell survival

In addition to their well-documented insulinotropic actions, the incretin hormones Glucose dependent insulinotropic polypeptide (GIP) and Glucagon Like Peptide-1 (GLP1), have recently been shown to exhibit non-insulinotropic effects on pancreatic (3-cell proliferation, survival and apoptosis. GIP regulates pancreatic (3-ceIl secretion by binding to its cognate Family B, G protein-coupled receptor and elevating intracellular cAMP and Ca 2 . The hypothesis for the studies described in this Thesis was that GIP can induce pancreatic (3-cell survival through activation of Protein Kinase B (PKB). Using a (3-ceIl line, INS-1, and in human islets, it was possible to correlate GIP receptor activation with PKB phosphorylation through lipid signaling (arachidonic acid production). Through molecular biological and pharmacological approaches, GIP was shown to activate PKB. It was also shown that GIP can activate known downstream targets of PKB, namely GSK3a/|3, FKHR and BAD. These events were demonstrated to be functionally relevant for (3-cell survival. Studies using the INS-1 cells also displayed the ability of GIP to improve cell survival within a glucolipotoxic environment. GIP treatment reduced the activity of the pro-apoptotic enzyme caspase-3 in both INS-1 cells and human islets. This reduction was shown to be dependent on the PI3K pathway. Further studies verified the protective effect of active PKB for INS-1 cells. Using an animal model of obesity-related diabetes (fa/fa Zucker rat) and a combination of techniques (oral glucose tolerance testing, caspase-3 activity assay, immunoblotting) it was also shown that long-term DP IV (P32/98) treatment can improve the survival of islets and improve glucose tolerance. These findings display a novel role for GIP in regulating (3-cell survival.
An Empirical Study on Unsupervised Network Anomaly Detection using Generative Adversarial Networks

Network anomalies can arise due to various causes such as abnormal behaviors from users, malfunctioning network devices, malicious activities performed by attackers, malicious software or botnets. With the emergence of machine learning and especially deep learning, many works in the literature developed learning models that are able to detect network anomalies. However, these models require massive amounts of labeled data for model training and may not be able to detect unknown anomalous traffic or zero-day attacks. Unsupervised learning techniques such as autoencoder and its variants do not require labeled data but their performance is still poor. Generative adversarial networks (GANs) have successfully demonstrated their capability of implicitly learning data distributions of arbitrarily complex dimensions. This motivates us to carry out an empirical study on the capability of GANs in network anomaly detection. We adopt two existing GAN models and develop new neural networks for their components, i.e., generator and discriminator. We carry out extensive experiments to evaluate the performance of GANs and compare with existing unsupervised detection techniques. We use multiple datasets that include both realistic traffic captures (PCAP) and synthetic traffic generated by simulation platforms. We develop a traffic aggregation technique to extract statistical features that are useful for the models to learn traffic behaviors. The experimental results show that GANs outperform the existing techniques with a significant improvement in different performance metrics.