Miracle-dz/UniFAR-FTUs huggingface dataset:
Connecting the Cosmic Star Formation Rate with the Local Star Formation

We present a model that unifies the cosmic star formation rate (CSFR), obtained through the hierarchical structure formation scenario, with the (Galactic) local star formation rate (SFR). It is possible to use the SFR to generate a CSFR mapping through the density probability distribution functions commonly used to study the role of turbulence in the star-forming regions of the Galaxy. We obtain a consistent mapping from redshift z ∼ 20 up to the present (z = 0). Our results show that the turbulence exhibits a dual character, providing high values for the star formation efficiency ( 〈 ε 〉 ∼ 0.32 ) in the redshift interval z ∼ 3.5 – 20 and reducing its value to 〈 ε 〉 = 0.021 at z = 0. The value of the Mach number (  crit ), from which 〈 ε 〉 rapidly decreases, is dependent on both the polytropic index (Γ) and the minimum density contrast of the gas. We also derive Larson’s first law associated with the velocity dispersion ( 〈 V rms 〉 ) in the local star formation regions. Our model shows good agreement with Larson’s law in the ∼ 10 – 50 pc range, providing typical temperatures T 0 ∼ 10 – 80 K for the gas associated with star formation. As a consequence, dark matter halos of great mass could contain a number of halos of much smaller mass, and be able to form structures similar to globular clusters. Thus, Larson’s law emerges as a result of the very formation of large-scale structures, which in turn would allow the formation of galactic systems, including our Galaxy.
Self-duality, helicity conservation, and normal ordering in nonlinear QED

We give a proof of the equivalence of the electric-magnetic duality on one side and helicity conservation of the tree level amplitudes on the other side within general models of nonlinear electrodynamics. Using modified Feynman rules derived from generalized normal ordered Lagrangian we discuss the interrelation of the above two properties of the theory also at higher loops. As an illustration we present two explicit examples, namely we find the generalized normal ordered Lagrangian for the Born-Infeld theory and derive a semi-closed expression for the Lagrangian of the Bossard-Nicolai model (in terms of the weak field expansion with explicitly known coefficients) from its normal ordered form.
Measurement of Soil Dielectric Permittivity Spectra at Various Temperatures

Dielectric sensors are a popular choice for soil moisture measurement. However, soil dielectric permittivity depends not only on moisture content but also on several other properties, including temperature. The aim of this research is to examine the impact of temperature on soil complex dielectric permittivity spectra in the 0.02-3 GHz frequency range. Soil samples were measured in coaxial cells connected to a vector-network-analyzer. The results indicated that the influence of temperature on dielectric permittivity depended on frequency and moisture content.
Constraints on quintessence scalar field models using cosmological observations

Scalar field models play an essential role in describing various phases of the universe evolution. A survey of literature reports that various potentials have been studied in the past couple of years. Now, with successive developments in the observational cosmology, it becomes essential to test their viabilities with the universe evolution. Thus, being motivated, here we consider a varieties of quintessence scalar field models in a homogeneous and isotropic geometry of the universe with zero spatial curvature aiming to provide stringent constraints using a series of astronomical data sets, namely, the cosmic microwave background (CMB) observations, baryon acoustic oscillations (BAO), redshift space distortions (RSD), joint light curve analysis (JLA) from Supernovae Type Ia and the Hubble parameter measurements from the cosmic chronometers (CC). From the qualitative evolution of the models, we find all of them are able to execute a fine transition from the past decelerating phase to the presently accelerating expansion where in addition, the equation of state of the scalar field (also the effective equation of state) might be close to that of the $Λ$CDM cosmology depending on its free parameters. From the statistical analysis, we find that within 68\% CL, the observational data always allow a constant potential. We also find that for all the models a strong negative correlation between the parameters ($H_0$, $Ω_{m0}$) exists, while the parameters ($H_0$, $σ_8$) are not correlated. We also comment that the present models are unable to reconcile the tension on $H_0$. Finally, we conclude our work with the Bayesian analyses which report that the non-interacting $Λ$CDM model is preferred over all the quintessence scalar field models.
Datamime: Generating Representative Benchmarks by Automatically Synthesizing Datasets

Benchmarks that closely match the behavior of production workloads are crucial to design and provision computer systems. However, current approaches fall short: First, open-source benchmarks use public datasets that cause different behavior from production workloads. Second, blackbox workload cloning techniques generate synthetic code that imitates the target workload, but the resulting program fails to capture most workload characteristics, such as microarchitectural bottlenecks or time-varying behavior.Generating code that mimics a complex application is an extremely hard problem. Instead, we propose a different and easier approach to benchmark synthesis. Our key insight is that, for many production workloads, the program is publicly available or there is a reasonably similar open-source program. In this case, generating the right dataset is sufficient to produce an accurate benchmark.Based on this observation, we present Datamime, a profile-guided approach to generate representative benchmarks for production workloads. Datamime uses the performance profiles of a target workload to generate a dataset that, when used by a benchmark program, behaves very similarly to the target workload in terms of its microarchitectural characteristics.We evaluate Datamime on several datacenter workloads. Datamime generates synthetic benchmarks that closely match the microarchitectural features of these workloads, with a mean absolute percentage error of 3.2% on IPC. Microarchitectural behavior stays close across processor types. Finally, time-varying behaviors are also replicated, making these benchmarks useful to e.g. characterize and optimize tail latency.
A Modified BP Bit-Flipping Algorithm for Polar Codes

The belief propagation (BP) algorithm for polar codes has attracted significant attention due to its soft-output property and high parallelism. However, the BP algorithm is not comparable with the successive cancellation list (SCL) algorithm in terms of the error-correction performance. In this paper, we propose a high-stage BP flip (HSBPF) algorithm to further improve the error-correction performance of the BP algorithm by analyzing the distribution of log-likelihood ratio (LLR) values. Simulation results show that the decoding latency can be reduced by 30% with the proposed algorithm. Moreover, benefiting from the simplified message passing of the factor graph, the computational complexity of the proposed algorithm is also significantly reduced compared with the existing algorithms.
The antiferromagnetic and phonon-mediated model of the NaFeAs, LiFeAs and FeSe superconductors

Recently it has been suggested that the role of electron-phonon coupling in the mechanism of iron-based superconductors may have been underestimated and that the antiferromagnetism and the induced xy potential may even have a dramatic amplification effect on electron-phonon coupling. To substantiate the recently announced xy potential in the literature, we create a two-channel model to separately superimpose the dynamics of the electron in the upper and lower tetrahedral plane. The results of our two-channel model support the literature data. While the scientists are still searching for a universal DFT functional that can describe the pairing mechanism of all iron-based superconductors, we are designing an empirical combination of DFT functional to calculate the electron-phonon coupling and antiferromagetism of LiFeAs, NaFeAs and FeSe. We use ARPES data to correct the electron concentration in superconducting state to ensure that all electrons involved in iron-based superconductivity are included in the ab-inito calculation. We present an ab-initio theoretical approach that takes into account this amplifying effect of antiferromagnetism and the correction of the superconducting electron concentration together with the abnormal soft out-of-plane lattice vibration of the layered structure, which allows us to calculate theoretical Tc values of LiFeAs, NaFeAs and FeSe as a function of pressure that correspond reasonably well to the experimental values.
Impact of Generative Modeling for Fundus Image Augmentation With Improved and Degraded Quality in the Classification of Glaucoma

Glaucoma is a heterogeneous group of diseases characterised by cupping of the optic nerve head and visual field damage, starting with a progressive loss of vision that leads to permanent blindness. When diagnosed in time, Glaucoma can be delayed by adequate treatment. More efficient processes for diagnosis are being proposed, and the role of artificial intelligence in the field is growing. This work presents a pipeline to evaluate the impact of generative modelling in Computer-Aided Diagnosis (CADx) of Glaucoma based on Deep Learning, particularly focused on the optic disc region. The methodology relies on transforming retinal fundus images to improve and degrade their quality to augment the training data and assess the diagnostic performance. The objective evaluation of the proposed model based on Generative Adversarial Networks revealed quantitative and qualitative improvements in image quality. To support this, we propose a new model to evaluate the quality of fundus images, which can also be used within the pipeline to reject samples with lower image quality for diagnosis. Its performance surpassed related work, achieving a balanced accuracy of 0.929. Concerning Glaucoma CADx, the results obtained in public datasets point to a considerable gain in Sensitivity, Specificity, and Accuracy, achieving scores of 0.883 (+0.054), 0.957 (+0.019), and 0.931 (+0.031), respectively, after image data augmentation when compared with previous work targeted at offline inference in mobile devices. Considering the restriction of choosing simpler backbone networks that can run on edge devices, our findings support the importance of image quality diversity and realistic augmentation.
The outer halo globular cluster system of M31 – III. Relationship to the stellar halo

We utilize the final catalogue from the Pan-Andromeda Archaeological Survey to investigate the links between the globular cluster system and field halo in M31 at projected radii Rproj = 25-150 kpc. In this region the cluster radial density profile exhibits a power-law decline with index Γ = -2.37 ± 0.17, matching that for the stellar halo component with [Fe/H] < -1.1. Spatial density maps reveal a striking correspondence between the most luminous substructures in the metal-poor field halo and the positions of many globular clusters. By comparing the density of metal-poor halo stars local to each cluster with the azimuthal distribution at commensurate radius, we reject the possibility of no correlation between clusters and field overdensities at 99.95{{ per cent}} significance. We use our stellar density measurements and previous kinematic data to demonstrate that ≈ 35-60 per cent of clusters exhibit properties consistent with having been accreted into the outskirts of M31 at late times with their parent dwarfs. Conversely, at least { }40{{ per cent}} of remote clusters show no evidence for a link with halo substructure. The radial density profile for this subgroup is featureless and closely mirrors that observed for the apparently smooth component of the metal-poor stellar halo. We speculate that these clusters are associated with the smooth halo; if so, their properties appear consistent with a scenario where the smooth halo was built up at early times via the destruction of primitive satellites. In this picture the entire M31 globular cluster system outside Rproj = 25 kpc comprises objects accumulated from external galaxies over a Hubble time of growth.
Explore-Exploit Tradeoffs Generate Cascading Societal Stereotypes

Mental representations of human social groups – social stereotypes – are widespread and consequential. Such mental representations are systematic and multidimensional (e.g., stereotypes of immigrant groups are organized by perceived warmth and competence). We show that adaptive exploration alone can create structured societal stereotypes that cascade from historical affordances – such as which group happened to be the first one adequate at a job – without requiring decision-makers to have malicious intentions or cognitive limitations, or social groups to differ in information accessibility or intrinsic quality. Rather than framing social perception as a static, one-shot event, we consider the consequences of sequential decisions in a setting where exploring new options carries an implicit cost (resulting in an “explore-exploit tradeoff”). In this setting, when groups have equal and high potential to succeed in diverse jobs, decision-makers nonetheless settle on a single social group to perform each job and form impressions of that group accordingly. Using stereotypes of immigrant groups based on warmth and competence as an example, we formalize this process as a contextual multi-armed bandit problem, show testable predictions from computational simulations, and demonstrate that human participants act consistently with these predictions in behavioral experiments. Our results show how rich, multidimensional stereotypes can emerge in an absolutely minimal setting.
Co-Cache: Inertial-Driven Infrastructure-less Collaborative Approximate Caching

Many emerging multimedia mobile applications rely heavily upon image recognition of both static images and live video streams. Image recognition is commonly achieved using deep neural networks (DNNs) which can achieve high accuracy but also incur significant computation latency and energy con-sumption on resource-constrained smartphones. Recent efforts addressing these issues include cloud offloading and reducing the complexity of the DNNs, which, however, introduce increased network latency or reduced accuracy. In-memory caching has also been explored to assess the similarity of images as opposed to exact matching. However, such approximate caching systems often treat devices as static nodes, and do not fully utilize the mobile and collaborative nature of smartphones without outside infrastructure. Another consequence of treating nodes as static is the necessity of cache sizes larger than what is feasible for individual mobile applications. In this paper we introduce Co-Cache, a in-memory caching paradigm that supports infrastructure-less collaborative compu-tation reuse in smartphone image recognition. Co-Cache utilizes the inertial movement of smartphones, the locality inherent in video streams, as well as information from nearby, peer-to-peer devices to maximize the computation reuse opportunities in mobile image recognition. Compared to other caching systems, our extensive evaluation shows that Co-Cache can reduce the required number of cache entries by 50–70 % while lowering the average latency of standard image recognition applications by up to 94 % with minimal loss of recognition accuracy.
MIDAS: Safeguarding IoT Devices Against Malware via Real-Time Behavior Auditing

The number of IoT devices on the Internet has surged recently, accompanied by a barrage of large-scale IoT malware infections breakouts. Designing security mechanisms for IoT devices poses significant challenges due to constantly changing malware variants that have numerous camouflage strategies, limited hardware resources, and heterogeneous architectures. In this article, we propose MIDAS, an adaptive safeguard framework for Linux-based IoT devices to defend against malwares with the real-time behavior auditing mechanism. First, we construct a stable and abstract behavior paradigm through behavioral characteristic extraction of 115 970 malwares. Then, based on the behavior paradigm, MIDAS can: 1) monitor suspicious behaviors of break-in programs in real-time driven by our built-in SELinux policy customized for malware defense; 2) aggregate behaviors of the program’s submodules with homology tracing; and 3) summarize these behaviors into abstract behavior pairs to unveil a possible IoT malware. Using the aforementioned real-time behavior auditing, MIDAS can constrain mutating and camouflaged malwares to protect discrepant IoT devices from being compromised while maintaining low overheads. We thoroughly evaluated the defense capabilities of MIDAS. On the benchmark dataset, MIDAS successfully constrained up to 94.46%, 91.79%, and 88.34% of 115970 malware samples on ARM, MIPS, and MIPSEL architectures, with less than 1.8 MiB of memory consumption and 0.54% CPU usage. Furthermore, we deployed virtual IoT devices worldwide to examine the performance of MIDAS when defending against real-world attacks. Over a duration of 25 days, these devices suffered from 971 951 attacks originating from 71 979 intruding malwares and 48 805 unique IPs distributed in 167 countries. For devices with MIDAS protection, the number of compromised incidents decreases by $343.1\times $ , and the duration of continuous operation is $179.2\times $ greater than devices without MIDAS on average. The evaluation results demonstrate that MIDAS can effectively safeguard IoT devices with minimal resource consumption.
An Improved Indicator of Asymmetry in the Coronal Body Plane

Objectives. The research aimed to increase validity of the indicators of asymmetry in the coronal body plane concerning a scoliosis about first grade pupils. Methods. Thirty-four pupils of six-seven years old (eighteen boys and sixteen girls) were randomly assigned in equal shares into experimental and control groups. Besides the standard physical education lessons, pupils of the experimental group had physical rehabilitation program specifically designed to prevent and reduce the asymmetry of a posture. From the very beginning and just before ending of the academic year, pupils were tested on the asymmetry of a spine column in the coronal plane. Results. The improved indicator was determined as a half sum of a magnitude of differences of the upper sides of Moshkov rhombus and a magnitude of difference of the lower sides. Conclusions. The indicator of a body asymmetry in the coronal plane, proposed in this work, which took into account the shape of the whole spine, made it possible to achieve consequently stronger significance (p=0.02) compared with a generally accepted indicator of the curvature of the neck area of the spine (p=0.05), which indicated significant improvement of the test validity.
Endoscopic eradication therapy for Barrett ’ s esophagus – related neoplasia: a ﬁ nal 10-year report from the UK National HALO Radiofrequency Ablation Registry

GRAPHICAL ABSTRACT Background and Aims: Long-term durability data for effectiveness of radiofrequency ablation (RFA) to prevent esophageal adenocarcinoma in patients with dysplastic Barrett ’ s esophagus (BE) are lacking. Methods: We prospectively collected data from 2535patients with BE (mean length, 5.2cm; range, 1-20) and neoplasia (20%low-gradedysplasia,54%high-gradedysplasia,26%intramucosalcarcinoma)whounderwentRFAtherapyacross28UKhospitals.Weassessedratesofinvasivecancerandperformeddetailedanalysesof1175patientstoassessclearanceratesofdysplasia(CR-D)andintestinalmetaplasia(CR-IM)within2yearsofstartingRFAtherapy.WeassessedrelapsesandratesofreturntoCR-D(CR-D2)andCR-IM(CR-IM2)afterfurthertherapy.CR-DandCR-IMwerecon ﬁ rmed by an absence of dysplasia and intestinal metaplasia on biopsy samples taken at 2 consecutive endoscopies. Results: Ten years after starting treatment, the Kaplan-Meier (KM) cancer rate was 4.1% with a crude incidence rate of . 52 per 100 patient-years. CR-D and CR-IM after 2 years of therapy were 88% and 62.6%, respectively. KM relapse rates were 5.9% from CR-D and 18.7% from CR-IM at 8 years, with most occurring in the ﬁ rst 2 years.
Accelerating Adaptive Federated Optimization with Local Gossip Communications

Recently, adaptive federated optimization methods, such as FedAdam and FedAMS-1 Grad, have gained increasing attention for their fast convergence and stable perfor-2 mance especially in training models with heavy-tail stochastic gradient distributions. 3 However, the implementation of such methods still faces several bottlenecks, such 4 as the large client-to-server communication overhead and the intense sensitivity to 5 heterogeneous data. More importantly, the two objectives may conflict with each 6 other, i.e., the convergence rate gets worse as the number of local steps increases 7 in the partial participation setting, making it challenging to further improve the 8 efficiency of adaptive federated optimization. We refer this problem as the dilemma 9 of local steps . In this paper, we propose a novel hybrid adaptive federated optimiza-10 tion method (HA-Fed) where the clients are partitioned into disjoint clusters inside 11 which they can communicate by fast client-to-client links. We show that HA-Fed re-12 solves the dilemma of local steps in prior adaptive federated optimization methods, 13 i.e., achieves a faster convergence rate as the local steps increases, while reducing 14 the client-to-server communication overhead under non-i.i. d. settings. Specifically, 15 HA-Fed improves the convergence rate from O ( √ τ/ √ TM ) in FedAMSGrad to 16 O (1 / √ TτM ) in partial participation scenarios under nonconvex stochastic setting. 17 Extensive experiments and ablation studies demonstrate the effectiveness and broad 18 applicability of our proposed method. 19
Vascularization of hepatic structures in a microfluidic platform

Liver disease is a global public health issue that affects from 4.5% to up to 9.5% people worldwide. The only current definitive therapy for end-stage liver disease is liver transplant however, the demand for organ donors far exceeds their supply. Thus, a pressing need arises for accurate liver models that allow for better understanding of liver behaviour in health and disease. The present work lays the foundation for the development of vascularized liver organoid and liver tumouroid models by creating a microfluidic culture system in which human umbilical vein endothelial cells (hUVECs) and human mesenchymal stromal cells (hMSCs) are co-cultured with liver organoids or liver tumouroids in a three-dimensional microfluidic environment, supported by a fibrin hydrogel based on pig liver extracellular matrix (ECM). Co-culture of hUVECs and hMSCs in the absence of liver structures led to de novo formation of a microvascular network inside the microfluidic device. Angiogenesis was found to be caused by a combination of adequate cell conditioning and culture architecture, as well as by action of the ECM-based hydrogel. Improvements in experimental design are likely to allow for fruitful angiogenic assays in the presence of liver organoids or tumouroids. Furthermore, future applications of this technology include spheroid vascularization, high-throughput screening assays, in vivo modelling of human liver and other therapeutic approaches.