Miracle-dz/UniFAR-FTUs huggingface dataset:
Cosmology with a new f(R) gravity model in Palatini formalism

One of the most favorable extensions of General Relativity is the [Formula: see text] gravity. [Formula: see text] gravity in Palatini formalism can be a realistic alternative to the dark energy problem. In this work, we study a recently introduced dark energy [Formula: see text] gravity model along with two other models in cosmological perspectives under the Palatini formalism. First, we study the cosmic expansion history of these models with the help of the important cosmographic parameters, such as the Hubble parameter, luminosity distance, effective equation of state, etc. This study shows that the new model behaves similarly with the other two models as well as with the [Formula: see text]CDM model in some respects in the early or very early phases of the universe. It could predict the present accelerated expansion of the universe somewhat differently from the other models with a peculiar future history of the universe. Within a constrained range of parameters all models show a good agreement with the Union2.1 luminosity distance data. However, the new model shows a quite satisfactory agreement in the whole range of its allowed parameters than that of the other two models. We also obtain cosmological constraints on these models from the observed Hubble data. Further, models have been tested by using [Formula: see text] test and statefinder diagnostics. These diagnostics especially the statefinder diagnostic shows that the evolutionary differences between these models are distinct. The evolutionary trajectories of the new model are completely different from the other two models we have considered.
Who is Better at Anticipating Traffic Crashes, Human or Artificial Intelligence? A Gaze Data-based Exploratory Study

Enhancing roadway safety is a priority of transportation. Hence, Artiﬁcial Intelligence (AI)-powered crash anticipation is receiving growing attention, which aims to assist drivers and Automated Driving Systems (ADSs) in avoiding crashes. To gain the trust from ADS users, it is essential to benchmark the performance of AI models against humans. This paper establishes a gaze data-based method with the measures and metrics for evaluating human drivers’ ability to anticipate crashes. A laboratory experiment is designed and performed, wherein a screen-based eye tracker collects the gaze data of six volunteers when they are watching 100 videos that include both normal and risky driving scenes. Statistical analyses (at the 0.05 signiﬁcance level) of experimental data show that on average drivers can anticipate a crash up to 2.61 ± 0.100 seconds before it occurs. The chance whereby drivers have anticipated crashes before they occur, termed humans’ recall, is 0.928 ± 0.0206. An AI model achieving the same recall value can anticipate crashes 2.22 seconds earlier than drivers on average, and the anticipation precision is 0.959. The study ﬁnds that crash involved traﬃc agents in driving scenes can vary drivers’ instant attention level, average attention level, and spatial attention distribution. This ﬁnding supports AI models that learn a dynamic spatial-temporal attention mechanism for strengthening their ability to anticipate crashes. Results from the comparison suggests beneﬁts
Is similarity-based interference caused by lossy compression or cue-based retrieval? A computational evaluation

The similarity-based interference paradigm has been widely used to investigate the factors subserving subject-verb agreement processing. A consistent finding is facilitatory interference effects in ungrammatical sentences but inconclusive results in grammatical sentences. Existing models propose that interference is caused either by misrepresentation of the input (representation distortion-based models) or by mis-retrieval of the interfering noun phrase based on cues at the verb (retrieval-based models). These models fail to fully capture the observed interference patterns in the experimental data. We implement two new models under the assumption that a comprehender utilizes a lossy memory representation of the intended message when processing subject-verb agreement dependencies. Our models outperform the existing cue-based retrieval model in capturing the observed patterns in the data for both grammatical and ungrammatical sentences. Lossy compression models under different constraints can be useful in understanding the role of representation distortion in sentence comprehension.
Linking Sap Flow Measurements with Earth Observations

While single-tree transpiration is challenging to compare with earth observation, canopy scale data are suitable for this purpose. To test the potentialities of the second approach, we equipped the trees at two measurement sites with sap flow sensors in spruce forests. The sites have contrasting topography. The measurement period covered the months between June 2020 and January 2021. To link plot scale transpiration with earth observations, we utilized Sentinel-2 and local meteorological data. Within a machine learning framework, we have tested the suitability of earth observations for modelling canopy transpiration. The R2 of the cross-validated trained models at the measurement sites was between 0.57 and 0.80. These results demonstrate the relevance of Sentinel-2 data for the data-driven upscaling of ecosystem fluxes from plot scale sap flow data. If applied to a broader network of sites and climatic conditions, such an approach could offer unprecedented possibilities for investigating our forests' resilience and resistance capacity to an intensified hydrological cycle in the contest of a changing climate.
Computing the Newton-step faster than Hessian accumulation

Computing the Newton-step of a generic function with $N$ decision variables takes $O(N^3)$ flops. In this paper, we show that given the computational graph of the function, this bound can be reduced to $O(m\tau^3)$, where $\tau, m$ are the width and size of a tree-decomposition of the graph. The proposed algorithm generalizes nonlinear optimal-control methods based on LQR to general optimization problems and provides non-trivial gains in iteration-complexity even in cases where the Hessian is dense.
Search for Galactic Civilizations Using Historical Supernovae

We study an interstellar signaling scheme that was originally proposed by Seto and efficiently links intentional transmitters to extraterrestrial intelligence (ETI) searchers through a conspicuous astronomical burst, without prior communication. Based on the geometrical and game theoretic viewpoints, the scheme can be refined so that intentional signals can be sent and received after observing a reference burst, in contrast to the original proposal (before observing a burst). Given this inverted temporal structure, Galactic supernovae recorded in the past 2000 yr can be regarded as interesting guideposts for an ETI search. While the best use period of SN 393 has presumably passed ∼100 yr ago, some of the historical supernovae might allow us to compactify the ETI survey regions down to less than one present of 4π, around two rings in the sky.
Cross-correlation of Dark Energy Survey Year 3 lensing data with ACT and 
Planck
 thermal Sunyaev-Zel’dovich effect observations. II. Modeling and constraints on halo pressure profiles

Hot, ionized gas leaves an imprint on the cosmic microwave background via the thermal Sunyaev Zel'dovich (tSZ) effect. The cross-correlation of gravitational lensing (which traces the projected mass) with the tSZ effect (which traces the projected gas pressure) is a powerful probe of the thermal state of ionized baryons throughout the Universe, and is sensitive to effects such as baryonic feedback. In a companion paper (Gatti et al. 2021), we present tomographic measurements and validation tests of the cross-correlation between galaxy shear measurements from the first three years of observations of the Dark Energy Survey, and tSZ measurements from a combination of Atacama Cosmology Telescope and ${\it Planck}$ observations. In this work, we use the same measurements to constrain models for the pressure profiles of halos across a wide range of halo mass and redshift. We find evidence for reduced pressure in low mass halos, consistent with predictions for the effects of feedback from active galactic nuclei. We infer the hydrostatic mass bias ($B \equiv M_{500c}/M_{\rm SZ}$) from our measurements, finding $B = 1.8\pm0.1$ when adopting the ${\it Planck}$-preferred cosmological parameters. We additionally find that our measurements are consistent with a non-zero redshift evolution of $B$, with the correct sign and sufficient magnitude to explain the mass bias necessary to reconcile cluster count measurements with the ${\it Planck}$-preferred cosmology. Our analysis introduces a model for the impact of intrinsic alignments (IA) of galaxy shapes on the shear-tSZ correlation. We show that IA can have a significant impact on these correlations at current noise levels.
Is Disentanglement enough? On Latent Representations for Controllable Music Generation

Improving controllability or the ability to manipulate one or more attributes of the generated data has become a topic of interest in the context of deep generative models of music. Recent attempts in this direction have relied on learning disentangled representations from data such that the underlying factors of variation are well separated. In this paper, we focus on the relationship between disentanglement and controllability by conducting a systematic study using different supervised disentanglement learning algorithms based on the Variational Auto-Encoder (VAE) architecture. Our experiments show that a high degree of disentanglement can be achieved by using different forms of supervision to train a strong discriminative encoder. However, in the absence of a strong generative decoder, disentanglement does not necessarily imply controllability. The structure of the latent space with respect to the VAE-decoder plays an important role in boosting the ability of a generative model to manipulate different attributes. To this end, we also propose methods and metrics to help evaluate the quality of a latent space with respect to the afforded degree of controllability.
Correcting Arabic Soft Spelling Mistakes using BiLSTM-based Machine Learning

Soft spelling errors are a class of spelling mistakes that is widespread among native Arabic speakers and foreign learners alike. Some of these errors are typographical in nature. They occur due to orthographic variations of some Arabic letters and the complex rules that dictate their correct usage. Many people forgo these rules, and given the identical phonetic sounds, they often confuse such letters. In this paper, we propose a bidirectional long short-term memory network that corrects this class of errors. We develop, train, evaluate, and compare a set of BiLSTM networks. We approach the spelling correction problem at the character level. We handle Arabic texts from both classical and modern standard Arabic. We treat the problem as a one-to-one sequence transcription problem. Since the soft Arabic errors class encompasses omission and addition mistakes, to preserve the one-to-one sequence transcription, we propose a simple low-resource yet effective technique that maintains the one-to-one sequencing and avoids using a costly encoder-decoder architecture. We train the BiLSTM models to correct the spelling mistakes using transformed input and stochastic error injection approaches. We recommend a configuration that has two BiLSTM layers, uses the dropout regularization, and is trained using the latter training approach with error injection rate of 40%. The best model corrects 96.4% of the injected errors and achieves a low character error rate of 1.28% on a real test set of soft spelling mistakes.
Solving Fashion Recommendation - The Farfetch Challenge

Recommendation engines are integral to the modern e-commerce experience, both for the seller and the end user. Accurate recommendations lead to higher revenue and better user experience. In this paper, we are presenting our solution to ECML PKDD Farfetch Fashion Recommendation Challenge [7]. The goal of this challenge is to maximize the chances of a click when the users are presented with set of fashion items. We have approached this problem as a binary classification problem. Our winning solution utilizes Catboost as the classifier and Bayesian Optimization for hyper parameter tuning. Our baseline model achieved MRR of 0.5153 on the validation set. Bayesian optimization of hyper parameters improved the MRR to 0.5240 on the validation set. Our final submission on the test set achieved a MRR of 0.5257.
Dynamic communication topologies for distributed heuristics in energy system optimization algorithms

The communication topology is an essential aspect in designing distributed optimization heuristics. It can influence the exploration and exploitation of the search space and thus the optimization performance in terms of solution quality, convergence speed and collaboration costs – relevant aspects for applications operating critical infrastructure in energy systems. In this work, we present an approach for adapting the communication topology during runtime, based on the principles of simulated annealing. We compare the approach to common static topologies regarding the performance of an exemplary distributed optimization heuristic. Finally, we investigate the correlations between fitness landscape properties and defined performance metrics.
Sexing white 2D footprints using convolutional neural networks

Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the density of friction ridges. Here we explore the relative importance of different components in sexing two-dimensional foot impressions namely, size, shape and texture. We use a machine learning approach and compare this to more traditional methods of discrimination. Two datasets are used, a pilot data set collected from students at Bournemouth University (N = 196) and a larger data set collected by podiatrists at Sheffield NHS Teaching Hospital (N = 2677). Our convolutional neural network can sex a footprint with accuracy of around 90% on a test set of N = 267 footprint images using all image components, which is better than an expert can achieve. However, the quality of the impressions impacts on this success rate, but the results are promising and in time it may be possible to create an automated screening algorithm in which practitioners of whatever sort (medical or forensic) can obtain a first order sexing of a two-dimensional footprint.
Energetics of quantum vacuum friction: Field fluctuations

Quantum fluctuations can induce a friction on a neutral but polarizable particle and cause it to radiate energy even if the particle is moving in free space filled with blackbody radiation, and is not in contact with or close to any surface or other object. We explore the energetics of such a particle moving uniformly in vacuum, continuing our previous investigations of quantum friction. The intrinsic polarizability of the particle is considered to be purely real before it is dressed by radiation. The particle is then guaranteed to be in the nonequilibrium steady state (NESS), where it absorbs and emits energy at the same rate. We first calculate the quantum frictional power and force on the particle in the rest frame of the blackbody radiation from first principles, namely the Maxwell-Heaviside equations and the Lorentz force law. Then we provide a simpler method of obtaining the same quantities in the rest frame of the particle by using the principle of virtual work. The equivalence of the two approaches is illustrated. The formulas we derive for quantum vacuum frictional power and force are fully relativistic and applicable to finite temperature. In NESS, the quantum vacuum frictional force on the particle is shown to be a true drag, independent of the model for polarizability and the polarization state of the particle. Finally, we give an estimate of the quantum vacuum friction on a gold atom and comment on the feasibility of detecting such quantum vacuum frictional effects.
Prospects for Detecting Exoplanets around Double White Dwarfs with LISA and Taiji

Recently, Tamanini & Danielski discussed the possibility of detecting circumbinary exoplanets (CBPs) orbiting double white dwarfs (DWDs) with the Laser Interferometer Space Antenna (LISA). Extending their methods and criteria, we discuss the prospects for detecting exoplanets around DWDs not only by LISA, but also by Taiji, a Chinese space-borne gravitational-wave (GW) mission that has slightly better sensitivity at low frequencies. We first explore how different binary masses and mass ratios affect the abilities of LISA and Taiji to detect CBPs. Second, for certain known detached DWDs with high signal-to-noise ratios, we quantify the possibility of CBP detections around them. Third, based on the DWD population obtained from the Mock LISA Data Challenge, we present basic assessments of the CBP detections in our Galaxy during a 4 yr mission time for LISA and Taiji. We discuss the constraints on the detectable zone of each system, as well as the distributions of the inner/outer edge of the detectable zone. With the DWD population, we further inject two different planet distributions with an occurrence rate of 50% and constrain the total detection rates. We briefly discuss the prospects for detecting habitable CBPs around DWDs with a simplified model. These results can provide helpful inputs for upcoming exoplanetary projects and help analyze planetary systems after the common envelope phase.
Magneto‐Optical Sensing Beyond the Shot Noise Limit

Magneto‐optical sensors including spin noise spectroscopies and magneto‐optical Kerr effect microscopies are now ubiquitous tools for materials characterization that can provide new understanding of spin dynamics, hyperfine interactions, spin‐orbit interactions, and charge‐carrier g‐factors. Both interferometric and intensity‐difference measurements can provide photon‐shot‐noise‐limited sensitivity, but further improvements in sensitivity with classical resources require either increased laser power that can induce unwanted heating and electronic perturbations or increased measurement times that can obscure out‐of‐equilibrium dynamics and slow experimental throughput. Proof‐of‐principle measurements have already demonstrated quantum enhanced spin noise measurements with a squeezed readout field that are likely to be critical to the nonperturbative characterization of spin excitations in quantum materials that emerge at low temperatures. Here, a truncated nonlinear interferometric readout for low‐temperature magneto‐optical Kerr effect and related magneto‐optical microscopies that is accessible with today's quantum optical resources is proposed. 10 nrad/Hz sensitivity is achievable with optical power as small as 1 µW. As a result, measurements may be performed at temperatures as low as 83 mK in commercially available dilution refrigerators. This combination of high sensitivity and low operating temperature is impossible to achieve with any classical measurement. The quantum advantage for the proposed measurements persists even in the limit of large loss and small squeezing parameters.
Impact of Load Demand Dataset Characteristics on Clustering Validation Indices

With the inclusion of smart meters, electricity load consumption data can be fetched for individual consumer buildings at high temporal resolutions. Availability of such data has made it possible to study daily load demand profiles of the households. Clustering households based on their demand profiles is one of the primary, yet a key component of such analysis. While many clustering algorithms/frameworks can be deployed to perform clustering, they usually generate very different clusters. In order to identify the best clustering results, various cluster validation indices (CVIs) have been proposed in the literature. However, it has been noticed that different CVIs often recommend different algorithms. This leads to the problem of identifying the most suitable CVI for a given dataset. Responding to the problem, this paper shows how the recommendations of validation indices are influenced by different data characteristics that might be present in a typical residential load demand dataset. Furthermore, the paper identifies the features of data that prefer/prohibit the use of a particular cluster validation index.
Comparative study of magnetic properties of Mn$^{3+}$ magnetic clusters in GaN using classical and quantum mechanical approach

Currently, simulations of many-body quantum systems are known to be computationally too demanding to be solved on classical computers. The main problem is that the computation time and memory necessary for performing the calculations usually grow exponentially with the number of particles N . An efficient approach to simulate many-body quantum systems is the use of classical approximation. However, it is known that at least at low temperatures, the allowed spin fluctuations in this approach are overestimated what results in enhanced thermal fluctuations. It is therefore timely and important to assess the validity of the classical approximation. To this end, in this work, we compare the results of numerical calculations of small Mn magnetic clusters in GaN, where the Mn spins are treated classically with those where they are treated quantum-mechanically (crystal field model). In the first case, we solve the Landau-Lifshitz-Gilbert (LLG) equation that describes the precessional dynamics of spins represented by classical vectors. On the other hand, in the crystal field model, the state of Mn ion (d configuration with S = 2, L = 2) is characterized by the set of orbital and spin quantum numbers |ms,mL >. Particular attention is paid to use numerical parameters that ensure the same single ion magnetic anisotropy in both classical and quantum approximation. Finally, a detailed comparative study of magnetization M(H, T ) as a function of the magnetic field H, temperature T , number of ions in a given cluster N and the strength of super-exchange interaction J , obtained from both approaches will be presented.
An Entropic Approach to Classical Density Functional Theory

The classical Density Functional Theory (DFT) is introduced as an application of entropic inference for inhomogeneous fluids in thermal equilibrium. It is shown that entropic inference reproduces the variational principle of DFT when information about the expected density of particles is imposed. This process introduces a family of trial density-parametrized probability distributions and, consequently, a trial entropy from which the preferred one is found using the method of Maximum Entropy (MaxEnt). As an application, the DFT model for slowly varying density is provided, and its approximation scheme is discussed.
SENTIMENT ANALYSIS FOR USER REVIEWS USING K-NN ALGORITHM

: Sentiment analysis, also called the Opinion Mining is a type of Natural Language Processing (NLP) in which the people’s opinions, sentiments, emotions, attitudes etc., are extracted from the text based on the polarity. i. e, the positivity or negativity in the text. It is an active research area in the field of study of NLP and the data mining. It has grown widely due to its importance to business and society. The Opinion Mining / Sentiment Analysis is used widely in the field of social media and other reviewing systems. In this paper, by using the K-Nearest Neighbour algorithm, the sentiment analysis is used for reviewing a system.