Miracle-dz/UniFAR-FTUs huggingface dataset:
A Role for Prior Knowledge in Statistical Classification of the Transition from MCI to Alzheimer's Disease

The transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of great interest to clinical researchers. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), yielding equivalent or superior classification accuracy over other ML methods. Further, in applications with many features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different selection procedures. In the present study, we sought to investigate the use of automated and theoretically-guided feature selection techniques, and as well as the L-1 norm when applying different classification techniques for predicting conversion from MCI to AD in a highly characterized and studied sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We propose an alternative pre-selection technique that utilizes an efficient feature selection based on clinical knowledge of brain regions involved in AD. The present findings demonstrate how similar performance can be achieved using user-guided pre-selection versus algorithmic feature selection techniques. Finally, we compare the performance of a support vector machine (SVM) with that of logistic regression on multi-modal data from ADNI. The present findings show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM's necessity or value for many clinical researchers.
Mass-gap Mergers in Active Galactic Nuclei

The recently discovered gravitational wave sources GW190521 and GW190814 have shown evidence of BH mergers with masses and spins outside of the range expected from isolated stellar evolution. These merging objects could have undergone previous mergers. Such hierarchical mergers are predicted to be frequent in active galactic nuclei (AGNs) disks, where binaries form and evolve efficiently by dynamical interactions and gaseous dissipation. Here we compare the properties of these observed events to the theoretical models of mergers in AGN disks, which are obtained by performing one-dimensional N-body simulations combined with semi-analytical prescriptions. The high BH masses in GW190521 are consistent with mergers of high-generation (high-g) BHs where the initial progenitor stars had high metallicity, 2g BHs if the original progenitors were metal-poor, or 1g BHs that had gained mass via super-Eddington accretion. Other measured properties related to spin parameters in GW190521 are also consistent with mergers in AGN disks. Furthermore, mergers in the lower mass gap or those with low mass ratio as found in GW190814 and GW190412 are also reproduced by mergers of 2g–1g or 1g–1g objects with significant accretion in AGN disks. Finally, due to gas accretion, the massive neutron star merger reported in GW190425 can be produced in an AGN disk.
Epac-mediated modulation of neurotransmitter release from cultured hippocampal neurons.

cAMP regulates secretory processes through both protein kinase A (PKA)-independent and PKA-dependent signaling pathways. However, their relative contributions to fast neurotransmission are unclear at present, although the cAMP mediated increase in neurotransmitter release could be attributed to an increase of vesicular release probability and to the enhancement of the number of releasable vesicles in various neuronal preparations. The present study analysed the contribution of the novel cAMP receptor Epac to the cAMP related modulation of neurotransmission. Neurotransmitter release from cultured murine or rat hippocampal excitatory autaptic neurons was monitored during low and high frequency stimulation in the presence or absence of Epac-Selective Cyclic AMP Analogs (ESCAs) using the patch-clamp technique. ESCA-induced Epac activation increased both the action potential-evoked excitatory postsynaptic current (EPSC) amplitudes during 0.2 Hz frequency stimulation and the number of spontaneous fusion events (mEPSCs) from autaptic excitatory Dentate Gyrus-neurons. The magnitude of potentiation depended on the average EPSC amplitude recorded during the first two minutes, on the cell culture age, and on the presynaptic calcium ion concentration. ESCA1 (8-pCPT-2 -O-Me-cAMP)-mediated Epac activation accounted on average for 38% of the forskolin-induced increase in evoked EPSC amplitudes and for 100% of the forskolin-induced increase in mEPSC frequency. Forskolin is generally believed to enhance intracellular cAMP levels by stimulation of adenylyl cyclase activity. The mEPSC amplitude and the number of readily releasable vesicles remained unchanged upon brief ESCA1 treatment (1.2 minutes). In conclusion, ESCA1 application increased vesicular release probability. Finally, the data show that ESCA1- and phorbol esters-stimulated signaling pathways interact. The observed coupling of both signaling pathways was dependent on protein kinase C activity.
Inductive biases for deep learning of higher-level cognition

A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behaviour of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help both clarify these principles and provide inspiration for AI research and neuroscience theories. Deep learning already exploits several key inductive biases, and this work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans’ abilities in terms of flexible out-of-distribution and systematic generalization, which is currently an area where a large gap exists between state-of-the-art machine learning and human intelligence.
Modeling of Benzene Adsorption in the Gas Phase on Single-Walled Carbon Nanotubes for Reducing Air Pollution

Air Pollution has always been a major problem in metropolises. Volatile organic compounds are one of the major pollutants that are caused by incomplete combustion of fuels in vehicles and gasoline evaporation, especially in fueling stations. Removing these pollutants through traditional methods has always been considered. The paper investigates and studies chemical adsorption behavior of benzene on single-walled carbon nanotubes (9, 9) and (7, 7) in the gas phase by the Gaussian 09 program and using quantum chemical calculations and density functional theory method (DFT). First, carbon nanotubes were generated by a nanotube modeler, and then benzene was passed through the inside and outside of the nanotubes. After that, the absorption energies were calculated using the B3LYP calculation method and 6-31G basic set at different time intervals. Next, the amount of structure energy for carbon nanotube and benzene was separately calculated by the Gaussian 09 program. Using the existing equations, the absorption energy at different time intervals was obtained as follows: For 0.5, 1, 1.5, and 2 angstrom outside the nanotube (7, 7), 14.25, 11.22, 3.32, and 0.78 electron volts (eV); for the nanotube (9, 9), -776.34, -807.12, -817.16, and -844.62 electron volts (eV); for the inside of the nanotube (7, 7), 14.66, 7.76, 7.30, and 7.27 electron volts (eV); and for the nanotube (9, 9), -813.69, -813.97, -816.68, and -819.33 electron volts (eV), respectively. As the results show, when the diameter of the nanotube increases, the energy absorption decreases. Therefore, the carbon nanotube (7, 7), which hasa smaller diameter than the carbon nanotube (9, 9), will be more effective in absorbing and removing benzene from the air.
mg-mamposst: a code to test modifications of gravity with internal kinematics and lensing analyses of galaxy clusters

We present an upgraded version of \emph{MG-MAMPOSSt}, a code to constrain modified gravity models with the dynamics of galaxy cluster's members, based on the original \emph{MAMPOSSt} method, along with an application to forecasts. Given an input for the velocity field of member galaxies, the code solves the Jeans' equation to provide a likelihood analysis for the free parameters of the gravity theory, that is, the mass profile and modified gravity parameters. The new version implements two distinct types of gravity modifications, namely general chameleon (including $f(R)$ models) and Beyond Horndeski gravity (Vainshtein screening), and is further equipped with a Monte-Carlo-Markov-Chain module for an efficient parameter space exploration. The program is complemented by the ClusterGEN code, capable of producing mock galaxy clusters under the assumption of spherical symmetry and dynamical equilibrium. The code is written in Fortran and we plan to make it publicly available. Its technical aspects will be presented elsewhere. We demonstrate the potential of the method in an ideal situation by analysing a set of synthetic, isolated and spherically-symmetric dark matter halos, focusing on the statistical degeneracies between model parameters. Assuming the availability of additional lensing-like information, we forecast the constraints on the modified gravity parameters for the two classes of models presented from joint lensing+dynamics analyses in view of upcoming galaxy cluster surveys.
Variability in NGC 3201 Giant Stars and Its Impact on Their Spectroscopic [Fe/H] Determination

We present the analysis of 34 light curves in V and I of 17 giant stars in the globular cluster NGC 3201 to check if such stars are variable and if their variability has some kind of impact on the iron abundance as obtained from spectroscopic measurements. First, we computed the generalized Lomb–Scargle and phase dispersion minimization periodograms of the sample to check if the stars were variables. In this way, seven stars of the sample were found to be nonvariable, two stars are considered as possible variables, and eight stars were found to be variable, with periods ranging from 0.0881 ± 0.0001 to 0.5418 ± 0.0027 days. According to the literature, the variables have distinct values of [Fe I/H]: the three most metal-rich stars are in the red giant branch (RGB) stage, one has [Fe I/H] = −1.37 dex, while the other two have [Fe I/H] = −1.31 dex. The two most metal-poor variables have [Fe I/H] = −1.61 dex and [Fe I/H] = −1.62 dex, and are AGB stars; the remaining variables have [Fe I/H] = −1.44, −1.48, and −1.50 dex, the first two being RGB stars while the last is an AGB star. On the other hand, stars that appear to be nonvariable have −1.56 ≤ [Fe I/H] ≤ −1.40. We conclude that variability somehow affects the spectroscopic determination of the iron content of giant stars in NGC 3201, increasing the iron spread of the cluster. If variability is not taken into account, this spread could be incorrectly interpreted as due to an intrinsic iron spread affecting the stars of the cluster.
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics

We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit images and jets of particles in proton-proton collisions like those at the LHC. We find the model successfully generates sparse MNIST digits and particle jet data. We quantify agreement between real and generated data with a graph-based Fr\'echet Inception distance, and the particle and jet feature-level 1-Wasserstein distance for the MNIST and jet datasets respectively.
Simulating Surface Wave Dynamics with Convolutional Networks

We investigate the performance of fully convolutional networks to simulate the motion and interaction of surface waves in open and closed complex geometries. We focus on a U-Net architecture and analyse how well it generalises to geometric configurations not seen during training. We demonstrate that a modified U-Net architecture is capable of accurately predicting the height distribution of waves on a liquid surface within curved and multi-faceted open and closed geometries, when only simple box and right-angled corner geometries were seen during training. We also consider a separate and independent 3D CNN for performing time-interpolation on the predictions produced by our U-Net. This allows generating simulations with a smaller time-step size than the one the U-Net has been trained for.
Spectral Analysis of Word Statistics.

Given a random text over a finite alphabet, we study the frequencies at which fixed-length words occur as subsequences. As the data size grows, the joint distribution of word counts exhibits a rich asymptotic structure. We investigate all linear combinations of subword statistics, and fully characterize their different orders of magnitude using diverse algebraic tools. Moreover, we establish the spectral decomposition of the space of word statistics of each order. We provide explicit formulas for the eigenvectors and eigenvalues of the covariance matrix of the multivariate distribution of these statistics. Our techniques include and elaborate on a set of algebraic word operators, recently studied and employed by Dieker and Saliola (Adv Math, 2018). Subword counts find applications in Combinatorics, Statistics, and Computer Science. We revisit special cases from the combinatorial literature, such as intransitive dice, random core partitions, and questions on random walk. Our structural approach describes in a unified framework several classical statistical tests. We propose further potential applications to data analysis and machine learning.
DEF

We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes. Differently from existing data-driven methods, which reduce this problem to feature classification, we propose to regress a scalar field representing the distance from point samples to the closest feature line on local patches. Our approach is the first that scales to massive point clouds by fusing distance-to-feature estimates obtained on individual patches. We extensively evaluate our approach against related state-of-the-art methods on newly proposed synthetic and real-world 3D CAD model benchmarks. Our approach not only outperforms these (with improvements in Recall and False Positives Rates), but generalizes to real-world scans after training our model on synthetic data and fine-tuning it on a small dataset of scanned data. We demonstrate a downstream application, where we reconstruct an explicit representation of straight and curved sharp feature lines from range scan data. We make code, pre-trained models, and our training and evaluation datasets available at https://github.com/artonson/def.
Laser induced molecular axis alignment : measurement and applications in attosecond science

This thesis reports the measurement and applications of molecular axis alignment induced by strong non-resonant linearly polarised laser fields. The spatial alignment of gas phase molecules overcomes the loss of information that results from averaging angle-dependent quantities over all the possible orientations of an isotropic sample. Therefore, laser-induced molecular alignment techniques are an essential component in new experiments aimed at measuring the structure of molecules with attosecond time resolution. In the first part of this thesis an experiment to measure molecular axis alignment is described. This experiment is based on the velocity map imaging technique in conjunction with time-resolved femtosecond laser Coulomb explosion of the molecular sample by an intense circularly polarised laser beam. A circularly polarised beam is needed to ensure a uniform detection efficiency for each possible orientation of the molecular axis in the polarisation plane. However, such a polarisation produces ion distributions that are not cylindrically symmetric, preventing the use of the standard Abel inversion technique to retrieve the three-dimensional ion distributions from the detected two-dimensional images. A new inversion algorithm is presented that allows the retrieval of molecular axis distributions from angular distributions of ions without cylindrical symmetry. The second part of the thesis focuses on the application of laser-induced molecular alignment to retrieve molecular structure and dynamics from high-order harmonic generation (HHG) experiments. HHG with a mid-infrared laser source (1300 nm) from aligned molecular samples of CO2, N2, C2H2, and N2O are presented. The use of a laser source with a wavelength longer than that used in previous experiments (800 nm) has increased the amount of information obtainable from such experiments. These experiments have provided insight into the hole dynamics of CO2 following ionisation, and reveal for the first time structural features in the HHG spectra of molecules with low ionisation potentials such as C2H2.
Currents of created pairs in strong electric fields

In this paper, we calculate tree-level currents of created particles in strong background electric fields in 4D QED for various initial states. Namely, we do that in pulse background for initial vacuum and thermal states at past infinity. In both cases, we find that the current grows linearly with the length of the pulse with coefficients of proportionality containing the characteristic Schwinger’s factor. For the constant electric field background, we calculate the current for several different initial states. We observe that in such a case the current is either zero or linearly divergent. We explain the reason for such a behavior and compare the situation in ordinary and scalar QED. Finally, we calculate the current in two-dimensional situation in the presence of such settings when the so-called Klein paradox can be observed.
Data-driven optimal control of a seir model for COVID-19

Since the first case of COVID-19 in December 2019, a total of $11,357,322$ confirmed cases in the US and $55,624,562$ confirmed cases worldwide have been reported, up to November 17, 2020, evoking fear locally and internationally. In particular, the coronavirus is surging nationwide at this time. In this paper, we investigate a basic Susceptible-Exposed-Infectious-Recovered (SEIR) model and calibrate the model parameters to the reported data. We also attempt to forecast the evolution of the outbreak over a relatively short time period and provide scheduled optimal controls of the COVID-19 epidemic. We provide efficient numerical algorithms based on a generalized Pontryagin Maximum Principle associated with the optimal control theory. Numerical experiments demonstrate the effective performance of the proposed model and its numerical approximations.
A Comprehensive X-Ray Report on AT2019wey

Here, we present MAXI, Swift, NICER, NuSTAR, and Chandra observations of the X-ray transient AT2019wey (SRGA J043520.9+552226, SRGE J043523.3+552234). From spectral and timing analyses we classify it as a Galactic low-mass X-ray binary (LMXB) with a black hole (BH) or neutron star (NS) accretor. AT2019wey stayed in the low/hard state (LHS) from 2019 December to 2020 August 21, and the hard-intermediate state (HIMS) from 2020 August 21 to 2020 November. For the first six months of the LHS, AT2019wey had a flux of ∼1 mCrab, and displayed a power-law X-ray spectrum with photon index Γ = 1.8. From 2020 June to August, it brightened to ∼20 mCrab. Spectral features characteristic of relativistic reflection became prominent. On 2020 August 21, the source left the “hard line” on the rms–intensity diagram, and transitioned from LHS to HIMS. The thermal disk component became comparable to the power-law component. A low-frequency quasi-periodic oscillation (QPO) was observed. The QPO central frequency increased as the spectrum softened. No evidence of pulsation was detected. We are not able to decisively determine the nature of the accretor (BH or NS). However, the BH option is favored by the position of this source on the Γ–L X, L radio–L X, and L opt–L X diagrams. We find the BH candidate XTE J1752−223 to be an analog of AT2019wey. Both systems display outbursts with long plateau phases in the hard states. We conclude by noting the potential of SRG in finding new members of this emerging class of low luminosity and long-duration LMXB outbursts.
Data Preprocessing to Mitigate Bias with Boosted Fair Mollifiers

In a recent paper, Celis et al. (2020) introduced a new approach to fairness that corrects the data distribution itself. The approach is computationally appealing, but its approximation guarantees with respect to the target distribution can be quite loose as they need to rely on a (typically limited) number of constraints on data-based aggregated statistics; also resulting on a fairness guarantee which can be data dependent. Our paper makes use of a mathematical object recently introduced in privacy -- mollifiers of distributions -- and a popular approach to machine learning -- boosting -- to get an approach in the same lineage as Celis et al. but without those impediments, including in particular, better guarantees in terms of accuracy and finer guarantees in terms of fairness. The approach involves learning the sufficient statistics of an exponential family. When training data is tabular, it is defined by decision trees whose interpretability can provide clues on the source of (un)fairness. Experiments display the quality of the results obtained for simulated and real-world data.
Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection. It overcomes the inherent limitations of IoU-based NMS variants to provide a more stable, consistent predictor of bounding box clustering by using a normalized Manhattan Distance inspired proximity metric to represent bounding box clustering. Unlike Greedy and Soft NMS, it does not rely solely on classification confidence scores to select optimal bounding boxes, instead selecting the box which is closest to every other box within a given cluster and removing highly confluent neighboring boxes. Confluence is experimentally validated on the MS COCO and CrowdHuman benchmarks, improving Average Precision by 0.2--2.7% and 1--3.8% respectively and Average Recall by 1.3--9.3 and 2.4--7.3% when compared against Greedy and Soft-NMS variants. Quantitative results are supported by extensive qualitative analysis and threshold sensitivity analysis experiments support the conclusion that Confluence is more robust than NMS variants. Confluence represents a paradigm shift in bounding box processing, with potential to replace IoU in bounding box regression processes.