Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Kepler/K2 and XMM‐Newton observations of superflares in the Pleiades

Context. Flares are powerful events ignited by a sudden release of magnetic energy which triggers a cascade of interconnected phenomena, each resulting in emission in different electromagnetic bands. In fact, in the Sun flares are observed across the whole electromagnetic spectrum. Multi-band observations of stellar flares are instead rare. This limits our ability to extend what we learn from solar flares to the case of flares occurring in stars with different properties. Aims. With the aim of studying flares in the 125-Myr-old stars in the Pleiades observed simultaneously in optical and X-ray light, we obtained new XMM-Newton observations of this cluster during the observations of Kepler K2 Campaign 4. The objective of this paper is to characterize the most powerful flares observed in both bands and to constrain the energy released in the optical and X-ray, the geometry of the loops, and their time evolution. We also aim to compare our results to existing studies of flares occurring in the Sun and stars at different ages. Methods. We selected bright X-ray/optical flares that occurred in 12 known members of the Pleiades from their K2 and XMM-Newton light curves. The sample includes ten K-M stars, one F9 star, and one G8 star. Flare average properties were obtained from integrated analysis of the light curves during the flares. The time evolution of the plasma in the magnetic loops is constrained with time-resolved X-ray spectral analysis. Results. Most of the flares studied in this work emitted more energy in optical than in X-rays, as in most solar flares, even if the Pleiades flares output a larger fraction of their total energy in X-rays than typical solar flares do. Additionally, the energy budget in the two bands is weakly correlated. We also found comparable flare duration in optical and X-rays and observed that rapidly rotating stars (e.g., with rotation period shorter than 0.5 days) preferentially host short flares. We estimated the slope of the cooling path of the flares in the log(EM)-vs.-log(T) plane. The values we obtained are affected by large uncertainties, but their nominal values suggest that the flares analyzed in this paper are mainly due to single loops with no sustained heating occurring during the cooling phase. We also observed and analyzed oscillations with a period of 500 s during one of the flares. Conclusions. The flares observed in the Pleiades can be classified as “superflares” based on their energy budget in the optical, and share some of the properties of the flares observed in the Sun, despite being more energetic. For instance, as in most solar flares, more energy is typically released in the optical than in X-rays and the duration of the flares in the two bands is correlated. We have attempted a comparison between the X-ray flares observed in the Pleiades and those observed in clusters with different ages, but to firmly address any evolutionary pattern of flare characteristics, similar and uniform multi-wavelength analyses on more complete samples are necessary.
Two independent photon pairs versus four-photon entangled states in parametric down conversion

Abstract We study the physics of four-photon states generated in spontaneous parametric down-conversion with a pulsed pump field. In the limit where the coherence time of the photons t ph c is much shorter than the duration of the pump pulse Δt, the four photons can be described as two independent pairs. In the opposite limit, the four photons are in a four-particle entangled state. Any intermediate case can be characterized by a single parameter χ, which is a function of t ph c Δt. We present a direct measurement of χ through a simple experimental set-up. The full theoretical analysis is also provided.
A Robust, Compact and Diverse Population Code for Competing Sounds in Auditory Cortex

Cortical circuits encoding sensory information consist of populations of neurons, yet how information aggregates via pooling individual cells remains poorly understood. Such pooling may be particularly important in noisy settings where single neuron encoding is degraded. One example is the cocktail party problem, with competing sounds from multiple spatial locations. How populations of neurons in auditory cortex (ACx) code competing sounds have not been previously investigated. Here, we apply a novel information theoretic approach to estimate information in populations of neurons in ACx about competing sounds from multiple spatial locations, including both summed population (SP) and labeled line (LL) codes. We find that a small subset of neurons is sufficient to nearly maximize mutual information over different spatial configurations, with the LL code outperforming the SP code, and approaching information levels attained without noise. Moreover, with a LL code, units with diverse spatial responses, including both regular and narrow-spiking units, constitute the best pool. Finally, information in the population increases with spatial separation between target and masker, in correspondence with behavioral results on spatial release from masking in human and animals. Taken together, our results reveal that a compact and diverse population of neurons in ACx provide a robust code for competing sounds from different spatial locations.
Fish-microbe systems in the hostile but highly biodiverse Amazonian blackwaters

Amazonian blackwaters are extremely biodiverse systems containing some of the most naturally acidic, dissolved organic carbon-rich and ion-poor waters on Earth. Physiological adaptations of fish facing these ionoregulatory challenges are unresolved but could involve microbially-mediated processes. Here, we characterize the physiological response of 964 fish-microbe systems from four blackwater Teleost species along a natural hydrochemical gradient, using dual RNA-Seq and 16S rRNA of gill samples. We find that responses to blackwaters are host-species-specific, but occasionally include the overexpression of Toll-receptors and integrins associated to interkingdom communication. Blackwater gill microbiomes are characterized by a transcriptionally-active betaproteobacterial cluster potentially interfering with epithelial permeability. We explore further blackwater fish-microbe interactions by analyzing transcriptomes of 320 axenic zebrafish larvae exposed to sterile, non-sterile and inverted (non-native bacterioplankton) blackwater. We find that axenic zebrafish do not survive well when exposed to sterile/inverted blackwater, suggesting an essential role of endogenous symbionts in blackwater fish physiology.
The role of consciously timed movements in shaping and improving auditory timing

Our subjective sense of time is intertwined with a plethora of perceptual, cognitive, and motor functions, and likewise, the brain is equipped to expertly filter, weight, and combine these signals for seamless interactions with a dynamic world. Until relatively recently, the literature on time perception has excluded the influence of motor activity, yet, it has been found that motor circuits in the brain are at the core of most timing functions. Several studies have now identified that concurrent movements exert robust effects on perceptual timing estimates, but critically, have not assessed how humans consciously judge the duration of their own movements. This creates a gap in our understanding of the mechanisms driving movement-related effects on sensory timing. We sought to address this gap by administering a sensorimotor timing task in which we explicitly compared the timing of isolated auditory tones and arm movements, or both simultaneously. We contextualized our findings within a Bayesian cue combination framework, in which separate sources of temporal information are weighted by their reliability and integrated into a unitary time estimate that is more precise than either unisensory estimate. Our results revealed differences in accuracy between auditory, movement, and combined trials, and crucially, that combined trials were the most accurately timed. Under the Bayesian framework, we found that participants’ combined estimates were more precise than isolated estimates in a way that trended towards optimality, while being overall less optimal than the model’s prediction. These findings elucidate previously unknown qualities of conscious motor timing, and proposes computational mechanisms that can describe how movements combine with perceptual signals to create unified, multimodal experiences of time.
Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors (TFs) in intricate regulatory networks at a cell-type specific manner. Here we presented a reference single-cell transcriptomic atlas of Arabidopsis seedlings by integration of 40 published scRNA-seq datasets from representative tissues as well as the entire under- and above-ground parts. We identified 34 distinct cell types or states, largely expanding our current view of plant cell compositions. We then mapped the developmental trajectory of root-shoot lineage separation and identified differential gene expression programs that may regulate the cell fate determination of under- and above-ground organs. Lastly, we systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordination manner to control cell-type specific gene expression. Taken together, our study not only offers a valuable resource plant cell atlas exploration but also provides molecular insights into gene-regulatory programs that determines organ specify, particularly the differentiation between root and shoot.
Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data

Although an essential step, the functional annotation of cells often proves particularly challenging in the analysis of single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatise the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to repurpose and contextualise new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways’ activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualise new single cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.
Transcriptome analyses using regulon, functional category, and operon information with GINtool

When analysing transcriptome data, threshold values are chosen to decide whether the regulation of a gene is relevant or not, however this may result in the loss of valuable information. To overcome this problem it can be useful to analyse regulons instead of individual genes, to harness the statistical power of combining genes. Another advantage of a regulon-based analysis is that it provides direct insights into the activity of regulatory pathways, which is the essence of transcriptome analyses. We have developed a software tool called GINtool that can use regulon information to analyse transcriptome data. GINtool includes the option to take the activity mode of a regulator in account, which is important when a regulator can function both as an activator and repressor. GINtool also contains two novel graphical representations that greatly facilitate the visual inspection of regulon-based transcriptome analyses. Additional features of GINtool includes the evaluation of transcriptome data using functional categories, and the analysis of gene expression differences within operons. To ease the analyses and downstream processing of figures, GINtool has been developed as an add-in for Excel.
TENALIGN: Joint Tensor Alignment and Coupled Factorization

Multimodal datasets represented as tensors oftentimes share some of their modes. However, even though there may exist a one-to-one (or perhaps partial) correspondence between the coupled modes, such correspondence/alignment may not be given, especially when integrating datasets from disparate sources. This is a very important problem, broadly termed as entity alignment or matching, and subsets of the problem such as graph matching have been extremely popular in the recent years. In order to solve this problem, current work computes the alignment based on existing embeddings of the data. This can be problematic if our end goal is the joint analysis of the two datasets into the same latent factor space: the embeddings computed separately per dataset may yield a suboptimal alignment, and if such an alignment is used to subsequently compute the joint latent factors, the computation will similarly be plagued by compounding errors incurred by the imperfect alignment. In this work, we are the first to define and solve the problem of joint tensor alignment and factorization into a shared latent space. By posing this as a unified problem and solving for both tasks simultaneously, we observe that the both alignment and factorization tasks benefit each other resulting in superior performance compared to two-stage approaches. We extensively evaluate our proposed method TENALIGN and conduct a thorough sensitivity and ablation analysis. We demonstrate that TENALIGN significantly outperforms baseline approaches where embedding and matching happen separately.
The Need for Tabular Representation Learning: An Industry Perspective

The total addressable market for data applications has been estimated at $70B. This 1 includes the $11B market for data integration, which is estimated to grow at 25% 2 in the coming year; $35B market for analytics, growing at 11%; and $19B market 3 for business intelligence, growing at 8% [1]. Given this data-driven future and the 4 scale at which Microsoft operates, we survey PMs, engineers and researchers and 5 synthesize their opinions around extracting insights from tabular data at-scale. We 6 see three main areas where tabular representation learning (TRL) can be leveraged: 7 Data insights . Enabling real-time analytics is one of the key priorities for Mi- 8 crosoft’s new intelligence platform [6] now that a converged environment exists 9 to house any type of data. TRL models can help expose column and table-level 10 semantic annotations, relationships between columns and between tables, and 11 advanced data patterns such as semantic-aware denial constraints [5]. 12 Data management . From our internal workload telemetry, we know that 17.8% of 13 tabular data across our virtual clusters remain unaccessed [9]. From an external 14 perspective, leveraging telemetry from Azure Observability Platform, we observed 15 that out of the 10B+ metrics generated, less than 0.1% is used [3]. Bringing this 16 data to light requires sophisticated data discovery, data understanding and data 17 integration capabilities. We believe TRL models can play an important role on tasks 18 such as entity detection and deduplication, schema mapping, and data imputation. 19 Data movement . It is well-known that data movement remains a key bottleneck 20 in analytics [10]. In order to ensure that our users receive the best performance 21 possible, investments have been made in smart caching policies
Superstring Theory in AdS 3 and Plane Waves

This thesis is devoted to the study of string theory in AdS3 and its applications to recent developments in string theory. The difficulties associated with formulating a consistent string theory in AdS3 and its underlying SL(2, R) WZW model are explained. We describe how these difficulties can be overcome by assuming that the SL(2, R) WZW model contains spectral flow symmetry. The existence of spectral flow symmetry in the fully quantum treatment is proved by a calculation of the one-loop string partition function. We consider Euclidean AdS3 with the time direction periodically identified, and compute the torus partition function in this background. The string spectrum can be reproduced by viewing the one-loop calculation as the free energy of a gas of strings, thus providing a rigorous proof of the results based on spectral flow arguments. Next, we turn to spacetimes that are quotients of AdS3, which include the BTZ black hole and conical spaces. Strings propagating in the conical space are described by taking an orbifold of strings in AdS3. We show that the twisted states of these orbifolds can be obtained by fractional spectral flow. We show that the shift in the ground state energy usually associated with orbifold twists is absent in this case, and offer a unified framework in which to view spectral flow. Lastly, we consider the RNS superstrings in AdS3 × S × M, where M may be K3 or T , based on supersymmetric extensions of SL(2, R) and SU(2) WZW models. We construct the physical states and calculate the spectrum. A subsector of this theory describes strings
A fast algorithm for solving hybrid integral equation

Based on the fast multipole method and the T-matrix formulation, an efficient approach is presented for reducing the operation count of matrix-vector multiplications in the conjugate gradient (CG) iterations to solve a hybrid (surface-volume) integral equation for a dielectric-coated metallic scatterer. The problem considered is the electromagnetic scattering of two-dimensional conductors with dielectric coating. Numerical results are given. The method given here has a computational complexity of N/sup 1.5/. It is seen that the method is more efficient than using CG directly, when the matrix filling for small N consumes a large portion of CPU time, while for the fast algorithm, there is no need to fully generate the coefficient matrix. <<ETX>>
Droplet nucleation and Smoluchowski's equation with growth and injection of particles

We show that models for homogeneous and heterogeneous nucleation of D-dimensional droplets in a d-dimensional medium are described in mean-field by a modified Smoluchowski equation for the distribution N(s,t) of droplets masses s, with additional terms accounting for exogenous growth from vapor absorption, and injection of small droplets when the model allows renucleation. The corresponding collision kernel is derived in both cases. For a generic collision kernel K, the equation describes a clustering process with clusters of mass s growing between collision with ds/dt=As^\\beta$, and injection of monomers at a rate I(t). General properties of this equation are studied. The gel criterion is determined. Without injection, exact solutions are found with a constant kernel, exhibiting unusual scaling behavior. For a general kernel, under the scaling assumption N(s,t)\\sim Y(t)^{-1}f(s/S(t)), we determine the asymptotics of S(t) and Y(t), and derive the scaling equation. Depending on \\beta and K, a great diversity of behaviors is found. For constant injection, there is an asymptotic steady state with N(s,t=\\infty)\\propto s^{-\\tau} and \\tau$is determined. The case of a constant mass injection rate is related to homogeneous nucleation and is studied. Finally, we show how these results shed some new light on heterogeneous nucleation with d=D. For d=D=2 (discs on a plane), numerical simulations are performed, in good agreement with the mean-field results.
Predicting Gravitational Lensing by Stellar Remnants

Gravitational lensing provides a means to measure mass that does not rely on detecting and analysing light from the lens itself. Compact objects are ideal gravitational lenses, because they have relatively large masses and are dim. In this paper we describe the prospects for predicting lensing events generated by the local population of compact objects, consisting of 250 neutron stars, 5 black holes, and approximately 35,000 white dwarfs. By focusing on a population of nearby compact objects with measured proper motions and known distances from us, we can measure their masses by studying the characteristics of any lensing event they generate. Here we concentrate on shifts in the position of a background source due to lensing by a foreground compact object. With HST, JWST, and Gaia, measurable centroid shifts caused by lensing are relatively frequent occurrences. We find that 30-50 detectable events per decade are expected for white dwarfs. Because relatively few neutron stars and black holes have measured distances and proper motions, it is more difficult to compute realistic rates for them. However, we show that at least one isolated neutron star has likely produced detectable events during the past several decades. This work is particularly relevant to the upcoming data releases by the Gaia mission and also to data that will be collected by JWST. Monitoring predicted microlensing events will not only help to determine the masses of compact objects, but will also potentially discover dim companions to these stellar remnants, including orbiting exoplanets.
Bandgap Manipulation of Armchair Graphene nanoribbon (AGNR)

: Bandgap Manipulation is very important for various applications. Optical Devices need smaller Bandgap where as Diode's need larger Bandgap. Armchair graphene Nanoribbon has a special property where if the numbers of atoms along the width is multiple of three or multiple of three plus one, they are semi metallic where as if the number of atoms is multiple of three plus two, it is Metallic that is band gap is zero. Here, it is shown that adding antidote can affect the Bandgap and Transport properties of material. I have used 11-AGNR and 9-AGNR to show that by adding antidotes the Bandgap can be increased or decreased to convert it from metallic to semi metallic. By doing this a significant change has been observed in the Current-Voltage characteristics of the devices where a negative resistance region is obtained which can be exploited in many applications. To simulate the structure, Tight-binding model and Non equilibrium Green's function method is used. The simulation was performed on MATLAB.
Using a robot to interact with children with Autism Spectrum Disorders: A feasibility trial

Background: Methods: Results: Conclusion: Abstract There has been increasing interest in using social robots in clinical settings for children with Autism Spectrum Disorder (ASD). Notably, studies with encouraging results were mostly conducted in highly-controlled and specific settings. We conducted a feasibility trial on the use of a social robot to interact with children with ASD ( n = 8) in a clinical therapeutic setting. Children’s level of interest and engagement, and social responsiveness were assessed via self-reports and parent ratings before and after the sessions. Trained observers rated the children’s behaviours during the sessions. Sustainability of the children’s interest was also measured by the number of sessions they chose to interact with the robot. While most children did not continue beyond four sessions, their reported level of engagement did not decline across the sessions. However, ratings of their social responsiveness did not differ significantly across sessions. Our findings suggest that the use of a social robot to facilitate interaction with children with ASD may be encouraging, but a longer trial with specific targets for improvement in social skills may be required. In addition, the expectation of gains should be moderated in view of the technical capabilities and application of the robot.