Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mechanism and Modulation of H-NS Mediated Repression in Escherichia coli.

The histone-like nucleoid structuring protein H-NS acts as a global repressor of genes that are expressed in response to environmental stimuli and stress conditions. Repression by H-NS is presumably mediated by binding of H NS to primary "nucleation sites" close to promoters, and the formation of extended nucleoprotein complex from these nucleation sites to inhibit transcription initiation. Modulation of H-NS mediated repression is a complex process involving specific transcription factors and physiology dependent structural alterations. The E. coli bgl and proU operons are model systems that are repressed by H-NS with exceptional specificity. Both of these systems possess upstream and downstream regulatory elements (URE and DRE) bound by H-NS for efficient repression. The present study demonstrates that repression by H-NS binding upstream and downstream is synergistic in proU (as shown in a parallel study for bgl), and that H-NS when bound within the transcription unit represses transcription initiation at the bgl promoter, as reported before for proU. Repression by binding of H-NS downstream is known to be modulated. Common to both proU and bgl is that an increase in the promoter activity abrogates repression. For bgl it is known, that the H-NS mediated repression of the promoter is counteracted by transcription factors BglJ and LeuO. Further, termination factor Rho and the protease Lon are known to modulate repression by H-NS through the DRE, and as shown here the DnaKJ chaperone system is essential for this repression. In case of proU, the promoter is osmoregulated; the RNA polymerase is poised at the promoter at low osmolarity, while it clears the promoter with better efficiency at high osmolarity. Furthermore, the proU operon is subject to post-transcriptional osmoregulation. The proU mRNA is processed by RNAse III within a stretch of highly conserved sequence, suggesting a common mechanism of regulation among Enterobacteria. In summary, the present study demonstrates that the mechanism of H-NS mediated repression of the bgl and proU operons is very similar. However, its modulation is complex involving numerous additional factors specific to the two systems, and thus is achieved in a context specific manner.
Lipschitz regularity of graph Laplacians on random data clouds

In this paper we study Lipschitz regularity of elliptic PDEs on geometric graphs, constructed from random data points. The data points are sampled from a distribution supported on a smooth manifold. The family of equations that we study arises in data analysis in the context of graph-based learning and contains, as important examples, the equations satisfied by graph Laplacian eigenvectors. In particular, we prove high probability interior and global Lipschitz estimates for solutions of graph Poisson equations. Our results can be used to show that graph Laplacian eigenvectors are, with high probability, essentially Lipschitz regular with constants depending explicitly on their corresponding eigenvalues. Our analysis relies on a probabilistic coupling argument of suitable random walks at the continuum level, and an interpolation method for extending functions on random point clouds to the continuum manifold. As a byproduct of our general regularity results, we obtain high probability $L^\infty$ and approximate $\mathcal{C}^{0,1}$ convergence rates for the convergence of graph Laplacian eigenvectors towards eigenfunctions of the corresponding weighted Laplace-Beltrami operators. The convergence rates we obtain scale like the $L^2$-convergence rates established by two of the authors in previous work.
Evolutionary dynamics of sex determination : Mechanistic theory and empirical investigations

Sex determining mechanisms are highly variable between different taxonomic groups and appear to change relatively quickly during evolution. Sex ratio selection could be a dominant force causing such changes. We investigate theoretically the effect of sex ratio selection on the dynamics of a multi-factorial sex determining system. The system considered resembles the naturally occurring three-locus system of the housefly which allows for male heterogamety, female heterogamety and a variety of other mechanisms. Sex ratio selection is modeled by assuming cost differences in the production of sons and daughters, a scenario leading to a strong sex ratio bias in the absence of constraints imposed by the mechanism of sex determination. We show that, despite of the presumed flexibility of the sex determining system considered, equilibrium sex ratios never deviate strongly from 1:1. Even if daughters are very costly, a male-biased sex ratio can never evolve. If sons are more costly, the sex ratio can be slightly female biased but even in case of large cost differences the bias is very small (<10% from 1:1). Sex ratio selection can lead to a shift in the sex determining mechanism, but cannot be the sole cause of complete switches from one sex determining system to another. In fact, more than one locus remains polymorphic at equilibrium. We discuss our results in the context of evolution of the variable sex determining mechanism found in natural housefly populations. *© 2006 European Society for Evolutionary Biology
A Self-Organizing Extreme-Point Tabu-Search Algorithm for Fixed Charge Network Problems with Extensions

We propose a new self-organizing algorithm for fixed-charge network flow problems based on ghost image (GI) processes as proposed in Glover (1994) and adapted to fixed-charge transportation problems in Glover, Amini and Kochenberger (2005). Our self-organizing GI algorithm iteratively modifies an idealized representation of the problem embodied in a parametric ghost image, enabling all steps to be performed with a primal network flow algorithm operating on the parametric GI. Computational tests are carried out on an extensive set of benchmark problems which includes the previous largest set in the literature, comparing our algorithm to the best methods previously proposed for fixed-charge transportation problems, though our algorithm is not specialized to this class. We also provide comparisons for additional more general fixed-charge network flow problems against Cplex 12.8 to demonstrate that the new self-organizing GI algorithm is effective on large problem instances, finding solutions with statistically equivalent objective values at least 700 times faster. The attractive outcomes produced by the current GI/TS implementation provide a significant advance in our ability to solve fixed-cost network problems efficiently and invites its use for larger instances from a variety of application domains.
Redshifted 21-cm bispectrum – I. Impact of the redshift space distortions on the signal from the Epoch of Reionization

The bispectrum can quantify the non-Gussianity present in the redshifted 21-cm signal produced by the neutral hydrogen (HI) during the epoch of reionization (EoR). Motivated by this, we perform a comprehensive study of the EoR 21-cm bispectrum using simulated signals. Given a model of reionization, we demonstrate the behaviour of the bispectrum for all unique triangles in $k$ space. For ease of identification of the unique triangles, we parametrize the $k$-triangle space with two parameters, namely the ratio of the two arms of the triangle ($n=k_2/k_1$) and the cosine of the angle between them ($\cos{\theta}$). Furthermore, for the first time, we quantify the impact of the redshift space distortions (RSD) on the spherically averaged EoR 21-cm bispectrum in the entire unique triangle space. We find that the real space signal bispectra for small and intermediate $k_1$-triangles ($k_1 \leq 0.6 \,{\rm Mpc^{-1}}$) is negative in most of the unique triangle space. It takes a positive sign for squeezed, stretched and linear $k_1$-triangles, specifically for large $k_1$ values ($k_1 \geq 0.6 \,{\rm Mpc^{-1}}$). The RSD affects both the sign and magnitude of the bispectra significantly. It changes (increases/decreases) the magnitude of the bispectra by $50-100\%$ without changing its sign (mostly) during the entire period of the EoR for small and intermediate $k_1$-triangles. For larger $k_1$-triangles, RSD affects the magnitude by $100-200\%$ and also flips the sign from negative to positive. We conclude that it is important to take into account the impact of RSD for a correct interpretation of the EoR 21-cm bispectra.
Area rule for circulation and minimal surfaces in three-dimensional turbulence

An important idea underlying a plausible dynamical theory of circulation in 3D turbulence is the so-called Area Rule, according to which the probability density function (PDF) of the circulation around closed loops depends only on the minimal area of the loop. Using data from direct numerical simulations on an $8192^3$ grid, we show that the tails of the PDF, characterizing large circulation amplitudes, satisfy the Area Rule. The tails of the PDFs for the minimal area of non-planar loops differ from those of planar loops circumscribing the same area, but the two agree when suitably normalized. A Gaussian Random Field with the same two-point correlation as the DNS data replicates the behavior of low-order moments of circulation but not the large amplitude universality. This work highlights the hitherto unknown connection between minimal surfaces and turbulence.
Fast and memory-optimal dimension reduction using Kac’s walk

In this work, we analyze dimension reduction algorithms based on the Kac walk and discrete variants. (1) For $n$ points in $\mathbb{R}^{d}$, we design an optimal Johnson-Lindenstrauss (JL) transform based on the Kac walk which can be applied to any vector in time $O(d\log{d})$ for essentially the same restriction on $n$ as in the best-known transforms due to Ailon and Liberty [SODA, 2008], and Bamberger and Krahmer [arXiv, 2017]. Our algorithm is memory-optimal, and outperforms existing algorithms in regimes when $n$ is sufficiently large and the distortion parameter is sufficiently small. In particular, this confirms a conjecture of Ailon and Chazelle [STOC, 2006] in a stronger form. (2) The same construction gives a simple transform with optimal Restricted Isometry Property (RIP) which can be applied in time $O(d\log{d})$ for essentially the same range of sparsity as in the best-known such transform due to Ailon and Rauhut [Discrete Comput. Geom. , 2014]. (3) We show that by fixing the angle in the Kac walk to be $\pi/4$ throughout, one obtains optimal JL and RIP transforms with almost the same running time, thereby confirming -- up to a $\log\log{d}$ factor -- a conjecture of Avron, Maymounkov, and Toledo [SIAM J. Sci. Comput. , 2010]. Our moment-based analysis of this modification of the Kac walk may also be of independent interest.
Component Order Connectivity in Directed Graphs

A directed graph D is semicomplete if for every pair x , y of vertices of D , there is at least one arc between x and y . Thus, a tournament is a semicomplete digraph. In the Directed Component Order Connectivity (DCOC) problem, given a digraph $$D=(V,A)$$ D = ( V , A ) and a pair of natural numbers k and $$\ell $$ ℓ , we are to decide whether there is a subset X of V of size k such that the largest strongly connected component in $$D-X$$ D - X has at most $$\ell $$ ℓ vertices. Note that DCOC reduces to the Directed Feedback Vertex Set problem for $$\ell =1. $$ ℓ = 1 . We study the parameterized complexity of DCOC for general and semicomplete digraphs with the following parameters: $$k, \ell ,\ell +k$$ k , ℓ , ℓ + k and $$n-\ell $$ n - ℓ . In particular, we prove that DCOC with parameter k on semicomplete digraphs can be solved in time $$O^*(2^{16k})$$ O ∗ ( 2 16 k ) but not in time $$O^*(2^{o(k)})$$ O ∗ ( 2 o ( k ) ) unless the Exponential Time Hypothesis (ETH) fails. The upper bound $$O^*(2^{16k})$$ O ∗ ( 2 16 k ) implies the upper bound $$O^*(2^{16(n-\ell )})$$ O ∗ ( 2 16 ( n - ℓ ) ) for the parameter $$n-\ell . $$ n - ℓ . We complement the latter by showing that there is no algorithm of time complexity $$O^*(2^{o({n-\ell })})$$ O ∗ ( 2 o ( n - ℓ ) ) unless ETH fails. Finally, we improve (in dependency on $$\ell $$ ℓ ) the upper bound of Göke, Marx and Mnich (2019) for the time complexity of DCOC with parameter $$\ell +k$$ ℓ + k on general digraphs from $$O^*(2^{O(k\ell \log (k\ell ))})$$ O ∗ ( 2 O ( k ℓ log ( k ℓ ) ) ) to $$O^*(2^{O(k\log (k\ell ))}). $$ O ∗ ( 2 O ( k log ( k ℓ ) ) ) . Note that Drange, Dregi and van ’t Hof (2016) proved that even for the undirected version of DCOC on split graphs there is no algorithm of running time $$O^*(2^{o(k\log \ell )})$$ O ∗ ( 2 o ( k log ℓ ) ) unless ETH fails and it is a long-standing problem to decide whether Directed Feedback Vertex Set admits an algorithm of time complexity $$O^*(2^{o(k\log k)}). $$ O ∗ ( 2 o ( k log k ) ) .
The effect of parameter uncertainty on a model with adjusted parameters

Des modeles de culture ont en general trop de parametres pour permettre de les ajuster tous aux donnees. On compense par l'utilisation d'information a priori sur les parametres, mais cette information est incertaine. Il est alors important d'evaluer l'importance de cette incertitude sur les modeles. On propose ici une telle evaluation dans le cas d'un modele de mais, ou un algorithme recent est utilise pour ajuster une partie des parametres. Pratiquement, on utilise l'algorithme avec des ensembles differents de valeurs initiales des parametres. L'algorithme determine automatiquement quels parametres devraient etre ajustes. On trouve que ce choix varie en fonction des valeurs initiales. Neanmoins, les deux premiers parametres choisis sont toujours les memes ou (pour le seul cas d'exception) ont essentiellement le meme effet dans le modele. La qualite de prediction et les predictions individuelles sont egalement relativement stables pour differentes valeurs initiales des parametres.
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level

Previous works on the CERT insider threat detection case have neglected graph and text features despite their relevance to describe user behavior. Additionally, existing systems heavily rely on feature engineering and audit data aggregation to detect malicious activities. This is time consuming, requires expert knowledge and prevents tracing back alerts to precise user actions. To address these issues we introduce ADSAGE to detect anomalies in audit log events modeled as graph edges. Our general method is the first to perform anomaly detection at edge level while supporting both edge sequences and attributes, which can be numeric, categorical or even text. We describe how ADSAGE can be used for fine-grained, event level insider threat detection in different audit logs from the CERT use case. Remarking that there is no standard benchmark for the CERT problem, we use a previously proposed evaluation setting based on realistic recall-based metrics. We evaluate ADSAGE on authentication, email traffic and web browsing logs from the CERT insider threat datasets, as well as on real-world authentication events. ADSAGE is effective to detect anomalies in authentications, modeled as user to computer interactions, and in email communications. Simple baselines give surprisingly strong results as well. We also report performance split by malicious scenarios present in the CERT datasets: interestingly, several detectors are complementary and could be combined to improve detection. Overall, our results show that graph features are informative to characterize malicious insider activities, and that detection at fine-grained level is possible.
DPPH free radical scavenging activity of some Bangladeshi medicinal plants

Barringtonia acutangula (Local name-Hijal, Kumia etc.), Erythrina variegata (Local name- Mandar) and Sida cordifolia (Local name- Brela, Bala etc.) are traditionally employed to cure various disorders. The crude extracts of these three traditionally used medicinal plants available in Bangladesh were tested for their potential antioxidant activity against the stable DPPH (2,2-diphenyl-1-picryl-hydrazyl) free radical. Ethanolic extracts of the leaves of Barringtonia acutangula and Erythrina variegata showed potential antioxidant activity (IC50:1 g/ml) against DPPH free radical whereas their ethanolic bark extracts and ethanolic root extract of Sida cordifolia showed moderate to mild activity.
Domain Adaptation for Robust Workload Classification using fNIRS

Significance: We demonstrated the potential of using domain adaptation on functional Near-Infrared Spectroscopy (fNIRS) data to detect and discriminate different levels of n-back tasks that involve working memory across different experiment sessions and subjects. Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W). Approach: We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels. We also compared with a supervised method - Convolutional Neural Network (CNN). Results: For session-by-session alignment, using G-W resulted in alignment accuracy of 70 $\pm$ 4 % (weighted mean $\pm$ standard error), whereas using CNN resulted in classification accuracy of 58 $\pm$ 5 % across five subjects. For subject-by-subject alignment, using FG-W resulted in alignment accuracy of 55 $\pm$ 3 %, whereas using CNN resulted in classification accuracy of 45 $\pm$ 1 %. Where in both cases 25 % represents chance. We also showed that removal of motion artifacts from the fNIRS data plays an important role in improving alignment performance. Conclusions: Domain adaptation is potential for session-by-session and subject-by-subject alignment using fNIRS data.
On the Approximation of the Black Hole Shadow with a Simple Polar Curve

A black hole embedded within a bright, optically thin emitting region imprints a nearly circular “shadow” on its image, corresponding to the observer’s line of sight into the black hole. The shadow boundary depends on the black hole’s mass and spin, providing an observable signature of both properties via high-resolution images. However, standard expressions for the shadow boundary are most naturally parametrized by Boyer–Lindquist radii rather than by image coordinates. We explore simple, approximate parameterizations for the shadow boundary using ellipses and a family of curves known as limaçons. We demonstrate that these curves provide excellent and efficient approximations for all black hole spins and inclinations. In particular, we show that the two parameters of the limaçon naturally account for the three primary shadow deformations resulting from mass and spin: size, displacement, and asymmetry. These curves are convenient for parametric model fitting directly to interferometric data, they reveal the degeneracies expected when estimating black hole properties from images with practical measurement limitations, and they provide a natural framework for parametric tests of the Kerr metric using black hole images.
Data-driven modelling and characterisation of task completion sequences in online courses

The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners' behaviors, and to identify critical tasks and course sessions in a given course design. We also introduce a recently developed probabilistic Bayesian model to learn sequence trajectories of students and predict student performance. The application of our data-driven sequence-based analyses to data from learners undertaking an on-line Business Management course reveals distinct behaviors within the cohort of learners, identifying learners or groups of learners that deviate from the nominal order expected in the course. Using course grades a posteriori, we explore differences in behavior between high and low performing learners. We find that high performing learners follow the progression between weekly sessions more regularly than low performing learners, yet within each weekly session high performing learners are less tied to the nominal task order. We then model the sequences of high and low performance students using the probablistic Bayesian model and show that we can learn engagement behaviors associated with performance. We also show that the data sequence framework can be used for task centric analysis; we identify critical junctures and differences among types of tasks within the course design. We find that non-rote learning tasks, such as interactive tasks or discussion posts, are correlated with higher performance. We discuss the application of such analytical techniques as an aid to course design, intervention, and student supervision.
Pose2RGBD. Generating Depth and RGB images from absolute positions

We propose a method at the intersection of Computer Vision and Computer Graphics fields, which automatically generates RGBD images using neural networks, based on previously seen and synchronized video, depth and pose signals. Since the models must be able to reconstruct both texture (RGB) and structure (Depth), it creates an implicit representation of the scene, as opposed to explicit ones, such as meshes or point clouds. The process can be thought of as neural rendering, where we obtain a function f : Pose -> RGBD, which we can use to navigate through the generated scene, similarly to graphics simulations. We introduce two new datasets, one based on synthetic data with full ground truth information, while the other one being recorded from a drone flight in an university campus, using only video and GPS signals. Finally, we propose a fully unsupervised method of generating datasets from videos alone, in order to train the Pose2RGBD networks. Code and datasets are available at:: this https URL.
Global Minimax Approximations and Bounds for the Gaussian Q-Function by Sums of Exponentials

This paper presents a novel systematic methodology to obtain new simple and tight approximations, lower bounds, and upper bounds for the Gaussian <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-function, and functions thereof, in the form of a weighted sum of exponential functions. They are based on minimizing the maximum absolute or relative error, resulting in globally uniform error functions with equalized extrema. In particular, we construct sets of equations that describe the behaviour of the targeted error functions and solve them numerically in order to find the optimized sets of coefficients for the sum of exponentials. This also allows for establishing a trade-off between absolute and relative error by controlling weights assigned to the error functions’ extrema. We further extend the proposed procedure to derive approximations and bounds for any polynomial of the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-function, which in turn allows approximating and bounding many functions of the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-function that meet the Taylor series conditions, and consider the integer powers of the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-function as a special case. In the numerical results, other known approximations of the same and different forms as well as those obtained directly from quadrature rules are compared with the proposed approximations and bounds to demonstrate that they achieve increasingly better accuracy in terms of the global error, thus requiring significantly lower number of sum terms to achieve the same level of accuracy than any reference approach of the same form.