Miracle-dz/UniFAR-FTUs huggingface dataset:
Capturing the Continuous Complexity of Behavior in C. elegans

Animal behavior is often quantified through subjective, incomplete variables that may mask essential dynamics. Here, we develop a behavioral state space in which the full instantaneous state is smoothly unfolded as a combination of short-time posture dynamics. Our technique is tailored to multivariate observations and extends previous reconstructions through the use of maximal prediction. Applied to high-resolution video recordings of the roundworm C. elegans, we discover a low-dimensional state space dominated by three sets of cyclic trajectories corresponding to the worm’s basic stereotyped motifs: forward, backward, and turning locomotion. In contrast to this broad stereotypy, we find variability in the presence of locally-unstable dynamics, and this unpredictability shows signatures of deterministic chaos: a collection of unstable periodic orbits together with a positive maximal Lyapunov exponent. The full Lyapunov spectrum is symmetric with positive, chaotic exponents driving variability balanced by negative, dissipative exponents driving stereotypy. The symmetry is indicative of damped, driven Hamiltonian dynamics underlying the worm’s movement control.
Bounding sets of sequential quantum correlations and device-independent randomness certification

An important problem in quantum information theory is that of bounding sets of correlations that arise from making local measurements on entangled states of arbitrary dimension. Currently, the best-known method to tackle this problem is the NPA hierarchy; an infinite sequence of semidefinite programs that provides increasingly tighter outer approximations to the desired set of correlations. In this work we consider a more general scenario in which one performs sequences of local measurements on an entangled state of arbitrary dimension. We show that a simple adaptation of the original NPA hierarchy provides an analogous hierarchy for this scenario, with comparable resource requirements and convergence properties. We then use the method to tackle some problems in device-independent quantum information. First, we show how one can robustly certify over 2.3 bits of device-independent local randomness from a two-quibt state using a sequence of measurements, going beyond the theoretical maximum of two bits that can be achieved with non-sequential measurements. Finally, we show tight upper bounds to two previously defined tasks in sequential Bell test scenarios.
StructEdit: Learning Structural Shape Variations

Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and for many other applications in 3D content creation. The common approach of encoding shapes as points in a high-dimensional latent feature space suggests treating shape differences as vectors in that space. Instead, we treat shape differences as primary objects in their own right and propose to encode them in their own latent space. In a setting where the shapes themselves are encoded in terms of fine-grained part hierarchies, we demonstrate that a separate encoding of shape deltas or differences provides a principled way to deal with inhomogeneities in the shape space due to different combinatorial part structures, while also allowing for compactness in the representation, as well as edit abstraction and transfer. Our approach is based on a conditional variational autoencoder for encoding and decoding shape deltas, conditioned on a source shape. We demonstrate the effectiveness and robustness of our approach in multiple shape modification and generation tasks, and provide comparison and ablation studies on the PartNet dataset, one of the largest publicly available 3D datasets.
Nearest Neighbor Sampling of Point Sets using Random Rays

We propose a new framework for the sampling, compression, and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces. A set of randomly selected rays are projected onto their closest points in the data set, forming the ray signature. From the signature, statistical information about the data set, as well as certain geometrical information, can be extracted, independent of the ray set. We present promising results from "RayNN", a neural network for the classification of point clouds based on ray signatures.
Fokker-Planck particle systems for Bayesian inference: Computational approaches

Bayesian inference can be embedded into an appropriately defined dynamics in the space of probability measures. In this paper, we take Brownian motion and its associated Fokker--Planck equation as a starting point for such embeddings and explore several interacting particle approximations. More specifically, we consider both deterministic and stochastic interacting particle systems and combine them with the idea of preconditioning by the empirical covariance matrix. In addition to leading to affine invariant formulations which asymptotically speed up convergence, preconditioning allows for gradient-free implementations in the spirit of the ensemble Kalman filter. While such gradient-free implementations have been demonstrated to work well for posterior measures that are nearly Gaussian, we extend their scope of applicability to multimodal measures by introducing localised gradient-free approximations. Numerical results demonstrate the effectiveness of the considered methodologies.
Towards Understanding the Information Ecosystem Through the Lens of Multiple Web Communities

The Web consists of numerous Web communities, news sources, and services, which are often exploited by various entities for the dissemination of false information. Yet, we lack tools and techniques to effectively track the propagation of information across the multiple diverse communities, and to model the interplay and influence between them. Also, we lack an understanding of what the role and impact of emerging communities and services on the Web are, and how such communities are exploited by bad actors that spread false and weaponized information. In this thesis, we study the information ecosystem on the Web by presenting a typology that includes the various types of false information, the involved actors and their possible motives. Then, we follow a data-driven cross-platform quantitative approach to analyze billions of posts from Twitter, Reddit, 4chan's /pol/, and Gab, to shed light on: 1) how news and memes travel from one Web community to another and how we can model and quantify the influence between Web communities; 2) characterizing the role of emerging Web communities and services on the Web, by studying Gab and two Web archiving services, namely the Wayback Machine and archive.is; and 3) how popular Web communities are exploited by state-sponsored actors for the purpose of spreading disinformation. Our analysis reveal that fringe Web communities like 4chan's /pol/ and The_Donald subreddit have a disproportionate influence on mainstream communities like Twitter with regard to the dissemination of news and memes. We find that Gab acts as the new hub for the alt-right community, while for Web archiving services we find that they can be misused to penalize ad revenue from news sources with conflicting ideology. Finally, when studying state-sponsored actors, we find that they were particularly influential in spreading news on popular communities like Twitter and Reddit.
Primary decompositions of unital locally matrix algebras

We construct a unital locally matrix algebra of uncountable dimension that (1) does not admit a primary decomposition, (2) has an infinite locally finite Steinitz number. It gives negative answers to questions from [V. M. Kurochkin, On the theory of locally simple and locally normal algebras, Mat. Sb. , Nov. Ser. 22(64)(3) (1948) 443–454; O. Bezushchak and B. Oliynyk, Unital locally matrix algebras and Steinitz numbers, J. Algebra Appl. (2020), online ready]. We also show that for an arbitrary infinite Steinitz number [Formula: see text] there exists a unital locally matrix algebra [Formula: see text] having the Steinitz number [Formula: see text] and not isomorphic to a tensor product of finite-dimensional matrix algebras.
Omega and Sharpe Ratio

Omega ratio, defined as the probability-weighted ratio of gains over losses at a given level of expected return, has been advocated as a better performance indicator compared to Sharpe and Sortino ratio as it depends on the full return distribution and hence encapsulates all information about risk and return. We compute Omega ratio for the normal distribution and show that under some distribution symmetry assumptions, the Omega ratio is oversold as it does not provide any additional information compared to Sharpe ratio. Indeed, for returns that have elliptic distributions, we prove that the optimal portfolio according to Omega ratio is the same as the optimal portfolio according to Sharpe ratio. As elliptic distributions are a weak form of symmetric distributions that generalized Gaussian distributions and encompass many fat tail distributions, this reduces tremendously the potential interest for the Omega ratio.
Quasicrystalline Chern insulators

Chern insulator or quantum anomalous Hall state is a topological state with integer Hall conductivity but in absence of Landau level. It had been well established on various two-dimensional lattices with periodic structure. Here, we report similar Chern insulators can also be realized on the quasicrystal with $5$-fold rotational symmetry. Providing the staggered flux through plaquettes, we propose two types of quasicrystalline Chern insulators. Their topological characterizations are well identified by the robustness of edge states, non-zero real-space Chern number, and quantized conductance. We further find the failure of integer conductivity but with quantized Chern number at some special energies. Our study therefore provide a new opportunity to searching topological materials in aperiodic system.
AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces

Humans can easily detect a defect (anomaly) because it is different or salient when compared to the surface it resides on. Today, manual human visual inspection is still the norm because it is difficult to automate anomaly detection. Neural networks are a useful tool that can teach a machine to find defects. However, they require a lot of training examples to learn what a defect is and it is tedious and expensive to get these samples. We tackle the problem of teaching a network with a low number of training samples with a system we call AnoNet. AnoNet's architecture is similar to CompactCNN with the exceptions that (1) it is a fully convolutional network and does not use strided convolution; (2) it is shallow and compact which minimizes over-fitting by design; (3) the compact design constrains the size of intermediate features which allows training to be done without image downsizing; (4) the model footprint is low making it suitable for edge computation; and (5) the anomaly can be detected and localized despite the weak labelling. AnoNet learns to detect the underlying shape of the anomalies despite the weak annotation as well as preserves the spatial localization of the anomaly. Pre-seeding AnoNet with an engineered filter bank initialization technique reduces the total samples required for training and also achieves state-of-the-art performance. Compared to the CompactCNN, AnoNet achieved a massive 94% reduction of network parameters from 1.13 million to 64 thousand parameters. Experiments were conducted on four data-sets and results were compared against CompactCNN and DeepLabv3. AnoNet improved the performance on an average across all data-sets by 106% to an F1 score of 0.98 and by 13% to an AUROC value of 0.942. AnoNet can learn from a limited number of images. For one of the data-sets, AnoNet learnt to detect anomalies after a single pass through just 53 training images.
Towards a composite Higgs and a partially composite top quark

We have calculated quantities of interest to a theory of compositeness. The lattice model, approximating the candidate theory, is the SU(4) gauge theory coupled to fermions in two color representations. For the composite Higgs, a current correlator gives one of the ingredients of the effective Higgs potential. For the partially composite top quark, we have hyperbaryon matrix elements that govern mixing of the fundamental quark with its heavy composite partner. The matrix elements turn out to be so small that the theory is disfavored as a source of a realistic top mass.
Finiteness Theorems for Kac–Moody Groups over Non-archimedean Local Fields

We prove the finiteness of formal analogs of the spherical function (Spherical Finiteness), the ${\textbf c}$-function (Gindikin–Karpelevich Finiteness), and obtain a formal analog of Harish-Chandra’s limit (Approximation Theorem) relating spherical and ${\textbf c}$-function in the setting of $p$-adic Kac–Moody groups. The finiteness theorems imply that the formal analog of the Gindikin–Karpelevich integral is well defined in local Kac–Moody settings. These results extend Braverman–Garland–Kazhdan–Patnaik’s affine Gindikin–Karpelevich finiteness theorems from [ 4] and provide an algebraic analog of the combinatorial results of Gaussent–Rousseau [10] and Hébert [14].
End-to-End Model-Free Reinforcement Learning for Urban Driving Using Implicit Affordances

Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own experiments and not rule-based control methods. However, there is no RL algorithm yet capable of handling a task as difficult as urban driving. We present a novel technique, coined implicit affordances, to effectively leverage RL for urban driving thus including lane keeping, pedestrians and vehicles avoidance, and traffic light detection. To our knowledge we are the first to present a successful RL agent handling such a complex task especially regarding the traffic light detection. Furthermore, we have demonstrated the effectiveness of our method by winning the Camera Only track of the CARLA challenge.
Vegetative propagation of guava through wedge grafting

In the present investigation, defoliation time of scion shoot was evaluated to ascertain the high percentage of grafts and save time as well as labour expenditure for grafting operation. Three defoliation time of scion i.e. 0 (day on the grafting) 4 and 8 days prior to grafting was tried throughout the investigation period. Out of three periods of scion defoliation prior to grafting operation, not much significant difference was observed in respect of success percentage of grafts either in greenhouse as well as open field conditions. However, defoliation of scion on the day of grafting and 8 days prior to grafting gave better success over the scion defoliated 4 days prior to grafting. In case of scion defoliated 8 days prior to grafting, the success ranged between 90–99 per cent in the month of October to March. Among the different verities, the maximum success of grafts was observed in Sardar closely followed by Allahabad Safeda and Lalit, respectively. Similarly, among different months of grafting operations, when the grafting operation was performed during April, May, October, November, December, January and February gave higher successes (94.5–99.9%) specially on scion defoliated on the day of grafting followed by 8 days prior to grafting in green house. Winter season was found to be much conscience for higher success of grafting irrespective of time of scion defoliation. The Capping of grafts was found to be instrumental for higher success of grafts either in greenhouse as well as in open field conditions. In Allahabad Safeda and Sardar 99% success of graft was recorded when grafting was performed in the month of October, November, February and March in greenhouse, Similarly, in open field conditions, the success of grafts ranged from 28–99% being maximum (99%) in all 3 cultivars when grafting was performed in the month of February. Utilizing scion shoots which was defoliated on the day of grafting with capping of grafts with polytube showed excellent performance towards success of grafts either in greenhouse as well as in open field conditions especially during winter season. It has been proved that without polyethylene cap, it not possible to enhanced success percentage of grafts either in greenhouse as well as in open field conditions. Overall, enhancement of graft percentage was more in greenhouse as compare to open field condition. Utilizing polycap exhibited better success percentage of grafts even under greenhouse as compared to without covering the grafts with polycap.
Quasi-uniform gravitational field of a disk revisited

We calculate the quasi-uniform gravitational field of a disk in the weak-field approximation and demonstrate an inappropriateness of preceding results. The Riemann tensor of this field is determined. The nonexistence of the uniform gravitational field is proven without the use of the weak-field approximation. The previously found difference between equations of motion for the momentum and spin in the accelerated frame and in the quasi-uniform gravitational field also takes place for the disk. However, it does not violate the Einstein equivalence principle because of the nonexistence of the uniform gravitational field.
On the volume of the shrinking branching Brownian sausage

The branching Brownian sausage in R was defined in [4] similarly to the classical Wiener sausage, as the random subset of R scooped out by moving balls of fixed radius with centers following the trajectories of the particles of a branching Brownian motion (BBM). We consider a d-dimensional dyadic BBM, and study the large-time asymptotic behavior of the volume of the associated branching Brownian sausage (BBM-sausage) with radius exponentially shrinking in time. Using a previous result on the density of the support of BBM, and some well-known results on the classical Wiener sausage and Brownian hitting probabilities, we obtain almost sure limit theorems as time tends to infinity on the volume of the shrinking BBM-sausage in all dimensions.
THE ROLE OF MICROFINANCE INSTITUTIONS ON POVERTY ALLEVIATION IN ETHIOPIA

Microfinance can be a critical element of an effective poverty reduction strategy mostly for developing countries. Particularly in Ethiopia there is lack of improved access and efficient provision of savings, credit, and insurance facilities to develop their businesses, enhance their income earning capacity, and enjoy an improved quality of life. The objectives of this paper are to asses the role of MFI (DECSI) on poverty reduction, to evaluate how the MFI (DECSI) helps to change their (poor) economy and to point out the significance of the provision of MF service to brake grinding cycle of poverty. In this study both secondary and primary data via self-administered questionnaires and interviews were used. Following the information collected from both MFI’s managers and their clients, it was revealed that MFI have changed the life of poor people in a positive way. Despite these achievements it was further observed that some conditions like high interest rates, loan application process & approval, collateral, service delivery and lack of close relationship between institution management and the borrowers have been limiting factors for poor people to access the
Co-spatial velocity and magnetic swirls in the simulated solar photosphere

Context. Velocity or intensity swirls have now been shown to be widely present throughout the photosphere and chromosphere. It has been suggested that these events could contribute to the heating of the upper solar atmosphere, via exciting Alfvén pulses, which could carry significant amounts of energy. However, the conjectured necessary physical conditions for their excitation, that the magnetic field rotates co-spatially and co-temporally with the velocity field, has not been verified. Aims. We aim to understand whether photospheric velocity swirls exist co-spatially and co-temporally with photospheric magnetic swirls, in order to demonstrate the link between swirls and pulses. Methods. The automated swirl detection algorithm (ASDA) is applied to the photospheric horizontal velocity and vertical magnetic fields obtained from a series of realistic numerical simulations using the radiative magnetohydrodynamics (RMHD) code Bifrost. The spatial relationship between the detected velocity and magnetic swirls is further investigated via a well-defined correlation index (CI) study. Results. On average, there are ∼63 short-lived photospheric velocity swirls (with lifetimes mostly less than 20 s, and average radius of ∼37 km and rotating speeds of ∼2.5 km s−1) detected in a field of view (FOV) of 6 × 6 Mm−2, implying a total population of velocity swirls of ∼1.06 × 107 in the solar photosphere. More than 80% of the detected velocity swirls are found to be accompanied by local magnetic concentrations in intergranular lanes. On average, ∼71% of the detected velocity swirls have been found to co-exist with photospheric magnetic swirls with the same rotating direction. Conclusions. The co-temporal and co-spatial rotation in the photospheric velocity and magnetic fields provide evidence that the conjectured condition for the excitation of Alfvén pulses by photospheric swirls is fulfilled.
The increasing doses of methotrexate pharmacokinetics after intravenous administration in rats - model selection

Background/Aim. Methotrexate (MTX) plays a significant role in the treatment of various diseases, but the toxicity remains the main issue of its use, especially when administered in high doses. Considering altered pharmacokinetics of MTX as a factor strongly implicated in the large interpatient variability and unexpected toxicity in certain patients, the accurate description of MTX pharmacokinetic behaviour of both low and high doses is of the utmost importance. Therefore, the objective of this study was to determine the pharmacokinetics of MTX after intravenous (iv) administration in ascending doses of 5, 40, 80 and 160 mg/kg in rats and to select the appropriate mathematical model describing MTX pharmacokinetics. Methods. Plasma concentrations of MTX were measured using the liquid chromatography - mass spectrometry (LC/MS) method. Pharmacokinetic parameters were calculated by non-compartmental and two-compartmental integer-order analyses. Results. MTX showed linear pharmacokinetics following iv administration up to the dose of 80 mg/kg. The administration of a high dose of MTX (160 mg/kg) resulted in the similar pharmacokinetic behaviour as when applied in the twice lower dose (80 mg/kg), which can be explained by dose-dependent changes in the expression of solute carrier (SLC) and ATP binding cassette (ABC) transport proteins and intracellular metabolism. Furthermore, the classical two-compartment model could not explain the pharmacokinetics of MTX in a small percentage of experimental animals, which opens up new strategies for the use of fractional order pharmacokinetic models in MTX therapy optimisation. Conclusion. These results of pharmacokinetic analyses may be helpful in adjusting the dosage regimen of MTX, but the application of novel pharmacokinetic models, such as those based on fractional calculus, is still needed in the process of MTX therapy optimisation.