Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic ion and electron heating on drift instabilities at the bow shock

The analysis of the wave content inside a perpendicular bow shock indicates that heating of ions is related to the Lower-Hybrid-Drift (LHD) instability, and heating of electrons to the Electron-Cyclotron-Drift (ECD) instability. Both processes represent stochastic acceleration caused by the electric field gradients on the electron gyroradius scales, produced by the two instabilities. Stochastic heating is a single particle mechanism where large gradients break adiabatic invariants and expose particles to direct acceleration by the DC- and wave-fields. The acceleration is controlled by function $\chi = m_iq_i^{-1} B^{-2}$div($\mathbf{E}$), which represents a general diagnostic tool for processes of energy transfer between electromagnetic fields and particles, and the measure of the local charge non-neutrality. The identification was made with multipoint measurements obtained from the Magnetospheric Multiscale spacecraft (MMS). The source for the LHD instability is the diamagnetic drift of ions, and for the ECD instability the source is ExB drift of electrons. The conclusions are supported by laboratory diagnostics of the ECD instability in Hall ion thrusters.
Classification of Spam Emails through Hierarchical Clustering and Supervised Learning

Spammers take advantage of email popularity to send indiscriminately unsolicited emails. Although researchers and organizations continuously develop anti-spam filters based on binary classification, spammers bypass them through new strategies, like word obfuscation or image-based spam. For the first time in literature, we propose to classify spam email in categories to improve the handle of already detected spam emails, instead of just using a binary model. First, we applied a hierarchical clustering algorithm to create SPEMC-$11$K (SPam EMail Classification), the first multi-class dataset, which contains three types of spam emails: Health and Technology, Personal Scams, and Sexual Content. Then, we used SPEMC-$11$K to evaluate the combination of TF-IDF and BOW encodings with Naive Bayes, Decision Trees and SVM classifiers. Finally, we recommend for the task of multi-class spam classification the use of (i) TF-IDF combined with SVM for the best micro F1 score performance, $95.39\%$, and (ii) TD-IDF along with NB for the fastest spam classification, analyzing an email in $2.13$ms.
Weakly Nuclear Maps on Real $C^*$-algebras and Quasidiagonality of These Algebras

In this paper, we show that a completely positive linear map is weakly nuclear if and only if its complexification is weakly nuclear. It is shown that a real $C^*$-algebra is exact if and only if its complexification is exact and similar case is provided for the quasidiaginality.
From boundaries to bumps: When closed (extremal) contours are critical

Abstract Invariants underlying shape inference are elusive: A variety of shapes can give rise to the same image, and a variety of images can be rendered from the same shape. The occluding contour is a rare exception: It has both image salience, in terms of isophotes, and surface meaning, in terms of surface normal. We relax the notion of occluding contour and, more accurately, the rim on the object that projects to it, to define closed extremal curves. This new shape descriptor is invariant over different renderings. It exists at the topological level, which guarantees an image-based counterpart. It surrounds bumps and dents, as well as common interior shape components, and formalizes the qualitative nature of bump perception. The invariants are biologically computable, unify shape inferences from shading and specular materials, and predict new phenomena in bump and dent perception. Most important, working at the topological level allows us to capture the elusive aspect of bump boundaries.
Estudo do crescimento e da reprodução de Leptinaria unilamellata (D'orbigny) (Mollusca, Subulinidae) em laboratório

Biological aspects of Leptinaria unlamellata (d'Orbigny, 1835) were studied, such as: onset of sexual maturity, occurrence of self-fertilization and shell length measurement at different stages of development. In isolated specimens of L. unilamellata, sexual maturity was reached at 74 days (minimum time) and 104 days (maximum time). For the grouped specimens, the minimum time was 71 days. The occurrence of self-fertilization was observed in both species, 100% of specimens. The total numberof young per birth varied from one to 22 (average: 7,35 ± 5,78), the total number of offsprings per mollusc varied from two to eight (average: 4,1 ± 1,41) and total number of young per mollusc varied from five to 46 (average: 30,21 ± 8,08). As to the shell length in different growth stages, it was observed that in L. unilamellata, growth rhythm was similar until 15 days for age. After this period grouped specimens showed a high growth rhythm until 105 days after which the growth rhythm of grouped specimens became constant and the growth rhythm isolated specimens became greater. Grouped and isolated specimens reached a maximum size of 18,7 mm and 20,6 mm respectively. In the moment of the sexual maturity, the length of the shell in the individuals that were kept grouped was smaller than the isolated ones for L. unilamellata. In this specie individuals that were kept isolated reach larger length of shell.
A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping

Gait, as a unique biometric feature that can be recognized at a distance, which can be widely applicated in public security. In this paper, we propose a novel model-based gait recognition method, JointsGait, which extracts gait information from human body joints. Early gait recognition methods are mainly based on appearance. The appearance-based features are usually extracted from human body silhouettes, which is not invariant to changes in clothing, and can be subject to drastic variations, due to camera motion or other external factors. In contrast to previous approaches, JointsGait firstly extracted spatio-temporal features using gait graph convolutional networks constructed by 18 2-D joints, which are less interfered by external factors. Then Joints Relationship Pyramid Mapping (JRPM) are proposed to map spatio-temporal gait features into a discriminative feature space with biological advantages according to physical structure and walking habit at various scales. Finally, we research a fusion loss strategy to help the joints features be insensitive to cross-view. Our method is evaluated on large datasets CASIA B. The experimental results show that JointsGait achieves the state-of-art performance, which is less affected by the view variations. Its recognition accuracy is higher than lasted model-based method PoseGait in all walking conditions, even outperforms most of state-of-art appearance-based methods, especially when there is a clothing variation.
Tight Analysis of Asynchronous Rumor Spreading in Dynamic Networks

The asynchronous rumor spreading algorithm propagates a piece of information, the so-called rumor, in a network. Starting with a single informed node, each node is associated with an exponential time clock with rate 1 and calls a random neighbor in order to possibly exchange the rumor. A well-studied parameter associated with the algorithm is the spread time, which is the first time when all nodes of a network are informed with high probability1. We consider the spread time of the algorithm in any dynamic evolving network, [EQUATION], which is a sequence of n-node graphs with the same set of nodes exposed at discrete time step t = 0, 1. We establish upper bounds for the spread time in terms of graph conductance and diligence. For a given connected simple graph G = (V, E), the diligence of cut set [EQUATION] is defined as [EQUATION] where du is the degree of u and [EQUATION] is the average degree of nodes in the one side of the cut with smaller volume (i.e., vol(S) = Σu∈S du). The diligence of G is also defined as ρ(G) = min∅≠S ⊂ V ρ(S). For some positive number ρ, G is called ρ-diligent if ρ(G) ≥ ρ. We show that the spread time of the algorithm in G is bounded by T, where T is the first time that [EQUATION] exceeds C log n, where Φ(G(t)) denotes the conductance of G(t) and C is a specified constant. Moreover, for every [EQUATION], we present a sequence of ρ-diligent graphs G(0), G(1), . where the upper bound matches the spread time up to o(log2 n) factor. We also define the absolute diligence as [EQUATION]. We present upper bound T for the spread time in terms of absolute diligence, which is the first time when [EQUATION]. Similarly, we construct dynamic networks where the upper bound is tight up to a constant and conclude that the spread time is bounded by O(n2) in connected dynamic networks. Additionally, we present striking dichotomies between the spread time of the asynchronous and synchronous algorithms in dynamic networks.
Analytical Theory for Sequence-Specific Binary Fuzzy Complexes of Charged Intrinsically Disordered Proteins.

Intrinsically disordered proteins (IDPs) are important for biological functions. In contrast to folded proteins, molecular recognition among certain IDPs is "fuzzy'" in that their binding and/or phase separation are stochastically governed by the interacting IDPs' amino acid sequences while their assembled conformations remain largely disordered. To help elucidate a basic aspect of this fascinating yet poorly understood phenomenon, the binding of a homo- or hetero-dimeric pair of polyampholytic IDPs is modeled statistical mechanically using cluster expansion. We find that the binding affinities of binary fuzzy complexes in the model correlate strongly with a newly derived simple "jSCD" parameter readily calculable from the pair of IDPs' sequence charge patterns. Predictions by our analytical theory are in essential agreement with coarse-grained explicit-chain simulations. This computationally efficient theoretical framework is expected to be broadly applicable to rationalizing and predicting sequence-specific IDP-IDP polyelectrostatic interactions.
Ambient Solar Radiation‐Induced Photodamage in Marine Bacterioplankton

Abstract— There has been much recent concern about the effects of increased UV radiation at certain locations on the earth's surface. There have been extensive studies of ultraviolet radiation effects on phytoplankton and primary production, yet the effects of UVB upon bacterioplankton have been largely overlooked. Bacteria play a central role in the cycling of nutrients and energy flow to higher trophic levels, serving as both mineralizers and secondary producers that are consumed by higher organisms. We have begun to investigate the induction of DNA photodamage by UVB in marine planktonic communities using a highly specific radioimmunoassay to measure cyclobutane pyrimidine dimers in samples collected from the northern Gulf of Mexico. DNA damage in the bacterioplankton size‐fraction (< 0.8 μ.m) was greater than in the larger eukaryotic size fraction (>0.8 μm <120 μm) in 9 of 10 samples. Diel patterns of dimer accumulation and repair were observed in surface waters over a 48 h period in the bacterioplankton size fraction and in the larger eukaryotic plankton size fraction. Depth profiles of DNA damage in the bacterioplankton size fraction appear to be dependent on surface water mixing. Damage was greatest in surface waters, decreased with depth and could be detected to 10 m in calm seas. No net accumulation of damage was observed in moderate seas, even at the surface. Solar radiation was found to inhibit significantly both 3H‐thymidine and 14C‐leucine incorporation. Ultraviolet B was responsible for approximately half of the total inhibition of 3H‐thymidine incorporation, UVA contributing the other half of the inhibition. The vast majority of 14C‐leucine incorporation inhibition was due to UVB, suggesting that protein synthesis is less affected by UVA. The results demonstrate that direct measures of DNA damage can be made of indigenous planktonic communities and that bacterioplankton are highly susceptible to UVB damage and may serve as a more sensitive indicator of UVR stress than other microorganisms.
S-ADDOPT: Decentralized Stochastic First-Order Optimization Over Directed Graphs

In this letter, we study decentralized stochastic optimization to minimize a sum of smooth and strongly convex cost functions when the functions are distributed over a directed network of nodes. In contrast to the existing work, we use gradient tracking to improve certain aspects of the resulting algorithm. In particular, we propose the <monospace><bold>S-ADDOPT</bold></monospace> algorithm that assumes a stochastic first-order oracle at each node and show that for a constant step-size <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, each node converges linearly inside an error ball around the optimal solution, the size of which is controlled by <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>. For decaying step-sizes <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}$ </tex-math></inline-formula>(1/<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>), we show that <monospace><bold>S-ADDOPT</bold></monospace> reaches the exact solution sublinearly at <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}$ </tex-math></inline-formula>(1/<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>) and its convergence is asymptotically network-independent. Thus the asymptotic behavior of <monospace><bold>S-ADDOPT</bold></monospace> is comparable to the centralized stochastic gradient descent. Numerical experiments over both strongly convex and non-convex problems illustrate the convergence behavior and the performance comparison of the proposed algorithm.
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation

Currently, the divergence in distributions of design and operational data, and large computational complexity are limiting factors in the adoption of CNNs in real-world applications. For instance, person re-identification systems typically rely on a distributed set of cameras, where each camera has different capture conditions. This can translate to a considerable shift between source (e.g. lab setting) and target (e.g. operational camera) domains. Given the cost of annotating image data captured for fine-tuning in each target domain, unsupervised domain adaptation (UDA) has become a popular approach to adapt CNNs. Moreover, state-of-the-art deep learning models that provide a high level of accuracy often rely on architectures that are too complex for real-time applications. Although several compression and UDA approaches have recently been proposed to overcome these limitations, they do not allow optimizing a CNN to simultaneously address both. In this paper, we propose an unexplored direction – the joint optimization of CNNs to provide a compressed model that is adapted to perform well for a given target domain. In particular, the proposed approach performs unsupervised knowledge distillation (KD) from a complex teacher model to a compact student model, by leveraging both source and target data. It also improves upon existing UDA techniques by progressively teaching the student about domain-invariant features, instead of directly adapting a compact model on target domain data. Our method is compared against state-of-the-art compression and UDA techniques, using two popular classification datasets for UDA – Office31 and ImageClef-DA. In both datasets, results indicate that our method can achieve the highest level of accuracy while requiring a comparable or lower time complexity.
Gentlemen on the Road: Effect of Yielding Behavior of Autonomous Vehicle on Pedestrian Head Orientation

Autonomous vehicles can improve pedestrian safety by learning human-like social behaviors (e.g., yielding). We conducted a virtual reality experiment with 39 participants and measured crossing times (seconds) and head orientation (yaw degrees). We manipulated AV yielding behavior (no-yield, slow-yield, and fast-yield) and the AV size (small, medium, and large). Using Dynamic time warping and K-means clustering, we classified the head orientation change of pedestrians by time into 6 clusters of patterns. Results indicate that head orientation change of pedestrians was influenced by AV yielding behavior as well as the size of the AV. Participants fixated on the front most of the time even when the car approached near. Participants changed head orientation most frequently when a large size AV did not yield (no-yield). In post-experiment interviews, participants reported that yielding behavior and size affected their decision to cross and perceived safety. For autonomous vehicles to be perceived as more safe and trustful, vehicle-specific factors such as size and yielding behavior should be considered in the designing process.
Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities.

In this paper we propose a completely new approach to the problem of covariate selection in linear regression. It is intuitive, very simple, fast and powerful, non-frequentist and non-Bayesian. It does not overfit, there is no shrinkage of the least squares coefficients, and it is model-free. A covariate or a set of covariates is included only if they are better in the sense of least squares than the same number of Gaussian covariates consisting of i.i. d. $N(0,1)$ random variables. The degree to which they are better is measured by the P-value which is the probability that the Gaussian covariates are better. This probability is given in terms of the Beta distribution, it is exact and it holds for the data at hand whatever this may be. The idea extends to a stepwise procedure, the main contribution of the paper, where the best of the remaining covariates is only accepted if it is better than the best of the same number of random Gaussian covariates. Again this probability is given in terms of the Beta distribution, it is exact and it holds for the data at hand whatever this may be. We use a version with default parameters which works for a large collection of known data sets with up to a few hundred thousand covariates. The computing time for the largest data sets was about four seconds, and it outperforms all other selection procedures of which we are aware. The paper gives the results of simulations, applications to real data sets and theorems on the asymptotic behaviour under the standard linear model. An R-package {\it gausscov} is available. \
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study

As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large sets of labeled data limits the application of such methods. As an example, high-quality delineations of regions of interest in the field of pathology is a tedious and time-consuming task due to the large image dimensions. In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning. We investigated the notion of similarity and dissimilarity in pathology whole-slide images and compared different setups from unsupervised and semi-supervised to supervised learning in our experiments. Additionally, different approaches were tested, applying few-shot learning on two publicly available pathology image datasets. We achieved high accuracy and generalization when the learned representations were applied to two different pathology datasets.
Escaping an epidemic: Movement optimization via Minority Game

We study an optimal movement strategy, for a population during an ongoing epidemic, among the ($D$) states/regions of a country having various levels of infection spreading. If a fraction of the agents in the population have a choice of staying in any two given states, we show that the total infected population in the country could be as low as that achieved with a complete stoppage of inter-state movements for a prolonged period, provided that the agents instead follow a stochastic strategy, used in the Minority Game, for their movement decisions between their two choices. Due to the random (but fixed in time) choices of the agents, the system essentially forms a set of $D/2$ coupled Minority Games, played simultaneously. The objective for an agent is to stay in the lower infected state between their two choices. We further show that it is the agents moving once between any two states, following the stochastic strategy, who are less likely to be infected than those not having (or not opting for) such a movement choice, when the risk of getting infected during the travel is not considered. This shows the incentive for the moving agents to follow the stochastic strategy. For the non-moving agents, the strategy is still helpful, as a complete random movement of the moving group results in a significantly higher total infection.
A Comparative Exploration of ML Techniques for Tuning Query Degree of Parallelism

There is a large body of recent work applying machine learning (ML) techniques to query optimization and query performance prediction in relational database management systems (RDBMSs). However, these works typically ignore the effect of \textit{intra-parallelism} -- a key component used to boost the performance of OLAP queries in practice -- on query performance prediction. In this paper, we take a first step towards filling this gap by studying the problem of \textit{tuning the degree of parallelism (DOP) via ML techniques} in Microsoft SQL Server, a popular commercial RDBMS that allows an individual query to execute using multiple cores. In our study, we cast the problem of DOP tuning as a {\em regression} task, and examine how several popular ML models can help with query performance prediction in a multi-core setting. We explore the design space and perform an extensive experimental study comparing different models against a list of performance metrics, testing how well they generalize in different settings: $(i)$ to queries from the same template, $(ii)$ to queries from a new template, $(iii)$ to instances of different scale, and $(iv)$ to different instances and queries. Our experimental results show that a simple featurization of the input query plan that ignores cost model estimations can accurately predict query performance, capture the speedup trend with respect to the available parallelism, as well as help with automatically choosing an optimal per-query DOP.