Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Better Understanding of the Relationships between Galectin-7, p53 and MMP-9 during Cancer Progression

It has been almost 25 years since the discovery of galectin-7. This member of the galectin family has attracted interest from many working in the cancer field given its highly restricted expression profile in epithelial cells and the fact that cancers of epithelial origin (carcinoma) are among the most frequent and deadly cancer subtypes. Initially described as a p53-induced gene and associated with apoptosis, galectin-7 is now recognized as having a protumorigenic role in many cancer types. Several studies have indeed shown that galectin-7 is associated with aggressive behavior of cancer cells and induces expression of MMP-9, a member of the matrix metalloproteinases (MMP) family known to confer invasive behavior to cancer cells. It is therefore not surprising that many studies have examined its relationships with p53 and MMP-9. However, the relationships between galectin-7 and p53 and MMP-9 are not always clear. This is largely because p53 is often mutated in cancer cells and such mutations drastically change its functions and, consequently, its association with galectin-7. In this review, we discuss the functional relationships between galectin-7, p53 and MMP-9 and reconcile some apparently contradictory observations. A better understanding of these relationships will help to develop a working hypothesis and model that will provide the basis for further research in the hope of establishing a new paradigm for tackling the role of galectin-7 in cancer.
A Low-Complexity Antenna Selection Algorithm for Quadrature Spatial Modulation Systems

In this work, an efficient transmit antenna selection approach for the quadrature spatial modulation (QSM) systems is proposed. The conventional Euclidean distance antenna selection (EDAS)-based schemes in QSM have too high computational complexity for practical use. The proposed antenna selection algorithm is based on approximation of the EDAS decision metric employed for QSM. The elimination of imaginary parts in the decision metric enables decoupling of the approximated decision metric, which enormously reduces the complexity. The proposed method is also evaluated via simulations in terms of symbol error rate (SER) performance and compared with the conventional EDAS methods in QSM systems.
Poliovirus Vaccination Induces a Humoral Immune Response That Cross Reacts With SARS-CoV-2

Background: Millions have been exposed to SARS-CoV-2, but the severity of resultant infections has varied among adults and children, with adults presenting more serious symptomatic cases. Children may possess an immunity that adults lack, possibly from childhood vaccinations. This retrospective study suggests immunization against the poliovirus may provide an immunity to SARS-CoV-2. Methods: Publicly available data were analyzed for possible correlations between national median ages and epidemiological outbreak patterns across 100 countries. Sera from 204 adults and children, who were immunized with the poliovirus vaccine, were analyzed using an enzyme-linked immunosorbent assay. The effects of polio-immune serum on SARS-CoV-2-induced cytopathology in cell culture were then evaluated. Results: Analyses of median population age demonstrated a positive correlation between median age and SARS-CoV-2 prevalence and death rates. Countries with effective poliovirus immunization protocols and younger populations have fewer and less pathogenic cases of COVID-19. Antibodies to poliovirus and SARS-CoV-2 were found in pediatric sera and in sera from adults recently immunized with polio. Sera from polio-immunized individuals inhibited SARS-CoV-2 infection of Vero cell cultures. These results suggest the anti-D3-pol-antibody, induced by poliovirus vaccination, may provide a similar degree of protection from SARS-CoV-2 to adults as to children. Conclusions: Poliovirus vaccination induces an adaptive humoral immune response. Antibodies created by poliovirus vaccination bind the RNA-dependent RNA polymerase (RdRp) protein of both poliovirus and SARS-CoV-2, thereby preventing SARS-CoV-2 infection. These findings suggest proteins other than “spike” proteins may be suitable targets for immunity and vaccine development.
Evolution of the Angular Momentum during Gravitational Fragmentation of Molecular Clouds

We investigate the origin of the observed scaling j ∼ R 3/2 between the specific angular momentum j and the radius R of molecular clouds (MCs) and their their substructures, and of the observed near independence of β, the ratio of rotational to gravitational energy, from R. To this end, we measure the angular momentum (AM) of “Lagrangian” particle sets in a smoothed particle hydrodynamics (SPH) simulation of the formation, collapse, and fragmentation of giant MCs. The Lagrangian sets are initially defined as connected particle sets above a certain density threshold at a certain time t def, and then the same set of SPH particles is followed either forward or backward in time. We find the following. (i) The Lagrangian particle sets evolve along the observed j–R relation when the volume containing them also contains a large number of other “intruder” particles. Otherwise, they evolve with j ∼ cst. (ii) Tracking Lagrangian sets to the future, we find that a subset of the SPH particles participates in the collapse, while the rest disperses away. (iii) These results suggest that the Lagrangian sets of fluid particles exchange their AM with other neighboring fluid particles via turbulent viscosity. (iv) We conclude that the j–R relation arises from a global tendency toward gravitational contraction, mediated by AM loss via turbulent viscosity, which induces fragmentation into dense, low-AM clumps, and diffuse, high-AM envelopes, which disperse away, limiting the mass efficiency of the fragmentation.
A
-type antiferromagnetic order and magnetic phase diagram of the trigonal Eu spin-
72
 triangular-lattice compound 
EuSn2As2

The trigonal compound EuSn2As2 was recently discovered to host Dirac surface states within the bulk band gap and orders antiferromagnetically below the Néel temperature TN ≈ 24 K. Here the magnetic ground state of single-crystal EuSn2As2 and the evolution of its properties versus temperature T and applied magnetic field H are reported. Included are zero-field singlecrystal neutron diffraction measurements versus T , magnetization M(H,T ), magnetic susceptibility χ(H,T ) = M(H,T )/H, heat capacity Cp(H,T ), and electrical resistivity ρ(H,T ) measurements. The neutron-diffraction and χ(T ) measurements both indicate a collinear A-type antiferromagnetic (AFM) structure below TN = 23.5(2) K, where the Eu 2+ spins S = 7/2 in a triangular ab-plane layer (hexagonal unit cell) are aligned ferromagnetically in the ab plane whereas the spins in adjacent Eu planes along the c axis are aligned antiferromagnetically. The χ(Hab, T ) and χ(Hc, T ) data together indicate a smooth crossover between the collinear AFM alignment and an unknown magnetic structure at H ∼ 0.15 T. Dynamic spin fluctuations up to 60 K are evident in the χ(T ), Cp(T ) and ρ(H,T ) measurements, a temperature that is more than twice TN. The ρ(H,T ) of the compound does not reflect a contribution of the topological state, but rather is consistent with a low-carrier-density metal with strong magnetic scattering. The magnetic phase diagrams for both H ‖ c and H ‖ ab in the H-T plane are constructed from the TN(H), χ(H,T ), Cp(H,T ), and ρ(H,T ) data.
Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection

This report describes our systems submitted to the DCASE2021 challenge task 3: sound event localization and detection (SELD) with directional interference. Our previous system based on activity-coupled Cartesian direction of arrival (ACCDOA) representation enables us to solve a SELD task with a single target. This ACCDOA-based system with efficient network architecture called RD3Net and data augmentation techniques outperformed state-of-the-art SELD systems in terms of localization and location-dependent detection. Using the ACCDOA-based system as a base, we perform model ensembles by averaging outputs of several systems trained with different conditions such as input features, training folds, and model architectures. We also use the event independent network v2 (EINV2)-based system to increase the diversity of the model ensembles. To generalize the models, we further propose impulse response simulation (IRS), which generates simulated multi-channel signals by convolving simulated room impulse responses (RIRs) with source signals extracted from the original dataset. Our systems significantly improved over the baseline system on the development dataset.
Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning

Unmanned-aerial-vehicle (UAV)-assisted data collection has been emerging as a prominent application due to its flexibility, mobility, and low operational cost. However, under the dynamic and uncertainty of Internet of Things data collection and energy replenishment processes, optimizing the performance for UAV collectors is a very challenging task. Thus, this article introduces a novel framework that jointly optimizes the flying speed and energy replenishment for each UAV to significantly improve the overall system performance (e.g., data collection and energy usage efficiency). Specifically, we first develop a Markov decision process to help the UAV automatically and dynamically make optimal decisions under the dynamics and uncertainties of the environment. Although traditional reinforcement learning algorithms, such as $Q$ -learning and deep $Q$ -learning, can help the UAV to obtain the optimal policy, they often take a long time to converge and require high computational complexity. Therefore, it is impractical to deploy these conventional methods on UAVs with limited computing capacity and energy resource. To that end, we develop advanced transfer learning techniques that allow UAVs to “share” and “transfer” learning knowledge, thereby reducing the learning time as well as significantly improving learning quality. Extensive simulations demonstrate that our proposed solution can improve the average data collection performance of the system up to 200% and reduce the convergence time up to 50% compared with those of conventional methods.
Manipulation-Robust Regression Discontinuity Designs

We present a new identification condition for regression discontinuity designs. We replace the local randomization of Lee (2008) with two restrictions on its threat, namely, the manipulation of the running variable. Furthermore, we provide the first auxiliary assumption of McCrary's (2008) diagnostic test to detect manipulation. Based on our auxiliary assumption, we derive a novel expression of moments that immediately implies the worst-case bounds of Gerard, Rokkanen, and Rothe (2020) and an enhanced interpretation of their target parameters. We highlight two issues: an overlooked source of identification failure, and a missing auxiliary assumption to detect manipulation. In the case studies, we illustrate our solution to these issues using institutional details and economic theories.
Rough stochastic differential equations

We build a hybrid theory of rough stochastic analysis which seamlessly combines the advantages of both It\^o's stochastic and Lyons' rough differential equations. This gives a direct and intrinsic understanding of multidimensional diffusion with Brownian noise $(B,\tilde B)$ $$ dY_t(\omega)=b(\omega,t,Y_t(\omega))dt+\sigma(\omega,t,Y_t(\omega))dB_t(\omega)+f({\omega,t},Y_t(\omega))d\tilde B_t \,, $$ in the annealed form, when conditioned on its environmental noise $\tilde B$. This situation arises naturally e.g. in filtering theory, for Feynman--Kac representations of solutions to stochastic partial differential equations, in Lions--Souganidis' theory of pathwise stochastic control, and for McKean--Vlasov stochastic differential equations with common noise. In fact, we establish well-posedness of rough stochastic differential equations, with $\tilde B$ replaced by a genuine rough path. As consequence, the `annealed' process $Y$ is a locally Lipschitz function of its environmental noise in rough path metrics. There is also interest in taking $\tilde B=\tilde B^H$, a fractional Brownian motion which fits our theory for $H>1/3$. Our assumptions for $b,\sigma$ agree with those from It\^o theory, those for $f$ with rough paths theory, including an extension of Davie's critical regularity result for deterministic rough differential equations. A major role in our analysis is played by a new scale of stochastic controlled rough paths spaces, related to a $(L^m,L^n)$-variant of stochastic sewing.
TeV-scale Lepton Number Violation: Connecting Leptogenesis, Neutrinoless Double Beta Decay, and Colliders

In the context of TeV-scale lepton number violating (LNV) interactions, we illustrate the interplay between leptogenesis, neutrinoless double beta (0νββ) decay, and LNV searches at proton-proton colliders. Using a concrete model for illustration, we identify the parameter space where standard thermal leptogenesis is rendered unviable due to washout processes and show how 0νββ decay and pp collisions provide complementary probes. We find that the new particle spectrum can have a decisive impact on the relative sensitivity of these two probes. ar X iv :2 10 6. 10 83 8v 3 [ he pph ] 2 8 Fe b 20 22
A Comprehensive Review on Non-Neural Networks Collaborative Filtering Recommendation Systems

Over the past two decades, recommender systems have attracted a lot of interest due to the explosion in the amount of data in online applications. A particular attention has been paid to collaborative filtering, which is the most widely used in applications that involve information recommendations. Collaborative filtering (CF) uses the known preference of a group of users to make predictions and recommendations about the unknown preferences of other users (recommendations are made based on the past behavior of users). First introduced in the 1990s, a wide variety of increasingly successful models have been proposed. Due to the success of machine learning techniques in many areas, there has been a growing emphasis on the application of such algorithms in recommendation systems. In this article, we present an overview of the CF approaches for recommender systems, their two main categories, and their evaluation metrics. We focus on the application of classical Machine Learning algorithms to CF recommender systems by presenting their evolution from their first use-cases to advanced Machine Learning models. We attempt to provide a comprehensive and comparative overview of CF systems (with python implementations) that can serve as a guideline for research and practice in this area.
Closing the ultrafine particle number concentration budget at road-to-ambient scale: Implications for particle dynamics

ABSTRACT Freshly emitted vehicle exhaust particles are diluted quickly as they mix into ambient air, but the contribution of evaporation, coagulation, and/or nucleation of new particles to the number concentration has been the subject of some debate. We analyzed one-second time resolution size distribution data from an early morning field campaign, data collected at a time at which dilution has a smaller (but still dominant; ∼70−80%) impact on particle concentrations. Because the plume is diluted over an hour, and a distance of 1500 m, we can constrain the processes with higher accuracy. We find that concentrations in the smaller size bins (5.6–23.7 nm) peak further downwind than the reference particles (42.1–562 nm), and decay significantly faster than larger particles particularly in the area 100−400 m downwind. Comparisons of the cumulative contributions of van der Waals enhanced coagulation, dry deposition, and dilution and the observed decay curves, imply that for up to the first 50–100 m there is nucleation and/or growth of particles smaller than 5.6 nm. In contrast, in the ∼100–400 m region, some of the smaller particles evaporate. In the further downwind areas (>400 m) the particles all appear to decay at rates consistent with the sum of dilution, coagulation, and deposition. We also find that a dry deposition parameterization at the low end of those available in the literature is most consistent with the observational data. © 2016 American Association for Aerosol Research
Catastrophic Cooling in Superwinds. II. Exploring the Parameter Space

Superwinds and superbubbles driven by mechanical feedback from super star clusters (SSCs) are common features in many star-forming galaxies. While the adiabatic fluid model can well describe the dynamics of superwinds, several observations of starburst galaxies revealed the presence of compact regions with suppressed superwinds and strongly radiative cooling, i.e., catastrophic cooling. In the present study, we employ the nonequilibrium atomic chemistry and cooling package MAIHEM, built on the FLASH hydrodynamics code, to generate a grid of models investigating the dependence of cooling modes on the metallicity, SSC outflow parameters, and ambient density. While gas metallicity plays a substantial role, catastrophic cooling is more sensitive to high mass loading and reduced kinetic heating efficiency. Our hydrodynamic simulations indicate that the presence of a hot superbubble does not necessarily imply an adiabatic outflow and vice versa. Using CLOUDY photoionization models, we predict UV and optical line emission for both adiabatic and catastrophic cooling outflows, for radiation-bounded and partially density-bounded models. Although the line ratios predicted by our radiation-bounded models agree well with observations of star-forming galaxies, they do not provide diagnostics that unambiguously distinguish the parameter space of catastrophically cooling flows. Comparison with observations suggests the possibility of minor density bounding, nonequilibrium ionization, and/or observational bias toward the central outflow regions.
Fast galaxy bars continue to challenge standard cosmology

Many observed disc galaxies harbour a central bar. In the standard cosmological paradigm, galactic bars should be slowed down by dynamical friction from the dark matter halo. This friction depends on the galaxy’s physical properties in a complex way, making it impossible to formulate analytically. Fortunately, cosmological hydrodynamical simulations provide an excellent statistical population of galaxies, letting us quantify how simulated galactic bars evolve within dark matter haloes. We measure bar strengths, lengths, and pattern speeds in barred galaxies in state-of-the-art cosmological hydrodynamical simulations of the IllustrisTNG and EAGLE projects, using techniques similar to those used observationally. We then compare our results with the largest available observational sample at redshift z = 0. We show that the tension between these simulations and observations in the ratio of corotation radius to bar length is 12. 62σ (TNG50), 13. 56σ (TNG100), 2. 94σ (EAGLE50), and 9. 69σ (EAGLE100), revealing for the first time that the significant tension reported previously persists in the recently released TNG50. The lower statistical tension in EAGLE50 is actually caused by it only having five galaxies suitable for our analysis, but all four simulations give similar statistics for the bar pattern speed distribution. In addition, the fraction of disc galaxies with bars is similar between TNG50 and TNG100, though somewhat above EAGLE100. The simulated bar fraction and its trend with stellar mass both differ greatly from observations. These dramatic disagreements cast serious doubt on whether galaxies actually have massive cold dark matter haloes, with their associated dynamical friction acting on galactic bars.
Cumulative structure and path length in networks of knowledge

An important knowledge dimension of science and technology is the extent to which their development is cumulative, that is, the extent to which later findings build on earlier ones. Cumulative knowledge structures can be studied using a network approach, in which nodes represent findings and links represent knowledge flows. Of particular interest to those studies is the notion of network paths and path length. Starting from the Price model of network growth, we derive an exact solution for the path length distribution of all unique paths from a given initial node to each node in the network. We study the relative importance of the average in-degree and cumulative advantage effect and implement a generalization where the in-degree depends on the number of nodes. The cumulative advantage effect is found to fundamentally slow down path length growth. As the collection of all unique paths may contain many redundancies, we additionally consider the subset of the longest paths to each node in the network. As this case is more complicated, we only approximate the longest path length distribution in a simple context. Where the number of all unique paths of a given length grows unbounded, the number of longest paths of a given length converges to a finite limit, which depends exponentially on the given path length. Fundamental network properties and dynamics therefore characteristically shape cumulative structures in those networks, and should therefore be taken into account when studying those structures.
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning

Video understanding relies on perceiving the global content and modeling its internal connections (e.g., causality, movement, and spatio-temporal correspondence). To learn these interactions, we apply a mask-then-predict pre-training task on discretized video tokens generated via VQ-VAE. Unlike language, where the text tokens are more independent, neighboring video tokens typically have strong correlations (e.g., consecutive video frames usually look very similar), and hence uniformly masking individual tokens will make the task too trivial to learn useful representations. To deal with this issue, we propose a block-wise masking strategy where we mask neighboring video tokens in both spatial and temporal domains. We also add an augmentation-free contrastive learning method to further capture the global content by predicting whether the video clips are sampled from the same video. We pre-train our model on uncurated videos and show that our pre-trained model can reach state-of-the-art results on several video understanding datasets (e.g., SSV2, Diving48). Lastly, we provide detailed analyses on model scalability and pre-training method design. Code is released at https://github.com/airsplay/vimpac.
Dynamic group testing to control and monitor disease progression in a population

In this paper, we introduce a "discrete-time SIR stochastic block model" that also allows for group testing and interventions on a daily basis. Our model can be regarded as a discrete version of the well-known continuous-time SIR stochastic network model [1] and relies on a specific type of weighted graph to capture the underlying community spread. Given that infection model, we then formulate a dynamic group-testing problem by asking: (a) what is the minimum number of tests needed everyday to identify all infections? and (b) are there nonadaptive group testing strategies that achieve this with vanishing error probability? Our results show that one can leverage the knowledge of the community infection model to compute a lower bound on the number of tests and also inform nonadaptive group testing algorithms, so that they can achieve (almost) the same performance as complete individual testing with a much smaller number of tests. Moreover, these algorithms are order-optimal, under specific conditions.