Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting the cosmic web: Lyα emission from simulated filaments at z = 3

The standard cosmological model ($\Lambda$CDM) predicts the existence of the cosmic web: a distribution of matter into sheets and filaments connecting massive halos. However, observational evidence has been elusive due to the low surface brightness of the filaments. Recent deep MUSE/VLT data and upcoming observations offer a promising avenue for Ly$\alpha$ detection, motivating the development of modern theoretical predictions. We use hydrodynamical cosmological simulations run with the AREPO code to investigate the potential detectability of large-scale filaments, excluding contributions from the halos embedded in them. We focus on filaments connecting massive ($M_{200c}\sim(1-3)\times10^{12} M_\odot$) halos at z=3, and compare different simulation resolutions, feedback levels, and mock-image pixel sizes. We find increasing simulation resolution does not substantially improve detectability notwithstanding the intrinsic enhancement of internal filament structure. By contrast, for a MUSE integration of 31 hours, including feedback increases the detectable area by a factor of $\simeq$5.5 on average compared with simulations without feedback, implying that even the non-bound components of the filaments have substantial sensitivity to feedback. Degrading the image resolution from the native MUSE scale of (0.2")$^2$ per pixel to (5.3")$^2$ apertures has the strongest effect, increasing the detectable area by a median factor of $\simeq$200 and is most effective when the size of the pixel roughly matches the width of the filament. Finally, we find the majority of Ly$\alpha$ emission is due to electron impact collisional excitations, as opposed to radiative recombination.
A note on the parametric integer programming in the average case: sparsity, proximity, and FPT-algorithms

We consider the Integer Linear Programming (ILP) problem $\max\{c^\top x : A x \leq b,\, x \in Z^n \}$, parameterized by a right-hand side vector $b \in Z^m$, where $A \in Z^{m \times n}$ is a matrix of the rank $n$. Let $v$ be an optimal vertex of the Linear Programming (LP) relaxation $\max\{c^\top x : A x \leq b\}$ and $B$ be a corresponding optimal base. We show that, for almost all $b \in Z^m$, an optimal point of the square ILP problem $\max\{c^\top x : A_B x \leq b_B,\, x \in Z^n \}$ satisfies the constraints $A x \leq b$ of the original problem. From works of R. Gomory it directly follows that the square ILP problem $\max\{c^\top x : A_B x \leq b_B,\, x \in Z^n \}$ can be solved by an algorithm of the arithmetic complexity $O(n \cdot \delta \cdot \log \delta)$, where $\delta = |\det A_B|$. Consequently, it can be shown that, for almost all $b \in Z^m$, the original problem $\max\{c^\top x : A x \leq b,\, x \in Z^n \}$ can be solved by an algorithm of the arithmetic complexity $O(n \cdot \Delta \cdot \log \Delta)$, where $\Delta$ is the maximum absolute value of $n \times n$ minors of $A$. By the same technique, we give new inequalities on the integrality gap and sparsity of a solution and slack variables. Another ingredient is a known lemma that states the equality of the maximum absolute values of rank minors of matrices with orthogonal columns. This lemma gives us a way to transform ILP problems of the type $\max\{c^\top x : A x = b,\, x \in Z^n_+\}$ to problems of the previous type, here we assume that $rank(A) = m$ and all the $m \times m$ minors of $A$ are coprime. Consequently, it follows that, for almost all $b \in Z^m$, there exists an algorithm with the arithmetic complexity $O((n-m) \cdot \Delta \cdot \log \Delta)$ to solve the problem in the equality form. Sparsity and integrality gap bounds are also presented.
On the Compressive Power of Boolean Threshold Autoencoders

An autoencoder is a layered neural network whose structure can be viewed as consisting of an encoder, which compresses an input vector to a lower dimensional vector, and a decoder, which transforms the low-dimensional vector back to the original input vector (or one that is very similar). In this article, we explore the compressive power of autoencoders that are Boolean threshold networks by studying the numbers of nodes and layers that are required to ensure that each vector in a given set of distinct input binary vectors is transformed back to its original. We show that for any set of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> distinct vectors there exists a seven-layer autoencoder with the optimal compression ratio, (i.e., the size of the middle layer is logarithmic in <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>), but that there is a set of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> vectors for which there is no three-layer autoencoder with a middle layer of logarithmic size. In addition, we present a kind of tradeoff: if the compression ratio is allowed to be considerably larger than the optimal, then there is a five-layer autoencoder. We also study the numbers of nodes and layers required only for encoding, and the results suggest that the decoding part is the bottleneck of autoencoding. For example, there always is a three-layer Boolean threshold encoder that compresses <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> vectors into a dimension that is twice the logarithm of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>.
Subcarrier Assignment and Power Allocation for SCMA Energy Efficiency

In this paper we propose a resource allocation algorithm for uplink sparse code multiple access (SCMA) networks to maximize the energy efficiency (EE). Due to the joint optimization of factor graph matrix and power allocation matrix, the EE maximization is a non-convex mixed-integer non-linear programming (MINLP) problem. After transforming the non-convex form of the uplink sum rate to a convex one and separating subcarrier assignment and power allocation, we propose an energy efficient subcarrier assignment algorithm. By applying the fractional programming theory based on the Dinkelbach method, we then propose power allocation algorithm to maximize EE. Finally, the simulation results show that the proposed resource allocation algorithm can significantly increase the EE of the uplink SCMA network.
Shaping for Construction D Lattices

In this paper we propose a practical and general method for shaping a coding lattice $\Lambda_{c}$ obtained by the construction D. The proposal assumes only that the coarse/shaping lattice $\Lambda_{s}$ is an integer lattice. To achieve this, a new way to encode and index a construction-D Voronoi lattice code is presented which has linear complexity in the block length. We test the performance of our proposal using a construction-D lattice obtained from an extended BCH code, and shaping is performed by a Gosset Lattice ($E_8$). This results in a shaping gain of 0.54 dB, which is close to the expected gain of the $E_8$, 0.65 dB.
On Lieb–Thirring inequalities for one-dimensional non-self-adjoint Jacobi and Schrödinger operators

We study to what extent Lieb--Thirring inequalities are extendable from self-adjoint to general (possibly non-self-adjoint) Jacobi and Schrodinger operators. Namely, we prove the conjecture of Hansmann and Katriel from [Complex Anal. Oper. Theory 5, No. 1 (2011), 197-218] and answer another open question raised therein. The results are obtained by means of asymptotic analysis of eigenvalues of discrete Schrodinger operators with rectangular barrier potential and complex coupling. Applying the ideas in the continuous setting, we also solve a similar open problem for one-dimensional Schrodinger operators with complex-valued potentials published by Demuth, Hansmann, and Katriel in [Integral Equations Operator Theory 75, No. 1 (2013), 1-5].
Dark matter properties through cosmic history

We perform the first test of Dark Matter (DM) stress-energy evolution through cosmic history, using Cosmic Microwave Background measurements supplemented with Baryon Acoustic Oscillation data and the Hubble Space Telescope key project data. We constrain the DM equation of state (EoS) in 8 redshift bins, and its sound speed and (shear) viscosity in 9 redshift bins, finding no convincing evidence for non-$\Lambda$CDM values in any of the redshift bins. Despite this enlarged parameter space, the sound speed and viscosity are constrained relatively well at late times (due to the inclusion of CMB lensing), whereas the EoS is most strongly constrained around recombination. These results constrain for the first time the level of ''coldness'' required of DM across various cosmological epochs at both the background and perturbative levels. We show that simultaneously allowing time dependence for both the EoS and sound speed parameters shifts the posterior of the DM abundance before recombination to a higher value, whilst keeping the present day DM abundance similar to the $\Lambda$CDM value. This shifts the posterior for the present day Hubble constant compared to $\Lambda$CDM, suggesting that DM with time-dependent parameters is well-suited to explore possible solutions to persistent tensions within the $\Lambda$CDM model.
Determining optimum plant population densities for three annual green manure crops under weedy and weed-free conditions

ii ACKNOWLEDGEMENTS. iv DEDICATION . v TABLE OF CONTENTS. vi LIST OF TABLES. viii LIST OF FIGURES. x 1.0 INTRODUCTION . 1 2.0 LITERATURE REVIEW. 4 2.1 CLASSIFICATION OF GREEN MANURE CROPS . 4 2.2 BENEFITS OF GREEN MANURE CROPS . 5 2.2.1 Nutrient management.5 2.2.2 Soil improvement.11 2.2.3 Weed control.13 2.3 PROBLEMS WITH GREEN MANURE CROPS . 14 2.3. 1 Excessive water use . 15 2.3. 2 Poor competition with weeds . 17 2.4 GREEN MANURE MANAGEMENT.17 2.4. 1 Time of termination . 17 2.4. 2 Termination method.18 2.4. 3 Annual green manure plant population densities . 19 2.5 DETERMINING RECOMMENDED PLANT POPULATION DENSITIES . 21 2.5. 1 Maximizing biological output . 21 2.5. 2 Optimizing economic output.24 2.6 RELEVANCE OF GREEN MANURE RESEARCH TO ORGANIC CROPPING SYSTEMS.25 3.0 THE YIELD-DENSITY RELATIONSHIP OF CROP BIOMASS FOR THREE ANNUAL LEGUME GREEN MANURE CROPS UNDER WEEDY AND WEED-FREE CONDITIONS . 27 3.
Fast and accurate approximations to fractional powers of operators

In this paper we consider some rational approximations to the fractional powers of self-adjoint positive operators, arising from the Gauss–Laguerre rules. We derive practical error estimates that can be used to select a priori the number of Laguerre points necessary to achieve a given accuracy. We also present some numerical experiments to show the effectiveness of our approaches and the reliability of the estimates.
Strong Consistency, Graph Laplacians, and the Stochastic Block Model

Spectral clustering has become one of the most popular algorithms in data clustering and community detection. We study the performance of classical two-step spectral clustering via the graph Laplacian to learn the stochastic block model. Our aim is to answer the following question: when is spectral clustering via the graph Laplacian able to achieve strong consistency, i.e., the exact recovery of the underlying hidden communities? Our work provides an entrywise analysis (an $\ell_{\infty}$-norm perturbation bound) of the Fielder eigenvector of both the unnormalized and the normalized Laplacian associated with the adjacency matrix sampled from the stochastic block model. We prove that spectral clustering is able to achieve exact recovery of the planted community structure under conditions that match the information-theoretic limits.
Why do People Share Misinformation during the COVID-19 Pandemic?

The World Health Organization have emphasised that misinformation - spreading rapidly through social media - poses a serious threat to the COVID-19 response. Drawing from theories of health perception and cognitive load, we develop and test a research model hypothesizing why people share unverified COVID-19 information through social media. Our findings suggest a person's trust in online information and perceived information overload are strong predictors of unverified information sharing. Furthermore, these factors, along with a person's perceived COVID-19 severity and vulnerability influence cyberchondria. Females were significantly more likely to suffer from cyberchondria, however, males were more likely to share news without fact checking their source. Our findings suggest that to mitigate the spread of COVID-19 misinformation and cyberchondria, measures should be taken to enhance a healthy skepticism of health news while simultaneously guarding against information overload.
Scale-free Collaborative Protocol Design for Synchronization of Homogeneous and Heterogeneous Discrete-time Multi-agent Systems

This paper studies synchronization of homogeneous and heterogeneous discrete-time multi-agent systems. A class of linear dynamic protocol design methodology is developed based on localized information exchange with neighbors which does not need any knowledge of the directed network topology and the spectrum of the associated Laplacian matrix. The main contribution of this paper is that the proposed protocols are scale-free and achieve synchronization for arbitrary number of agents.
Grounding Conversations with Improvised Dialogues

Effective dialogue involves grounding, the process of establishing mutual knowledge that is essential for communication between people. Modern dialogue systems are not explicitly trained to build common ground, and therefore overlook this important aspect of communication. Improvisational theater (improv) intrinsically contains a high proportion of dialogue focused on building common ground, and makes use of the yes-and principle, a strong grounding speech act, to establish coherence and an actionable objective reality. We collect a corpus of more than 26,000 yes-and turns, transcribing them from improv dialogues and extracting them from larger, but more sparsely populated movie script dialogue corpora, via a bootstrapped classifier. We fine-tune chit-chat dialogue systems with our corpus to encourage more grounded, relevant conversation and confirm these findings with human evaluations.
AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing

Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e. model-based methods and classical deep network methods. By unfolding the iterative optimization algorithm for model-based methods onto networks, deep unfolding methods have the good interpretation of model-based methods and the high speed of classical deep network methods. In this article, to solve the visual image CS problem, we propose a deep unfolding model dubbed AMP-Net. Rather than learning regularization terms, it is established by unfolding the iterative denoising process of the well-known approximate message passing algorithm. Furthermore, AMP-Net integrates deblocking modules in order to eliminate the blocking artifacts that usually appear in CS of visual images. In addition, the sampling matrix is jointly trained with other network parameters to enhance the reconstruction performance. Experimental results show that the proposed AMP-Net has better reconstruction accuracy than other state-of-the-art methods with high reconstruction speed and a small number of network parameters.
Power and subcarrier allocation in multicarrier NOMA-FD systems

In this paper, we study the problem of power and channel allocation for multicarrier non-orthogonal multiple access (NOMA) full duplex (FD) systems. In such a system there are multiple interfering users transmitting over the same channel and the allocation task is a non-convex and extremely challenging problem. The objective of our work is to propose a solution that is close to the theoretic optimum but is of limited complexity. Following a block coordinate descent approach, we propose two algorithms based on the decomposition of the original allocation problem in lower-complexity sub-problems, which can be solved in the Lagrangian dual domain with a great reduction of the computational load. Numerical results show the effectiveness of approach we propose, which outperforms other schemes designed to address NOMA-FD allocation and attains performance similar to the optimal solution with much lower complexity.
A Mathematical Programming approach to Binary Supervised Classification with Label Noise

In this paper we propose novel methodologies to construct Support Vector Machine -based classifiers that takes into account that label noises occur in the training sample. We propose different alternatives based on solving Mixed Integer Linear and Non Linear models by incorporating decisions on relabeling some of the observations in the training dataset. The first method incorporates relabeling directly in the SVM model while a second family of methods combines clustering with classification at the same time, giving rise to a model that applies simultaneously similarity measures and SVM. Extensive computational experiments are reported based on a battery of standard datasets taken from UCI Machine Learning repository, showing the effectiveness of the proposed approaches.
Turbulence and Energetic Particles in Radiative Shock Waves in the Cygnus Loop. I. Shock Properties

We have obtained a contiguous set of long-slit spectra of a shock wave in the Cygnus Loop to investigate its structure, which is far from the morphology predicted by 1D models. Proper motions from Hubble Space Telescope images combined with the known distance to the Cygnus Loop provide an accurate shock speed. Earlier analyses of shock spectra estimated the shock speed, postshock density, temperature, and elemental abundances. In this paper we determine several more shock parameters: a more accurate shock speed, ram pressure, density, compression ratio, dust destruction efficiency, magnetic field strength, and vorticity in the cooling region. From the derived shock properties we estimate the emissivities of synchrotron emission in the radio and pion decay emission in the gamma-rays. Both are consistent with the observations if we assume simple adiabatic compression of ambient cosmic-rays as in the van der Laan mechanism. We also find that, although the morphology is far from that predicted by 1D models and the line ratios vary dramatically from point to point, the average spectrum is matched reasonably well by 1D shock models with the shock speed derived from the measured proper motion. A subsequent paper will analyze the development of turbulence in the cooling zone behind the shock.
Solving Gauss's law on digital quantum computers with loop-string-hadron digitization

We show that using the loop-string-hadron (LSH) formulation of SU(2) lattice gauge theory (arXiv:1912.06133) as a basis for digital quantum computation easily solves an important problem of fundamental interest: implementing gauge invariance (or Gauss's law) exactly. We first discuss the structure of the LSH Hilbert space in $d$ spatial dimensions, its truncation, and its digitization with qubits. Error detection and mitigation in gauge theory simulations would benefit from physicality "oracles,'"so we decompose circuits that flag gauge invariant wavefunctions. We then analyze the logical qubit costs and entangling gate counts involved with the protocols. The LSH basis could save or cost more qubits than a Kogut-Susskind-type representation basis, depending on how that is digitized as well as the spatial dimension. The numerous other clear benefits encourage future studies into applying this framework.
The Effect of a Modified Constant Flow Insufflation of Oxygen during Cardiopulmonary Resuscitation in a Rat Model of Respiratory Cardiac Arrest on Arterial Oxygenation, Alveolar Barotrauma, and Brain Tissue Injury

Aim Intermittent positive pressure ventilation (IPPV) can adversely affect cardiopulmonary resuscitation outcomes by increasing the intrathoracic pressure. Continuous flow insufflation of oxygen (CFIO) has been investigated as a potential alternative, but evidence supporting its superiority over intermittent positive pressure ventilation in cases of cardiac arrest is scant. The aim of the current study was to compare the effects of continuous flow insufflation of oxygen using a one-way valve during cardiopulmonary-resuscitation with intermittent positive pressure ventilation in a rat model of respiratory arrest. Methods Male Sprague-Dawley rats weighing 400∼450 g (from minimum to maximum) were randomly assigned to either a sham, IPPV, or CFIO group (n = 10 per group). Respiratory arrest was induced by blocking the endotracheal tube. Arterial blood gas analysis was performed during cardiopulmonary resuscitation to compare the oxygenation levels. Tissues were then harvested to compare the degrees of pulmonary barotrauma and ischemic brain injury. Results Return of spontaneous circulation was observed in 6/10 rats in the IPPV group and 5/10 in the CFIO group. During cardiopulmonary resuscitation, the mean PaO2 was significantly higher in the CFIO group (83.10 mmHg) than in the IPPV group (56.10 mmHg). Lung biopsy revealed more inflammatory cells and marked thickening of the alveolar wall in the IPPV group; the group also exhibited a higher frequency of neuroglial cells and apoptotic bodies of pyramidal cells, resulting from ischemic injury. Conclusion In a rat model of respiratory arrest, CFIO using a one-way valve resulted in a greater level of oxygenation and less lung and brain injuries than with IPPV.
The role of mouse models in colorectal cancer research—The need and the importance of the orthotopic models

Colorectal cancer is a worldwide health burden, with high incidence and mortality, especially in the advanced stages of the disease. Preclinical models are very important and valuable to discover and validate early and specific biomarkers as well as new therapeutic targets. In order to accomplish that, the animal models must replicate the clinical evolution of the disease in all of its phases. In this article, we review the existent mouse models, with their strengths and weaknesses in the replication of human cancer disease progression, with major focus on orthotopic models.