Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of Pruning Techniques for Deep Neural Networks Simplification

Convolutional Neural Networks (CNNs) suffer from different issues, such as computational complexity and the number of parameters. In recent years pruning techniques are employed to reduce the number of operations and model size in CNNs. Different pruning methods are proposed, which are based on pruning the connections, channels, and filters. Various techniques and tricks accompany pruning methods, and there is not a unifying framework to model all the pruning methods. In this paper pruning methods are investigated, and a general model which is contained the majority of pruning techniques is proposed. The advantages and disadvantages of the pruning methods can be identified, and all of them can be summarized under this model. The final goal of this model is to provide a general approach for all of the pruning methods with different structures and applications.
Characterization of many-body mobility edges with random matrices.

Whether the many-body mobility edges can exist in a one-dimensional interacting quantum system is a controversial problem, mainly hampered by the limited system sizes amenable to numerical simulations. We investigate the transition from chaos to localization by constructing a combined random matrix, which has two extremes, one of Gaussian orthogonal ensemble and the other of Poisson statistics, drawn from different distributions. We find that by fixing a scaling parameter, the mobility edges can exist while increasing the matrix dimension $D\rightarrow\infty$, depending on the distribution of matrix elements of the diagonal uncorrelated matrix. By applying those results to a specific one-dimensional isolated quantum system of random diagonal elements, we confirm the existence of a many-body mobility edge, connecting it with results on the onset of level repulsion extracted from ensembles of mixed random matrices.
Capacity and Optimal Resource Allocation for IRS-Assisted Multi-User Communication Systems

The fundamental capacity limits of intelligent reflecting surface (IRS)-assisted multi-user wireless communication systems are investigated in this article. Specifically, the capacity and rate regions for both capacity-achieving non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission schemes are characterized by jointly optimizing the IRS reflection matrix and wireless resource allocation under the constraints of a maximum number of IRS reconfiguration times. In NOMA, all users are served in the same resource blocks by employing superposition coding and successive interference cancelation techniques. In OMA, all users are served by being allocated orthogonal resource blocks of different sizes. For NOMA, the ideal case with an asymptotically large number of IRS reconfiguration times is firstly considered, where the optimal solution is obtained by employing the Lagrange duality method. Inspired by this result, an inner bound of the capacity region for the general case with a finite number of IRS reconfiguration times is derived. For OMA, the optimal transmission strategy for the ideal case is to serve each individual user alternatingly with its effective channel power gain maximized. Based on this result, a rate region inner bound for the general case is derived. Finally, numerical results are provided to show that: i) a significant capacity and rate region improvement can be achieved by using IRS; ii) the capacity gain can be further improved by dynamically configuring the IRS reflection matrix.
Learning Topometric Semantic Maps from Occupancy Grids

Today’s mobile robots are expected to operate in complex environments they share with humans. To allow intuitive human-robot collaboration, robots require a human-like understanding of their surroundings in terms of semantically classified instances. In this paper, we propose a new approach for deriving such instance-based semantic maps purely from occupancy grids. We employ a combination of deep learning techniques to detect, segment and extract door hypotheses from a random-sized map. The extraction is followed by a post-processing chain to further increase the accuracy of our approach, as well as place categorization for the three classes room, door and corridor. All detected and classified entities are described as instances specified in a common coordinate system, while a topological map is derived to capture their spatial links. To train our two neural networks used for detection and map segmentation, we contribute a simulator that automatically creates and annotates the required training data. We further provide insight into which features are learned to detect doorways, and how the simulated training data can be augmented to train networks for the direct application on real-world grid maps. We evaluate our approach on several publicly available real-world data sets. Even though the used networks are solely trained on simulated data, our approach demonstrates high robustness and effectiveness in various real-world indoor environments.
Empirical bias-reducing adjustments to estimating functions

We develop a novel, general framework for reduced-bias $M$-estimation from asymptotically unbiased estimating functions. The framework relies on an empirical approximation of the bias by a function of derivatives of estimating function contributions. Reduced-bias $M$-estimation operates either implicitly, by solving empirically adjusted estimating equations, or explicitly, by subtracting the estimated bias from the original $M$-estimates, and applies to models that are partially- or fully-specified, with either likelihoods or other surrogate objectives. Automatic differentiation can be used to abstract away the only algebra required to implement reduced-bias $M$-estimation. As a result, the bias reduction methods we introduce have markedly broader applicability and more straightforward implementation than other established bias-reduction methods that require resampling or evaluation of expectations of products of log-likelihood derivatives. If $M$-estimation is by maximizing an objective, then there always exists a bias-reducing penalized objective. That penalized objective relates closely to information criteria for model selection, and can be further enhanced with plug-in penalties to deliver reduced-bias $M$-estimates with extra properties, like finiteness in models for categorical data. The reduced-bias $M$-estimators have the same asymptotic distribution as the original $M$-estimators, and, hence, standard procedures for inference and model selection apply unaltered with the improved estimates. We demonstrate and assess the properties of reduced-bias $M$-estimation in well-used, prominent modelling settings of varying complexity.
Use of Trastuzumab for Early-Stage Invasive Breast Cancer

C P J / R P C • J A N U A R Y / F E B R U A R Y 2 0 0 6 • V O L 1 3 9 , N O 1 3 5 Trastuzumab is a recombinant monoclonal antibody against the extracellular domain of HER2 protein. HER2 (human epidural growth factor receptor 2) protein plays a role in regulating the growth and differentiation of cells. Overexpression of HER2 on the surface of breast cancer cells (and/or the amplification of the HER2 gene) occurs in 15%–25% of breast cancer, and is associated with a more aggressive clinical course. Trastuzumab is indicated in Canada for treatment of patients with metastatic breast cancer whose tumors substantially overexpress the HER2 protein; however, two recently published studies have provided the evidence for the use of trastuzumab for early stage invasive breast cancer as well. This article provides a summary of these two studies.
On the Controllability of Matrix-Weighted Networks

This letter examines the controllability of matrix-weighed networks from a graph-theoretic perspective. As distinct from the scalar-weighted networks, the rank of weight matrices introduce additional intricacies into characterizing the dimension of the controllable subspace for such networks. Specifically, we investigate how the definiteness of weight matrices, encoding a generalized characterization of inter-agent connectivity on matrix-weighted networks, influences the lower and upper bounds of the associated controllable subspaces. We show that such a lower bound is determined by the existence of a certain positive path in the distance partition of the network. By introducing the notion of matrix-valued almost equitable partitions, we show that the corresponding upper bound is determined by the product of the dimension of the weight matrices and the cardinality of the associated matrix-valued almost equitable partition. Furthermore, the structure of an uncontrollable input for such networks is examined.
A broadband X-ray view of the NLSy1 1E 0754.6+3928

Context. The soft X-ray band of many active galactic nuclei (AGNs) is affected by obscuration due to partially ionised matter crossing our line of sight. In this context, two past XMM-Newton observations that were six months apart and a simultaneous NuSTAR-Swift exposure of the Narrow Line Seyfert 1 galaxy 1E 0754.6+3928, which was approximately eight years later, revealed an intense and variable warm absorber (WA) and hints of additional absorbers in the Fe Kα band. Aims. We aim to provide the first X-ray characterisation of this AGN by discussing its broadband (0.3–79 keV) spectrum and temporal properties. Methods. We conduct a temporal and spectroscopic analysis on two ∼10 ks (net exposure) XMM-Newton snapshots performed in April and October 2006. We also study the high energy behaviour of 1E 0754.6+3928 and model its broadband spectrum by using simultaneous Swift-NuSTAR data. Both phenomenological and physically motivated models are tested. Results. We find the presence of flux variability of ∼150% and 30% for 0.3–2 and 2–10 keV bands, respectively, and spectral changes at months timescales of ΔΓ ∼ 0.4. A reflection component that is consistent with being constant over years and arising from relatively cold material that is far from the central super massive black hole is detected. The main spectral feature shaping the 1E 0754.6+3928 spectrum is a WA. Such a component is persistent over the years and the variability of its ionisation and column density is observed down on months in the ranges 3 × 1022cm−2 ≲ NH ≲ 7.2 × 1022cm−2 and 1.5 ≲ log (ξ/erg s−1 cm) ≲ 2.1. Despite the short exposures, we find possible evidence of two additional high-ionisation and high-velocity outflow components in absorption. Conclusions. Our analysis suggests the existence of a complex system of absorbers in 1E 0754.6+3928. Longer exposures are required in order to characterise, on more solid grounds, the absorbers in this AGN.
Simple relation between frustration and transition points in diluted spin glasses.

We investigate a possible relation between frustration and phase-transition points in two-dimensional spin glasses at zero temperature. The relation consists of a condition on the average number of frustrated plaquettes and was reported to provide very good predictions for the critical points at zero temperature, for several two-dimensional lattices. Although there has been no proof of the relation, the good correspondence in several lattices suggests the validity of the relation and an important role of frustration in the phase transitions. To examine the relation further, we present a natural extension of the relation to diluted lattices and verify its effectiveness for bond-diluted square lattices. We then confirm that the resulting points are in good agreement with the phase-transition points in a wide range of dilution rate. Our result supports the suggestion from R. Miyazaki [J. Phys. Soc. Jpn. 82, 094001 (2013)JUPSAU0031-901510.7566/JPSJ.82. 094001] for nondiluted lattices on the importance of frustration to the phase transition of two-dimensional spin glasses at zero temperature.
Minimizing Age of Information via Hybrid NOMA/OMA

This paper considers a wireless network with a base station (BS) conducting timely transmission to two clients in a slotted manner via hybrid non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA). Specifically, the BS is able to adaptively switch between NOMA and OMA for the downlink transmission to minimize the information freshness, characterized by Age of Information (AoI), of the network. If the BS chooses OMA, it can only serve one client within a time slot and should decide which client to serve; if the BS chooses NOMA, it can serve both clients simultaneously and should decide the power allocated to each client. To minimize the weighted sum of expected AoI of the network, we formulate a Markov Decision Process (MDP) problem and develop an optimal policy for the BS to decide whether to use NOMA or OMA for each downlink transmission based on the instantaneous AoI of both clients. We prove the existence of optimal stationary and deterministic policy, and perform action elimination to reduce the action space for lower computation complexity. The optimal policy is shown to have a switching-type property with obvious decision switching boundaries. A suboptimal policy with lower computation complexity is also devised, which can achieve near-optimal performance according to our simulation results. The performance of different policies under different system settings is compared and analyzed in numerical results to provide useful insights for practical system designs.
Fundamental difference in the electronic reconstruction of infinite-layer versus perovskite neodymium nickelate films on 
SrTiO3
(001)

The discovery of superconductivity in a noncuprate NdNiO${}_{2}$ film on SrTiO${}_{3}$(001) has raised significant interest, though its origin is so far elusive. Since the bulk compound shows no superconductivity, here the authors focus on the role of the film geometry and surface and interface from first principles. A fundamentally distinct electronic reconstruction is found for the infinite layer, as opposed to perovskite films, involving the emergence of an interfacial two-dimensional electron gas and a concomitant depletion of the self-doping Nd 5$d$ states, rendering cupratelike behavior.
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning

Many medical decision-making tasks can be framed as partially observed Markov decision processes (POMDPs). However, prevailing two-stage approaches that first learn a POMDP and then solve it often fail because the model that best fits the data may not be well suited for planning. We introduce a new optimization objective that (a) produces both high-performing policies and high-quality generative models, even when some observations are irrelevant for planning, and (b) does so in batch off-policy settings that are typical in healthcare, when only retrospective data is available. We demonstrate our approach on synthetic examples and a challenging medical decision-making problem.
RANDOM REAL BRANCHED COVERINGS OF THE PROJECTIVE LINE

Abstract In this paper, we construct a natural probability measure on the space of real branched coverings from a real projective algebraic curve $(X,c_X)$ to the projective line $(\mathbb{C} \mathbb {P}^1,\textit{conj} )$ . We prove that the space of degree d real branched coverings having “many” real branched points (for example, more than $\sqrt {d}^{1+\alpha }$ , for any $\alpha>0$ ) has exponentially small measure. In particular, maximal real branched coverings – that is, real branched coverings such that all the branched points are real – are exponentially rare.
Guaranteed Two-Sided Bounds on All Eigenvalues of Preconditioned Diffusion and Elasticity Problems Solved by the Finite Element Method

A method of characterizing all eigenvalues of a preconditioned discretized scalar diffusion operator with Dirichlet boundary conditions has been recently introduced in Gergelits, Mardal, Nielsen, and Strakoš (2019). Motivated by this paper, we offer a slightly different approach that extends the previous results in some directions. Namely, we provide bounds on all increasingly ordered eigenvalues of a general diffusion or elasticity operator with tensor data, discretized with the conforming finite element method, and preconditioned by the inverse of a matrix of the same operator with different data. Our results hold for mixed Dirichlet and Robin or periodic boundary conditions applied to the original and preconditioning problems. The bounds are two-sided, guaranteed, easily accessible, and depend solely on the material data.
Towards causality-aware predictions in static machine learning tasks: the linear structural causal model case

While counterfactual thinking has been used in ML tasks that aim to predict the consequences of different actions, policies, and interventions, it has not yet been leveraged in more traditional/static supervised learning tasks, such as the prediction of discrete labels in classification tasks or continuous responses in regression problems. Here, we propose a counterfactual approach to train "causality-aware" predictive models that are able to leverage causal information in static ML tasks. In applications plagued by confounding, the approach can be used to generate predictions that are free from the influence of observed confounders. In applications involving observed mediators, the approach can be used to generate predictions that only capture the direct or the indirect causal influences. The ability to quantify how much of the predictive performance of a learner is actually due to the causal relations of interest is important to improve the explainability of ML systems. Mechanistically, we train and evaluate supervised ML algorithms on (counterfactually) simulated data which retains only the associations generated by the causal relations of interest. In this paper we focus on linear models, where analytical results connecting covariances to causal effects are readily available. Quite importantly, we show that our approach does not require knowledge of the full causal graph. It suffices to know which variables represent potential confounders and/or mediators, and whether the features have a causal influence on the response (or vice-versa).
Note on 
S
-channel factorization in multitrace Berenstein-Maldacena-Nastase correlators

In a previous paper we proposed a factorization principle for the correlation functions of Berenstein-Maldacena-Nastase (BMN) operators in free $\mathcal{N}=4$ super-Yang-Mills theory. These correlators are conjectured to described physical string amplitudes in an infinitely curved Ramond-Ramond pp-wave background. There was a puzzle that the factorization seems to break down for $S$-channel in the $2\rightarrow 2$ scattering process. Here we resolve this puzzle by including some diagrams missed in the previous paper. We also observe some interesting relations which further support the interpretation of higher genus correlators as physical string loop amplitudes.
Finite-Sample Analysis of Image Registration

We study the problem of image registration in the finite-resolution regime and characterize the error probability of algorithms as a function of properties of the transformation and the image capture noise. Specifically, we define a channel-aware Feinstein decoder to obtain upper bounds on the minimum achievable error probability under finite resolution. We specifically focus on the higher-order terms and use Berry-Esseen type CLTs to obtain a stronger characterization of the achievability condition for the problem. Then, we derive a strong type-counting result to characterize the performance of the MMI decoder in terms of the maximum likelihood decoder, in a simplified setting of the problem. We then describe how this analysis, when related to the results from the channel-aware context provide stronger characterization of the finite-sample performance of universal image registration.
xGASS: Robust quantification of asymmetries in global H i spectra and their relationship to environmental processes

We present an analysis of asymmetries in global HI spectra from the extended GALEX Arecibo SDSS Survey (xGASS), a stellar mass-selected and gas fraction-limited survey which is representative of the HI properties of galaxies in the local Universe. We demonstrate that the asymmetry in a HI spectrum is strongly linked to its signal-to-noise meaning that, contrary to what was done in previous works, asymmetry distributions for different samples cannot be compared at face value. We develop a method to account for noise-induced asymmetry and find that the typical galaxy detected by xGASS exhibits higher asymmetry than what can be attributed to noise alone, with 37% of the sample showing asymmetry greater than 10% at an 80% confidence level. We find that asymmetric galaxies contain, on average, 29% less HI mass compared to their symmetric counterparts matched in both stellar mass and signal-to-noise. We also present clear evidence that satellite galaxies, as a population, exhibit more asymmetric HI spectra than centrals and that group central galaxies show a slightly higher rate of HI asymmetries compared to isolated centrals. All these results support a scenario in which environmental processes, in particular those responsible for gas removal, are the dominant driver of asymmetry in xGASS.
Multi-source Domain Adaptation for Visual Sentiment Classification

Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data. However, in practice, data from a single source domain usually have a limited volume and can hardly cover the characteristics of the target domain. In this paper, we propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN), for visual sentiment classification. To handle data from multiple source domains, it learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. This is achieved via cycle consistent adversarial learning in an end-to-end manner. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-the-art MDA approaches for visual sentiment classification.