Miracle-dz/UniFAR-FTUs huggingface dataset:
The Arabic Citation Index: Toward a better understanding of Arab scientific literature

Abstract The Arabic Citation Index (ARCI) was launched in 2020. This article provides an overview of the scientific literature contained in this new database and explores its possible usage in research evaluation. As of May 2022, ARCI had indexed 138,283 scientific publications published between 2015 and 2020. ARCI’s coverage is characterized by using the metadata available in scientific publications. First, I investigate the distributions of the indexed literature at various levels (research domains, countries, languages, open access). Articles make up nearly all the documents indexed with a share of 99% of ARCI. The Arts & Humanities and Social Sciences fields have the highest concentration of publications. Most indexed journals are published in Egypt, Algeria, Iraq, Jordan, and Saudi Arabia. About 8% of publications in ARCI are published in languages other than Arabic. Second, I use an unsupervised machine learning model, Latent Dirichlet Allocation, and the text mining algorithm of VOSviewer to uncover the main topics in ARCI. These methods provide a better understanding of ARCI’s thematic structure. Next, I discuss how ARCI can complement global standards in the context of a more inclusive research evaluation. Finally, I suggest a few research opportunities after discussing the findings of this study.
Contributions to bayesian statistical analysis: model specification and nonparametric inference

This dissertation concerns two topics in Bayesian statistical analysis: model specification and nonparametric inference. We begin, following de Finetti, with (i) the observation that perhaps the most general way to define the phrase Bayesian model is by regarding such a model as a joint predictive distribution p(y) for observables (data) y that have not yet been observed, and with (ii) the principle that in specifying p( y) we would like the modeling process to be driven as much as possible by the context of the real-world problem at issue. In simple situations with discrete outcomes this leads via de Finetti's notion of exchangeability to parametric models (those in which the underlying mechanism generating the observed data is taken to be a member of a standard family of probability distributions indexed by a parameter vector); if instead the outcomes are continuous, exchangeability considerations arising from problem context lead to nonparametric models (those in which uncertainty about the underlying data-generating mechanism is modeled by placing probability distributions on the space of all possible cumulative distribution functions). After an introduction to the basic issues in Chapter 1, the second chapter of the dissertation identifies two types of tools needed in Bayesian parametric modeling: (a) given two candidate models i1 and M 2 under consideration, investigators need a method for choosing between them, and (b) having made a series of comparisons among the k models under consideration using methods of the type identified in step (a), and having identified the best model M* of the k under consideration, investigators need a method for deciding whether M* is good enough to stop looking for even better models. In Chapter 2 we argue that model selection is really a decision problem which should be approached by maximizing expected utility, and this perspective leads us to examine the performance of a particular tool of type (a), the predictive log-score (LS) criterion, based on a utility structure that rewards accuracy in predicting future data. In Chapter 3 we undertake a simulation study to explore the ability of Bayesian parametric and nonparametric models to provide an adequate fit to count data, of the type that would routinely be modeled parametrically either through fixed effects or random-effects Poisson regression. Chapter 4 presents new Bayesian nonparametric methodology for quantile regression, which is useful in situations where it is more flexible to quantify the relationship between the response variable and available covariates through regression on a set of quantiles of the response distribution, rather than just on the mean, as in traditional regression models. (Abstract shortened by UMI.)
CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans

Segmentation of lung tissue in computed tomography (CT) images is a precursor to most pulmonary image analysis applications. Semantic segmentation methods using deep learning have exhibited top-tier performance in recent years, however designing accurate and robust segmentation models for lung tissue is challenging due to the variations in shape, size, and orientation. Additionally, medical image artifacts and noise can affect lung tissue segmentation and degrade the accuracy of downstream analysis. The practicality of current deep learning methods for lung tissue segmentation is limited as they require significant computational resources and may not be easily deployable in clinical settings. This paper presents a fully automatic method that identifies the lungs in three-dimensional (3D) pulmonary CT images using deep networks and transfer learning. We introduce (1) a novel 2.5-dimensional image representation from consecutive CT slices that succinctly represents volumetric information and (2) a U-Net architecture equipped with pre-trained InceptionV3 blocks to segment 3D CT scans while maintaining the number of learnable parameters as low as possible. Our method was quantitatively assessed using one public dataset, LUNA16, for training and testing and two public datasets, namely, VESSEL12 and CRPF, only for testing. Due to the low number of learnable parameters, our method achieved high generalizability to the unseen VESSEL12 and CRPF datasets while obtaining superior performance over Luna16 compared to existing methods (Dice coefficients of 99.7, 99.1, and 98.8 over LUNA16, VESSEL12, and CRPF datasets, respectively). We made our method publicly accessible via a graphical user interface at medvispy.ee. kntu.ac. ir.
Phylogenetic analysis of Blaberoidea reveals non‐monophyly of taxa and supports the creation of multiple new subfamilies

The superfamily Blaberoidea is a highly species‐rich group of cockroaches. High‐level blaberoidean phylogenetics are still under debate owing to variable taxon sampling and incongruence between mitochondrial and nuclear evolution, as well as different methods used in various phylogenetic studies. We here present a phylogenetic analysis of Blaberoidea based on a dataset combining the mitochondrial genome with two nuclear markers from representatives of all recognized families within the superfamily. Our results support the monophyly of Blaberiodea, which includes Ectobiidae s.s. (=Ectobiinae), Pseudophyllodromiidae, Nyctiboridae, Blattellidae s.s. (=Blattellinae) and Blaberidae. Ectobiidae s.s. was recovered as sister to the remaining Blaberoidea in all inferences. Pseudophyllodromiidae was paraphyletic with respect to Anaplectoidea + Malaccina. Blattellidae s.s. excluding Anaplectoidea + Malaccina formed a monophyletic group that was sister to Blaberidae. Based on our results, we propose a revised classification for Blaberoidea: Anaplectoidinae subfam.nov. and Episorineuchora gen.nov., and two new combinations at species level within Pseudophyllodromiidae; Rhabdoblattellinae subfam.nov., Calolamprodinae subfam.nov., Acutirhabdoblatta gen.nov., as well as new combinations for three species within Blaberidae. Ancestral state reconstructions based on four morphological characters allow us to infer that the common ancestor of blaberoid cockroaches is likely to be a species with characteristics similar to those found in Ectobiidae, that is, front femur Type B, arolium present, abdomen with a visible gland and male genital hook on the left side.
Translatome analysis reveals microglia and astrocytes to be distinct regulators of inflammation in the hyperacute and acute phases after stroke

Neuroinflammation is a hallmark of ischemic stroke, which is a leading cause of death and long‐term disability. Understanding the exact cellular signaling pathways that initiate and propagate neuroinflammation after stroke will be critical for developing immunomodulatory stroke therapies. In particular, the precise mechanisms of inflammatory signaling in the clinically relevant hyperacute period, hours after stroke, have not been elucidated. We used the RiboTag technique to obtain microglia and astrocyte‐derived mRNA transcripts in a hyperacute (4 h) and acute (3 days) period after stroke, as these two cell types are key modulators of acute neuroinflammation. Microglia initiated a rapid response to stroke at 4 h by adopting an inflammatory profile associated with the recruitment of immune cells. The hyperacute astrocyte profile was marked by stress response genes and transcription factors, such as Fos and Jun, involved in pro‐inflammatory pathways such as TNF‐α. By 3 days, microglia shift to a proliferative state and astrocytes strengthen their inflammatory response. The astrocyte pro‐inflammatory response at 3 days is partially driven by the upregulation of the transcription factors C/EBPβ, Spi1, and Rel, which comprise 25% of upregulated transcription factor‐target interactions. Surprisingly, few sex differences across all groups were observed. Expression and log2 fold data for all sequenced genes are available on a user‐friendly website for researchers to examine gene changes and generate hypotheses for stroke targets. Taken together, our data comprehensively describe the microglia and astrocyte‐specific translatome response in the hyperacute and acute period after stroke and identify pathways critical for initiating neuroinflammation.
Resampling-based efficient shrinkage method for non-smooth minimands

In many regression models, the coefficients are typically estimated by optimising an objective function with a U-statistic structure. Under such a setting, we propose a simple and general method for simultaneous coefficient estimation and variable selection. It combines an efficient quadratic approximation of the objective function with the adaptive lasso penalty to yield a piecewise-linear regularisation path which can be easily obtained from the fast lars–lasso algorithm. Furthermore, the standard asymptotic oracle properties can be established under general conditions without requiring the covariance assumption (Wang, H., and Leng, C. (2007), ‘Unified Lasso Estimation by Least Squares Approximation’, Journal of the American Statistical Association, 102, 1039–1048). This approach applies to many semiparametric regression problems. Three examples are used to illustrate the practical utility of our proposal. Numerical results based on simulated and real data are provided.
Evaluation of the cognitive outcome after out‐of‐hospital cardiac arrest: The role of thalamus

Cardiac arrest survivors develop a variety of neuropsychological impairments and neuroanatomical lesions. The goal of this study is to evaluate if brain voxel‐based morphometry and lesional Magnetic Resonance Imaging (MRI) analyses performed in the acute phase of an Out‐of‐Hospital Cardiac Arrest (OHCA) can be sensitive enough to predict the persistence of neuropsychological disorders beyond 3 months. Survivors underwent a prospective brain MRI during the first month after an OHCA and performed neuropsychological assessments at 1 and 3 months. According to the second neuropsychological assessment, survivors were separated into two subgroups, a deficit subgroup with persistent memory, executive functions, attention and/or praxis disorders (n = 11) and a preserved subgroup, disorders free (n = 14). Brain vascular lesion images were investigated, and volumetric changes were compared with healthy controls. Correlations were discussed between brain MRI results, OHCA data and the second neuropsychological assessment. Analyses of acute ischemic lesions did not reveal significant differences between the two subgroups (p = .35), and correlations with cognitive impairments could not be assessed. voxel‐based morphometry analyses revealed a global cerebral volume reduction for the two subgroups and a clear decrease of the right thalamic volume for the deficit subgroup. It was associated with a cognitive dysexecutive syndrome represented by four executive indexes according to the ‘Groupe de Réflexion pour l'Evaluation des Fonctions EXécutives’ criteria. The right thalamus atrophy seems to be more predictive than the vascular lesions and more specific than a global cerebral volume reduction of post‐OHCA neuropsychological executive disorders.
Out-of-Variable Generalization

The ability of an agent to perform well in new and unseen environments is a crucial aspect of intelligence. In machine learning, this ability is referred to as strong or out-of-distribution generalization. However, simply considering differences in data distributions is not sufﬁcient to fully capture differences in environments. In the present paper, we assay out-of-variable generalization, which refers to an agent’s ability to handle new situations that involve variables never jointly observed before. We expect that such ability is important also for AI-driven scientiﬁc discovery: humans, too, explore "Nature" by probing, observing and measuring subsets of variables at one time. Mathematically, it requires efﬁcient re-use of past marginal knowledge, i.e., knowledge over subsets of variables. We study this problem, focusing on prediction tasks that involve observing overlapping, yet distinct, sets of causal parents. We show that the residual distribution of one environment encodes the partial derivative of the true generating function with respect to the unobserved causal parent. Hence, learning from the residual allows zero-shot prediction even when we never observe the outcome variable in the other environment.
RAELLA: Reforming the Arithmetic for Efficient, Low-Resolution, and Low-Loss Analog PIM: No Retraining Required!

Processing-In-Memory (PIM) accelerators have the potential to efficiently run Deep Neural Network (DNN) inference by reducing costly data movement and by using resistive RAM (ReRAM) for efficient analog compute. Unfortunately, overall PIM accelerator efficiency is limited by energy-intensive analog-to-digital converters (ADCs). Furthermore, existing accelerators that reduce ADC cost do so by changing DNN weights or by using low-resolution ADCs that reduce output fidelity. These strategies harm DNN accuracy and/or require costly DNN retraining to compensate. To address these issues, we propose the RAELLA architecture. RAELLA adapts the architecture to each DNN; it lowers the resolution of computed analog values by encoding weights to produce near-zero analog values, adaptively slicing weights for each DNN layer, and dynamically slicing inputs through speculation and recovery. Low-resolution analog values allow RAELLA to both use efficient low-resolution ADCs and maintain accuracy without retraining, all while computing with fewer ADC converts. Compared to other low-accuracy-loss PIM accelerators, RAELLA increases energy efficiency by up to 4.9× and throughput by up to 3.3×. Compared to PIM accelerators that cause accuracy loss and retrain DNNs to recover, RAELLA achieves similar efficiency and throughput without expensive DNN retraining.
MS-LSTM: Exploring Spatiotemporal Multiscale Representations in Video Prediction Domain

The drastic variation of motion in spatial and temporal dimensions makes the video prediction task extremely challenging. Existing RNN models obtain higher performance by deepening or widening the model. They obtain the multi-scale features of the video only by stacking layers, which is inefficient and brings unbearable training costs (such as memory, FLOPs, and training time). Different from them, this paper proposes a spatiotemporal multi-scale model called MS-LSTM wholly from a multi-scale perspective. On the basis of stacked layers, MS-LSTM incorporates two additional efficient multi-scale designs to fully capture spatiotemporal context information. Concretely, we employ LSTMs with mirrored pyramid structures to construct spatial multi-scale representations and LSTMs with different convolution kernels to construct temporal multi-scale representations. We theoretically analyze the training cost and performance of MS-LSTM and its components. Detailed comparison experiments with twelve baseline models on four video datasets show that MS-LSTM has better performance but lower training costs.
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

The linearized-Laplace approximation (LLA) has been shown to be effective and efficient in constructing Bayesian neural networks. It is theoretically compelling since it can be seen as a Gaussian process posterior with the mean function given by the neural network's maximum-a-posteriori predictive function and the covariance function induced by the empirical neural tangent kernel. However, while its efficacy has been studied in large-scale tasks like image classification, it has not been studied in sequential decision-making problems like Bayesian optimization where Gaussian processes -- with simple mean functions and kernels such as the radial basis function -- are the de-facto surrogate models. In this work, we study the usefulness of the LLA in Bayesian optimization and highlight its strong performance and flexibility. However, we also present some pitfalls that might arise and a potential problem with the LLA when the search space is unbounded.
Probing the Solar Interior with Lensed Gravitational Waves from Known Pulsars

When gravitational waves (GWs) from a spinning neutron star arrive from behind the Sun, they are subjected to gravitational lensing that imprints a frequency-dependent modulation on the waveform. This modulation traces the projected solar density and gravitational potential along the path as the Sun passes in front of the neutron star. We calculate how accurately the solar density profile can be extracted from the lensed GWs using a Fisher analysis. For this purpose, we selected three promising candidates (the highly spinning pulsars J1022+1001, J1730−2304, and J1745−23) from the pulsar catalog of the Australia Telescope National Facility. The lensing signature can be measured with 3σ confidence when the signal-to-noise ratio (S/N) of the GW detection reaches 100 (f/300 Hz)−1 over a 1 yr observation period (where f is the GW frequency). The solar density profile can be plotted as a function of radius when the S/N improves to ≳104.
Optimal distributions for randomized unbiased estimators with an infinite horizon and an adaptive algorithm

The randomized unbiased estimators of Rhee and Glynn (Operations Research:63(5), 1026-1043, 2015) can be highly efficient at approximating expectations of path functionals associated with stochastic differential equations (SDEs). However, there is a lack of algorithms for calculating the optimal distributions with an infinite horizon. In this article, based on the method of Cui et.al. (Operations Research Letters: 477-484, 2021), we prove that, under mild assumptions, there is a simple representation of the optimal distributions. Then, we develop an adaptive algorithm to compute the optimal distributions with an infinite horizon, which requires only a small amount of computational time in prior estimation. Finally, we provide numerical results to illustrate the efficiency of our adaptive algorithm.
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks

Directed evolution as a widely-used engineering strategy faces obstacles in finding desired mutants from the massive size of candidate modifications. While deep learning methods learn protein contexts to establish feasible searching space, many existing models are computationally demanding and fail to predict how specific mutational tests will affect a protein's sequence or function. This research introduces a lightweight graph representation learning scheme that efficiently analyzes the microenvironment of wild-type proteins and recommends practical higher-order mutations exclusive to the user-specified protein and function of interest. Our method enables continuous improvement of the inference model by limited computational resources and a few hundred mutational training samples, resulting in accurate prediction of variant effects that exhibit near-perfect correlation with the ground truth across deep mutational scanning assays of 19 proteins. With its affordability and applicability to both computer scientists and biochemical laboratories, our solution offers a wide range of benefits that make it an ideal choice for the community.
A refined Bloch-Wigner exact sequence in characteristic 2

Let $A$ be a local domain of characteristic $2$ such that its residue field has more than $64$ elements. Then we find an exact relation between the third integral homology of the group $\mathrm{SL}_2(A)$ and Hutchinson's refined Bloch group $\mathcal{RB}(A)$.
Open Maps Preserve Stability

Stability is a fundamental notion in dynamical systems and control theory that, traditionally understood, describes asymptotic behavior of solutions around an equilibrium point. This notion may be characterized abstractly as continuity of a map associating to each point in a state-space the corresponding integral curve with specified initial condition. Interpreting stability as such permits a natural perspective of arbitrary trajectories as stable, irrespective of whether they are stationary or even bounded, so long as trajectories starting nearby stay nearby for all time. While methods exist for recognizing stability of equilibria points, such as Lyapunov's first and second methods, such rely on the state's local property, which may be readily computed or evaluated because solutions starting at equilibria go nowhere. Such methods do not obviously extend for non-stationary stable trajectories. After introducing a notion of stability which makes sense for trajectories generally, we give examples confirming intuition and then present a method for using knowledge of stability of one system to guarantee stability of another, so long as there is an open map of dynamical systems from the known stable system. We thus define maps of dynamical systems and prove that a class of open maps preserve stability.
Wireless Channel Charting: Theory, Practice, and Applications

Channel charting is a recently proposed framework that applies dimensionality reduction to channel state information (CSI) in wireless systems with the goal of associating a pseudo-position to each mobile user in a low-dimensional space: the channel chart. Channel charting summarizes the entire CSI dataset in a self-supervised manner, which opens up a range of applications that are tied to user location. In this article, we introduce the theoretical underpinnings of channel charting and present an overview of recent algorithmic developments and experimental results obtained in the field. We furthermore discuss concrete application examples of channel charting to network- and user-related applications, and we provide a perspective on future developments and challenges as well as the role of channel charting in next-generation wireless networks.