Miracle-dz/UniFAR-FTUs huggingface dataset:
Causes of Postpartum Depression among Sudanese Women from 2019 to 2021 in Taha Baasher Hospital

Introduction: Postpartum depression (PPD) is a major and common mental health problem complicating maternal status after delivery, mainly associated with maternal suffering and altering the mental status for both herself and her offspring. It is a complex condition results mainly from a mix of physical, emotional, and behavioral changes happens to women after giving birth. The interactions between chemical, social, and psychological variables factors especially in newly mothers experience are probably responsible for initiation and ranges of postpartum depression. Objectives: To determine the causes of postpartum depression among Sudanese women and other associated factors. Methods: This study will be conducted via Cohort – analytical study design based on retrospective database in the last 3 years from current day, will be conducted from Taha Baasher hospital medical records, a psychiatry department. The process started from October 2021. We used scientifically structured questionnaire as a tool to collect data from registered patient’s data with approval from Alzaiem Alazhari University. Result: Total of 79 cases have been studied in order to analyze the most prominent data they might have role in developing causes or risk factor of postpartum depression. The percentage of different parameters manly young age 62.5%, education of primary school 41.3%, with high percentage of positive family history of mental illness and previous history mental illness, 42.1% and 32.9% respectively. The majority of the cases have Moderate symptoms 53.8%, while 27.5% were Severe. The risk of patients was classified into homicidal 3.8% and suicidal were 7.7%. The final diagnosis of the cases was psychosis 76.5% and only 23.5% was depression. Treatment by drugs only was about 97.5%. Conclusion: The majority of causes and risk factor for postpartum depression manly varied from having history of mental illness or similar condition in the family or in the cases themselves, also having young age, and the lack of proper education and occupations. History of victimization also plays a role.
Forced Response of Polar Orthotropic Tapered Circular Plates Resting on Elastic Foundation

Forced axisymmetric response of polar orthotropic circular plates of linearly varying thickness resting on Winkler type of elastic foundation has been studied on the basis of classical plate theory. An approximate solution of problem has been obtained by Rayleigh Ritz method, which employs functions based upon the static deflection of polar orthotropic circular plates. The effect of transverse loadings has been studied for orthotropic circular plate resting on elastic foundation. The transverse deflections and bending moments are presented for various values of taper parameter, rigidity ratio, foundation parameter, and flexibility parameter under different types of loadings. A comparison of results with those available in literature shows an excellent agreement.
The Mediators of Collegial Solidarity on Job Performance: A Relational Study in the Education Sector

One of the distinguishing features of educational institutions is that the learning and teaching processes that are at their core are mostly carried out by expert educators independently on their own in the classroom. This state of independence and solitude causes collegial solidarity to emerge as a more important issue for educational institutions when compared to other institutions. This study examines the mediators of the relationship between educators’ collegial solidarity and job performance. This study examines the mediating roles of three individual variables (i.e., thriving at work, self-efficacy, and work engagement) and three institutional variables (i.e., collegial relations, administrative support, and organizational climate) have on the relationship between educators’ collegial solidarity and job performance using data collected from a total of 766 participants working at educational institutions. It has been determined that institutional and individual variables have a mediating effect in the relationship between educators’ colleague solidarity and job performance. It was observed that institutional variables had a weaker mediating effect than individual variables in this effect. This result shows that the unique nature of the education profession reveals a different structure in terms of the variables examined in the research.
Discerning Limitations of GNN-based Attacks on Logic Locking

Machine learning (ML)-based attacks have revealed the possibility of utilizing neural networks to break locked circuits without needing functional chips (Oracle). Among ML approaches, GNN (graph neural networks)-based attacks are the most potent tools that attackers can employ as they exploit graph structures inherent to a circuit’s netlist. Although promising, in this paper, we reveal that GNNs have some impediments in attacking locked circuits. We investigate the limits of the state-of-the-art GNN-based attacks against logic locking and show that we can drastically decrease the accuracy of these attacks by utilizing these limitations in the locking process.
Deriving interpretable thresholds for variable importance in random forests by permutation

In the context of clinical research and in particular precision medicine the identification of predictive or prognostic biomarkers is of utmost importance. Especially when dealing with high-dimensional data discriminating between informative and uninformative variables plays a crucial role. Machine Learning approaches, and especially Random Forests, are promising approaches in this situation as the Variable Importance (VIMP) of a Random Forest can serve as a decision guidance for the identification of potentially relevant variables. Many different approaches for Random Forest VIMP have been proposed and evaluated (e.g., Speiser et al. 2019). One of the algorithms is the well-performing Boruta method (Kursa and Rudnicki 2010), which adds permutated and thus uninformative versions of each variable (so-called shadow variables) to the set of predictors. Based on that, it classifies the covariables into three possible importance categories: confirmed, tentative or rejected. We propose a variation of the Boruta method and evaluate the relevance of the variables based on different criteria. Our method is independent of the simulations runs and compares the VIMP of each covariate directly with its permuted version. In addition, the uninformative versions are generated by permutating the rows of the dataset, which preserves the relationship between the original variables. For evaluating the importance of the features, we use different criteria, e.g., descriptive statistics of the shadow VIMPs and controlling for the FWER or for the FDR, given a significance level. We examine our method with simulations similar to Degenhardt et al. 2019 and compare its results to the Boruta algorithm. Furthermore, we apply it on public available datasets of varying sizes to illustrate its real-life behaviour. In our approach, the user is guided by several criteria summarized in one visual presentation and has therefore the flexibility to be more conservative by picking all important covariates or more permissive by taking only the most important ones, depending on the nature of their problem.
Gender differences in job mobility and pay progression in the UK

Understanding disparities in the rates at which men and women’s wages grow over the life course is critical to explaining the gender pay gap. Using panel data from 2009 to 2019 for the United Kingdom, we examine how differences in the rates and types of job mobility of men and women – with and without children - influence the evolution of wages. We contrast the rates and wage returns associated with different types of job moves, including moving employer for family reason, moving for wage or career-related reasons, and changing jobs but remaining with the same employer. Despite overall levels of mobility being similar for men and women, we find important differences in the types of mobility they experience, with
Effectiveness of Gait Training Using Body-Weight-Supported Treadmill among Patients with Stroke: A Review Study

Background : Body-weight-supported treadmill training (BWSTT) or herness system is a new approach to treat gait recovery among stroke patients. Objectives : To explore the effects of gait training using body weight supported treadmill or herness system among stroke patients. Methodology: This study aimed to explore the effectiveness of gait training using body-weight-supported treadmill among stroke patients by reviewing of the article. Five articles were reviewed in order to fulfil the purpose of this study. With a PEDro score ranging from 4 to 8, randomized controlled trial was used. Each article is analyzed carefully to find out the effectiveness of the intervention. Results: In this study found that both BWSTT and conventional overground training has beneficiary effects for recovery and had impact on parameters related to balance, mobility, and fear of falling among stroke patients. Conclusion: To determine the intensity and precise number of training sessions that should be carried out to restore normal gait, more and better study is required
CENTER FOR HEALTH ECONOMICS AND POLICY STUDIES

To gain membership in theWorld Trade Organization in 2001, China substantially modified its regulation of foreign trade and investment. These reforms coincided with rapid changes in the composition of capital inflows, as the dominant entry mode shifted from joint venture to wholly foreign owned affiliate. Foreign-invested enterprises contributed a rising share of China’s rapidly growing exports, especially those directly linked to global value chains (GVCs). We investigate how much China’s foreign ownership liberalization contributed to these observed trends in capital inflows and Chinese exports. Accounting for both the set of activities from which it removed foreign equity caps as well as those into which it newly encouraged investment, we estimate the impact of China’s reforms on foreign firm entry and exports using a difference-in-differences estimator. To eliminate bias resulting from heterogeneous and dynamic treatment effects, we also apply novel dynamic difference-indifferences estimators. We find that removal of foreign equity caps induced entry of wholly foreign owned firms, especially in R&D-intensive and skill-intensive activities. Concurrently, new incentives for investment in favored activities also induced foreign entry, particularly joint ventures, and increased processing exports linked to GVCs. Reduced-form calculations imply that FDI policy changes explain almost 9% of the increase in exports from foreign-invested firms over the decade studied. The effect was larger in sectors identified as “high-tech industries” by the Chinese government, as they contribute most of the estimated policy-driven export growth from foreign-invested firms.
Suborbital Soft X-Ray Spectroscopy with Gaseous Electron Multipliers

This thesis consists of the design, fabrication, and launch of a sounding rocket payload to observe the spectrum of the soft X-ray emission (0.1-1 keV) from the Cygnus Loop supernova remnant. This instrument, designated the Off-plane Grating Rocket for Extended Source Spectroscopy (OGRESS), was launched from White Sands Missile Range on May 2nd, 2015. The X-ray spectrograph incorporated a wire-grid focuser feeding an array of gratings in the extreme off-plane mount which dispersed the spectrum onto Gaseous Electron Multiplier (GEM) detectors. The gain characteristics of OGRESS's GEM detectors were fully characterized with respect to applied voltage and internal gas pressure, allowing operational settings to be optimized. The GEMs were optimized to operate below laboratory atmospheric pressure, allowing lower applied voltages, thus reducing the risk of both electrical arcing and tearing of the thin detector windows. The instrument recorded 388 seconds of data and found highly uniform count distributions over both detector faces, in sharp contrast to the expected thermal line spectrum. This signal is attributed to X-ray fluorescence lines generated inside the spectrograph. The radiation is produced when thermal ionospheric particles are accelerated into the interior walls of the spectrograph by the high voltages of the detector windows. A fluorescence model was found to fit the flight data better than modeled supernova spectra. Post-flight testing and analysis revealed that electrons produce distinct signal on the detectors which can also be successfully modeled as fluorescence emission.
Aortic and Arterial Mechanics

Knowledge of the mechanical properties of the aorta is essential as important concerns regarding the treatment of aortic pathologies, such as atherosclerosis, aneurysms and dissections, are fundamentally mechanobiological. A comprehensive review is presented in the current chapter. As the function of arteries is to carry blood to the peripheral organs, the main biomechanical properties of interest are elasticity and rupture properties. Regarding elastic properties, an essential parameter remains the physiological linearized elastic modulus in the circumferential direction, although sophisticated constitutive equations including hyperelasticity are available to model the complex coupled roles played by collagen, elastin and smooth muscle cells in the mechanics of the aorta. Regarding rupture properties, tensile strengths in the axial and circumferential directions are on the order of 1.5 MPa in the thoracic aorta and the radial strength, which is important regarding dissections, is about 0.1 MPa. All these properties may vary spatially and change with the adaptation of the aortic wall to different conditions through growth and remodelling. The progression of diseases, such as aneurysms and atherosclerosis, also manifests with alterations of these material properties. After presenting how the mechanical properties of elastic arteries, including the aorta, can be measured and how they can be used in models to predict the biomechanical response of same under different circumstances, a section will focus on the biomechanics of the ascending thoracic aorta and the chapter will end with current challenges regarding predictive numerical simulations for personalized medicine.
Hypercongestion, autonomous vehicle, and urban spatial structure

This paper examines the effects of hypercongestion mitigation by perimeter control and the introduction of autonomous vehicles on the spatial structures of cities. By incorporating a bathtub model, we develop a land use model where hypercongestion occurs in the downtown area and interacts with land use. We show that hypercongestion mitigation by perimeter control decreases the commuting cost in the short-run and results in a less dense urban spatial structure in the long-run. Furthermore, we reveal that the impact of autonomous vehicles depends on the presence of hypercongestion. Introduction of autonomous vehicles may increase the commuting cost in the presence of hypercongestion and cause a decrease in suburban population, but make cities spatially expanded outward.This result contradicts that of the standard bottleneck model. When perimeter control is implemented, the introduction of autonomous vehicles decreases the commuting cost and results in a less dense urban spatial structure. These results show that hypercongestion is a key factor that can change urban spatial structures.
Optimal Information Encoding in Chemical Reaction Networks

Discrete chemical reaction networks formalize the interactions of molecular species in a well-mixed solution as stochastic events. Given their basic mathematical and physical role, the computational power of chemical reaction networks has been widely studied in the molecular programming and distributed computing communities. While for Turing-universal systems there is a universal measure of optimal information encoding based on Kolmogorov complexity, chemical reaction networks are not Turing universal unless error and unbounded molecular counts are permitted. Nonetheless, here we show that the optimal number of reactions to generate a specific count $x \in \mathbb{N}$ with probability $1$ is asymptotically equal to a ``space-aware'' version of the Kolmogorov complexity of $x$, defined as $\mathrm{\widetilde{K}s}(x) = \min_p\left\{\lvert p \rvert / \log \lvert p \rvert + \log(\texttt{space}(\mathcal{U}(p))) : \mathcal{U}(p) = x \right\}$, where $p$ is a program for universal Turing machine $\mathcal{U}$. This version of Kolmogorov complexity incorporates not just the length of the shortest program for generating $x$, but also the space usage of that program. Probability $1$ computation is captured by the standard notion of stable computation from distributed computing, but we limit our consideration to chemical reaction networks obeying a stronger constraint: they ``know when they are done'' in the sense that they produce a special species to indicate completion. As part of our results, we develop a module for encoding and unpacking any $b$ bits of information via $O(b/\log{b})$ reactions, which is information-theoretically optimal for incompressible information. Our work provides one answer to the question of how succinctly chemical self-organization can be encoded -- in the sense of generating precise molecular counts of species as the desired state.
Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking

There has been significant interest in zero and few-shot learning for dialogue state tracking (DST) due to the high cost of collecting and annotating task-oriented dialogues. Recent work has demonstrated that in-context learning requires very little data and zero parameter updates, and even outperforms trained methods in the few-shot setting (Hu et al. 2022). We propose RefPyDST, which advances the state of the art with three advancements to in-context learning for DST. First, we formulate DST as a Python programming task, explicitly modeling language coreference as variable reference in Python. Second, since in-context learning depends highly on the context examples, we propose a method to retrieve a diverse set of relevant examples to improve performance. Finally, we introduce a novel re-weighting method during decoding that takes into account probabilities of competing surface forms, and produces a more accurate dialogue state prediction. We evaluate our approach using MultiWOZ and achieve state-of-the-art multi-domain joint-goal accuracy in zero and few-shot settings.
FAM: Relative Flatness Aware Minimization

Flatness of the loss curve around a model at hand has been shown to empirically correlate with its generalization ability. Optimizing for flatness has been proposed as early as 1994 by Hochreiter and Schmidthuber, and was followed by more recent successful sharpness-aware optimization techniques. Their widespread adoption in practice, though, is dubious because of the lack of theoretically grounded connection between flatness and generalization, in particular in light of the reparameterization curse - certain reparameterizations of a neural network change most flatness measures but do not change generalization. Recent theoretical work suggests that a particular relative flatness measure can be connected to generalization and solves the reparameterization curse. In this paper, we derive a regularizer based on this relative flatness that is easy to compute, fast, efficient, and works with arbitrary loss functions. It requires computing the Hessian only of a single layer of the network, which makes it applicable to large neural networks, and with it avoids an expensive mapping of the loss surface in the vicinity of the model. In an extensive empirical evaluation we show that this relative flatness aware minimization (FAM) improves generalization in a multitude of applications and models, both in finetuning and standard training. We make the code available at github.
Threshold Testing and Semi-Online Prophet Inequalities

We study threshold testing , an elementary probing model with the goal to choose a large value out of n i.i. d. random variables. An algorithm can test each variable X i once for some threshold t i , and the test returns binary feedback whether X i ≥ t i or not. Thresholds can be chosen adaptively or non-adaptively by the algorithm. Given the results for the tests of each variable, we then select the variable with highest conditional expectation. We compare the expected value obtained by the testing algorithm with expected maximum of the variables. Threshold testing is a semi-online variant of the gambler’s problem and prophet inequalities. Indeed, the optimal performance of non-adaptive algorithms for threshold testing is governed by the standard i.i. d. prophet inequality of approximately 0 . 745 + o (1) as n → ∞ . We show how adaptive algorithms can significantly improve upon this ratio. Our adaptive testing strategy guarantees a competitive ratio of at least 0 . 869 − o (1). Moreover, we show that there are distributions that admit only a constant ratio c < 1, even when n → ∞ . Finally, when each box can be tested multiple times (with n tests in total), we design an algorithm that achieves a ratio of 1 − o (1).
A Stackelberg Viral Marketing Design for Two Competing Players

A Stackelberg duopoly model in which two firms compete to maximize their market share is considered. The firms offer a service/product to customers that are spread over several geographical regions (e.g., countries, provinces, or states). Each region has its own characteristics (spreading and recovery rates) of each service propagation. We consider that the spreading rate can be controlled by each firm and is subject to some investment that the firm does in each region. One of the main objectives of this letter is to characterize the advertising budget allocation strategy for each firm across regions to maximize its market share when competing. To achieve this goal we propose a Stackelberg game model that is relatively simple while capturing the main effects of the competition for market share. By characterizing the strong/weak Stackelberg equilibria of the game, we provide the associated budget allocation strategy. In this setting, it is established under which conditions the solution of the game is the so-called “winner takes all”. Numerical results expand upon our theoretical findings and we provide the equilibrium characterization for an example.
A relative orientation for the moduli space of stable maps to a del Pezzo surface

We prove orientation results for evaluation maps of moduli spaces of rational stable maps to del Pezzo surfaces over a field, both in characteristic $0$ and in positive characteristic. These results and the theory of degree developed in a sequel produce quadratically enriched counts of rational curves over non-algebraically closed fields of characteristic not $2$ or $3$. Orientations are constructed in two steps. First, the ramification locus of the evaluation map is shown to be the divisor in the moduli space of stable maps where image curves have a cusp. Second, this divisor is related to the discriminant of a branched cover of the moduli space given generically by pairs of points on the universal curve with the same image.
Absolute gradient bound for surfaces of constant mean curvature

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