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We propose a novel digital backpropagation (DBP) technique that combines perturbation theory, subband processing, and splitting ratio optimization. We obtain 0.23 dB, 0.47 dB, or 0.91 dB gains w.r.t. dispersion compensation with only 74, 161, or 681 real multiplications/2D-symbol, improving significantly on existing DBP techniques., Comment: The paper has been submitted for publication to ECOC 2024

Pursuit of honest and truthful decision-making is crucial for governance and accountability in democracies. However, people sometimes take different perspectives of what it means to be honest and how to pursue truthfulness. Here we explore a continuum of perspectives from evidence-based reasoning, rooted in ascertainable facts and data, at one end, to intuitive decisions that are driven by feelings and subjective interpretations, at the other. We analyze the linguistic traces of those contrasting perspectives in Congressional speeches from 1879 to 2022. We find that evidence-based language has continued to decline since the mid-1970s, together with a decline in legislative productivity. The decline was accompanied by increasing partisan polarization in Congress and rising income inequality in society. Results highlight the importance of evidence-based language in political decision-making.

We present a set of controlled hydrodynamical simulations to study the effects of strong galactic outflows on the density and temperature structures, and associated X-ray signatures, of extra-planar and circumgalactic gas. We consider three initial state models, isothermal, isentropic, and rotating cooling-flow, for the hot circumgalactic medium (CGM) into which the outflows are driven. The energy sources are either stellar winds and supernovae, or active galactic nuclei. We consider energy injection rates in the range $10^{40} < \dot{E}_{\rm inj} <10^{44.5}$ erg s$^{-1}$, and compute the time-dependent soft X-ray (0.5-2 keV) surface brightness. For $\dot{E}_{\rm inj} \gtrsim 10^{41} - 10^{42}$ erg s$^{-1}$, with the exact threshold depending on the initial CGM state, the X-ray response is dominated by dense hot gas in the forward shock that eventually fades into the CGM as a sound wave. The shock surrounds an inner hot bubble leading to a radial flattening of the X-ray surface brightness. For lower energy injection rates, the X-ray surface brightness of the initial CGM state is almost unaffected. We present analytic approximations for the outflow shock propagation and the associated X-ray emissions., Comment: Accepted for publication in MNRAS

A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and representative patient datasets with appropriate annotations may be burdensome due to high acquisition cost, safety limitations, patient privacy restrictions or low disease prevalence rates. In silico data offers a number of potential advantages to patient data, such as diminished patient harm, reduced cost, simplified data acquisition, scalability, improved quality assurance testing, and a mitigation approach to data imbalances. We summarize key research trends and practical uses for synthetically generated data for radiological applications of AI. Specifically, we discuss different types of techniques for generating synthetic examples, their main application areas, and related quality control assessment issues. We also discuss current approaches for evaluating synthetic imaging data. Overall, synthetic data holds great promise in addressing current data availability gaps, but additional work is needed before its full potential is realized., Comment: Provisionally accepted to the British Journal of Radiology (BJR) Artificial Intelligence

Structural balance theory predicts that triads in networks gravitate towards stable configurations. The theory has been verified for undirected graphs. Since real-world networks are often directed, we introduce a novel method for considering both transitivity and sign consistency for evaluating partial balance in signed digraphs. We test our approach on graphs constructed by using different methods for identifying edge signs: natural language processing to infer signs from underlying text data, and self-reported survey data. Our results show that for various social contexts and edge sign detection methods, partial balance of these digraphs are moderately high, ranging from 61% to 96%. Our approach not only enhances the theoretical framework of structural balance but also provides practical insights into the stability of social networks, enabling a deeper understanding of interpersonal and group dynamics across different communication platforms., Comment: arXiv admin note: text overlap with arXiv:2006.02565

Probability density functions form a specific class of functional data objects with intrinsic properties of scale invariance and relative scale characterized by the unit integral constraint. The Bayes spaces methodology respects their specific nature, and the centred log-ratio transformation enables processing such functional data in the standard Lebesgue space of square-integrable functions. As the data representing densities are frequently observed in their discrete form, the focus has been on their spline representation. Therefore, the crucial step in the approximation is to construct a proper spline basis reflecting their specific properties. Since the centred log-ratio transformation forms a subspace of functions with a zero integral constraint, the standard $B$-spline basis is no longer suitable. Recently, a new spline basis incorporating this zero integral property, called $Z\!B$-splines, was developed. However, this basis does not possess the orthogonal property which is beneficial from computational and application point of view. As a result of this paper, we describe an efficient method for constructing an orthogonal $Z\!B$-splines basis, called $Z\!B$-splinets. The advantages of the $Z\!B$-splinet approach are foremost a computational efficiency and locality of basis supports that is desirable for data interpretability, e.g. in the context of functional principal component analysis. The proposed approach is demonstrated on an empirical demographic dataset.

In theory, the fact that American colleges and universities tend to be liberal-leaning should not be an issue. However, several studies document that political bias in today's college classrooms causes students to avoid discussing potentially controversial topics for fear of criticism and ridicule, lowered grades, and even physical harm. Inquiry-based instruction can increase students' feelings of safety in sharing their views, decrease student self-censorship, and allow students to practice engaging in uncomfortable yet collegial conversations. This article describes how inquiry-based instruction can be used to: (1) encourage students' consideration of a balanced range information drawn from a variety of sources (curriculum); (2) facilitate active and student-centered thinking, discourse, and decision-making (instruction); and (3) emphasize thoughtful and research-based logic, reasoning, and rationale over simply expression of opinions and ideas (assessment). Such an inquiry-based approach to curriculum, instruction, and assessment can reduce political bias in today's college classrooms.