Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF CENTRAL BANKS' GOVERNANCE ON MONETARY POLICY: A CROSS-SECTIONAL ANALYSIS OF SELECTED SUB-SAHARAN AFRICAN CENTRAL BANKS

The objective of the study was to investigate the effect of governance on monetary policy objective. The research was conducted through a cross-sectional analysis. A sample was obtained from the selected 16 Sub-Saharan African central banks websites. The target populations were 52 Sub-Saharan African countries where only 16 countries were selected for the study because of the data constrains. The websites of central banks from 1996 to 2011 were used which the researcher selected a maximum of 288 observations. The selected countries were reached through convenience sampling procedure for the study. The study used reports from central banks to gather pertinent data. Data Analysis was analyzed through the use of qualitative and quantitative analysis. Regression model was estimated using the random effects methods and tested by the Hausman random effects. ANOVA was used to test for differences among the means of the populations by examining the amount of variations between each of the samples, relative to the amount of variation between the samples. The findings suggest that governance proxy is another measure of central bank independence from the politician that results to appointment and dismissal of governor's turnover rate in office because of the election cycles. The study recommended that high inflation has adverse effects on economic performance either by creating distortions, encouraging rent seeking activity, or by raising risk premier, one would expect central bank independence to improve economic performance.
Structured Knowledge Grounding for Question Answering

Can language models (LM) ground question-answering (QA) tasks in the knowledge base via inherent relational reasoning ability? While previous models that use only LMs have seen some success on many QA tasks, more recent methods include knowledge graphs (KG) to complement LMs with their more logic-driven implicit knowledge. However, effectively extracting information from structured data, like KGs, empowers LMs to remain an open question, and current models rely on graph techniques to extract knowledge. In this paper, we propose to solely leverage the LMs to combine the language and knowledge for knowledge based question-answering with flexibility, breadth of coverage and structured reasoning. Specifically, we devise a knowledge construction method that retrieves the relevant context with a dynamic hop, which expresses more comprehensivenes than traditional GNN-based techniques. And we devise a deep fusion mechanism to further bridge the information exchanging bottleneck between the language and the knowledge. Extensive experiments show that our model consistently demonstrates its state-of-the-art performance over CommensenseQA benchmark, showcasing the possibility to leverage LMs solely to robustly ground QA into the knowledge base.
Defect Solutions of the Non-reciprocal Cahn-Hilliard Model: Spirals and Targets

We study the defect solutions of the Non-reciprocal Cahn-Hilliard model (NRCH). We find two kinds of defects, spirals with unit magnitude topological charge, and topologically neutral targets. These defects generate radially outward travelling waves and thus break the parity and time-reversal symmetry. For a given strength of non-reciprocity, spirals and targets with unique asymptotic wavenumber and amplitude are selected. We use large-scale simulations to show that at low non-reciprocity $\alpha$, a quenched disordered state evolves into quasi-stationary spiral networks. With increasing $\alpha$, we observe networks composed primarily of targets. Beyond a critical threshold $\alpha_c$, a disorder-order transition to from defect networks to travelling waves emerges. The transition is marked by a sharp rise in the global polar order.
A theoretical model for power generation via liquid crystal elastomers

Motivated by the need for new materials and green energy production and conversion processes, a class of mathematical models for liquid crystal elastomers (LCEs) integrated within a theoretical charge pump electrical circuit is considered. The charge pump harnesses the chemical and mechanical properties of LCEs transitioning from the nematic to isotropic phase when illuminated or heated to generate higher voltage from a lower voltage supplied by a battery. For the material constitutive model, purely elastic and neoclassical-type strain energy densities applicable to a wide range of monodomain nematic elastomers are combined, while elastic and photothermal responses are decoupled to make the investigation analytically tractable. By varying the model parameters of the elastic and neoclassical terms, it is found that LCEs are more effective than rubber when used as dielectric material within a charge pump capacitor.
How the dynamical properties of globular clusters impact their γ-ray and X-ray emission

The X-ray and γ-ray emission of globular clusters (GCs) is attributed to their large fraction of compact binary systems, especially those with millisecond pulsars (MSPs). We analyse a population of 124 Galactic GCs to investigate how their dynamical properties affect the formation and evolution of compact binary systems and how this can be translated into the clusters’ observed X-ray and γ-ray emission. We use mainly Chandra X-ray Observatory and Fermi Large Area Telescope observations to achieve our goals and start by detecting 39 GCs in γ rays, seven of which are not listed in previous Fermi-LAT catalogs. Additionally, we find that the total number of X-ray sources within a GC and its γ-ray luminosity are linearly correlated with the stellar encounter rate, indicating that compact binary systems are mainly formed via close stellar encounters. We also find an unexpected rise in the number of X-ray sources for GCs with low rates of stellar encounters, suggesting that there is a dynamical threshold where the formation of X-ray sources is dominated by stellar encounters. Furthermore, we use the Heggie-Hills law to find that subsequent stellar encounters in these compact binaries will, on average, make the binaries even harder, with basically no possibility of binary ionization. Finally, we find that all GCs are point-like sources in γ rays, indicating that the MSPs are concentrated in the clusters’ cores, likely due to dynamical friction.
Probing the Heart of Active Narrow-line Seyfert 1 Galaxies with VERA Wideband Polarimetry

We explored the parsec-scale nuclear regions of a sample of radio-loud narrow-line Seyfert 1 galaxies (NLSy1s) using the VLBI Exploration of Radio Astrometry wideband (at a recording rate of 16 Gbps) polarimetry at 22 and 43 GHz. Our targets include 1H 0323+342, SBS 0846+513, PMN J0948+0022, 1219+044, PKS 1502+036, and TXS 2116-077, which are all known to exhibit γ-ray emission indicative of possessing highly beamed jets similar to blazars. For the first time, we unambiguously detected Faraday rotation toward the parsec-scale radio core of NLSy1s, with a median observed core rotation measure (RM) of 2.7 × 103 rad m−2 (or 6.3 × 103 rad m−2 for redshift-corrected). This level of RM magnitude is significantly larger than those seen in the core of BL Lacertae objects (BLOs; a dominant subclass of blazars), suggesting that the nuclear environment of NLSy1s is more gas-rich than that in BLOs. Interestingly, the observed parsec-scale polarimetric properties of NLSy1s (low core fractional polarization, large core RM and jet–EVPA misalignment) are rather similar to those of flat-spectrum radio quasars (FSRQs). Our results are in accordance with the scenario that NLSy1s are in an early stage of active galactic nucleus evolution with their central black hole masses being smaller than those of more evolved FSRQs.
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models

We introduce a method that takes advantage of high-quality pretrained multimodal representations to explore fine-grained semantic networks in the human brain. Previous studies have documented evidence of functional localization in the brain, with different anatomical regions preferentially activating for different types of sensory input. Many such localized structures are known, including the fusiform face area and parahippocampal place area. This raises the question of whether additional brain regions (or conjunctions of brain regions) are also specialized for other important semantic concepts. To identify such brain regions, we developed a data-driven approach to uncover visual concepts that are decodable from a massive functional magnetic resonance imaging (fMRI) dataset. Our analysis is broadly split into three sections. First, a fully connected neural network is trained to map brain responses to the outputs of an image-language foundation model, CLIP (Radford et al., 2021). Subsequently, a contrastive-learning dimensionality reduction method reveals the brain-decodable components of CLIP space. In the final section of our analysis, we localize shared decodable concepts in the brain using a voxel-masking optimization method to produce a shared decodable concept (SDC) space. The accuracy of our procedure is validated by comparing it to previous localization experiments that identify regions for faces, bodies, and places. In addition to these concepts, whose corresponding brain regions were already known, we localize novel concept representations which are shared across participants to other areas of the human brain. We also demonstrate how this method can be used to inspect fine-grained semantic networks for individual participants. We envisage that this extensible method can also be adapted to explore other questions at the intersection of AI and neuroscience.
Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks

A dominating set of a graph $\mathcal{G=(V, E)}$ is a subset of vertices $S\subseteq\mathcal{V}$ such that every vertex $v\in \mathcal{V} \setminus S$ outside the dominating set is adjacent to a vertex $u\in S$ within the set. The minimum dominating set problem seeks to find a dominating set of minimum cardinality and is a well-established NP-hard combinatorial optimization problem. We propose a novel learning-based heuristic approach to compute solutions for the minimum dominating set problem using graph convolutional networks. We conduct an extensive experimental evaluation of the proposed method on a combination of randomly generated graphs and real-world graph datasets. Our results indicate that the proposed learning-based approach can outperform a classical greedy approximation algorithm. Furthermore, we demonstrate the generalization capability of the graph convolutional network across datasets and its ability to scale to graphs of higher order than those on which it was trained. Finally, we utilize the proposed learning-based heuristic in an iterative greedy algorithm, achieving state-of-the-art performance in the computation of dominating sets.
Looking and Listening: Audio Guided Text Recognition

Text recognition in the wild is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest vision and language processing are effective for scene text recognition. Yet, solving edit errors such as add, delete, or replace is still the main challenge for existing approaches. In fact, the content of the text and its audio are naturally corresponding to each other, i.e., a single character error may result in a clear different pronunciation. In this paper, we propose the AudioOCR, a simple yet effective probabilistic audio decoder for mel spectrogram sequence prediction to guide the scene text recognition, which only participates in the training phase and brings no extra cost during the inference stage. The underlying principle of AudioOCR can be easily applied to the existing approaches. Experiments using 7 previous scene text recognition methods on 12 existing regular, irregular, and occluded benchmarks demonstrate our proposed method can bring consistent improvement. More importantly, through our experimentation, we show that AudioOCR possesses a generalizability that extends to more challenging scenarios, including recognizing non-English text, out-of-vocabulary words, and text with various accents. Code will be available at https://github.com/wenwenyu/AudioOCR.
Efficient Centrality Maximization with Rademacher Averages

The identification of the set of k most central nodes of a graph, or centrality maximization, is a key task in network analysis, with various applications ranging from finding communities in social and biological networks to understanding which seed nodes are important to diffuse information in a graph. As the exact computation of centrality measures does not scale to modern-sized networks, the most practical solution is to resort to rigorous, but efficiently computable, randomized approximations. In this work we present CentRA, the first algorithm based on progressive sampling to compute high-quality approximations of the set of k most central nodes. CentRA is based on a novel approach to efficiently estimate Monte Carlo Rademacher Averages, a powerful tool from statistical learning theory to compute sharp data-dependent approximation bounds. Then, we study the sample complexity of centrality maximization using the VC-dimension, a key concept from statistical learning theory. We show that the number of random samples required to compute high-quality approximations scales with finer characteristics of the graph, such as its vertex diameter, or of the centrality of interest, significantly improving looser bounds derived from standard techniques. We apply CentRA to analyze large real-world networks, showing that it significantly outperforms the state-of-the-art approximation algorithm in terms of number of samples, running times, and accuracy.
A Novel Approach to User Agent String Parsing for Vulnerability Analysis Using Multi-Headed Attention

The increasing reliance on the internet has led to the proliferation of a diverse set of web-browsers and operating systems (OSs) capable of browsing the web. User agent strings (UASs) are a component of web browsing that are transmitted with every Hypertext Transfer Protocol (HTTP) request. They contain information about the client device and software, which is used by web servers for various purposes such as content negotiation and security. However, due to the proliferation of various browsers and devices, parsing UASs is a non-trivial task due to a lack of standardization of UAS formats. Current rules-based approaches are often brittle and can fail when encountering such non-standard formats. In this work, a novel methodology for parsing UASs using Multi-Headed Attention Based transformers is proposed. The proposed methodology exhibits strong performance in parsing a variety of UASs with differing formats. Furthermore, a framework to utilize parsed UASs to estimate the vulnerability scores for large sections of publicly visible IT networks or regions is also discussed. The methodology present here can also be easily extended or deployed for real-time parsing of logs in enterprise settings.
Constraints on long-lived di-baryons and di-baryonic dark matter

A color-flavor-spin singlet state of six quarks $uuddss$ ($S$, or sexaquark) has been argued to be a potentially undiscovered deeply bound, long-lived hadron. Theoretical calculations of the $S$ mass have been made in the literature, widely varying from deeply-bound $\sim 1.2$ GeV to weakly-bound $\sim2.2$ GeV. Given the spread of the mass predictions, it is vital to derive observational constraints on the state as a function of the mass. The transition rates between $S$ and two baryons are governed by $\tilde{g}$, the effective Yukawa coupling between $S$ and two baryons with the same quantum numbers as $S$. In this paper, we place strong observational constraints on $\tilde{g}$, improving on various previous limits; additional limits assuming $S$ is dark matter are also presented.
RACECAR - The Dataset for High-Speed Autonomous Racing

This paper describes RACECAR, the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data was collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge (2021–2022) have contributed to this dataset. The dataset spans 11 racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The dataset contains 27 racing sessions across 11 scenarios with over 6.5 hours of autonomous racing. The data has been released in both ROS 2 and nuScenes format. We have also developed the ROSbag2nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing. We present several benchmark problems on localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping to explore issues that arise at the limits of operation of the vehicle. RACECAR data can be accessed at https://github.com/linklab-uva/RACECAR_DATA.
Two suggested configurations for the Chinese space telescope

China will establish a 2-meter space-based astronomical telescope. Its main science goals are performing a sky survey for research about dark matter and dark energy, and high resolution observations. Some experts suggest that this space telescope should be installed inside the Chinese space station. In accord with this suggestion we put forward our first configuration, i.e., to adopt a coudé system for this telescope. This coudé system comes from the Chinese 2.16m telescope's coudé system, which includes a relay mirror so that excellent image quality can be obtained. In our second configuration, we suggest that the whole space telescope fly freely as an independent satellite outside the space station. When it needs servicing, for example, changing instruments, refilling refrigerant or propellant, etc., this space telescope can fly near or even dock with the core space station. Although some space stations have had accompanying satellites, the one we propose is a space telescope that will be much larger than other accompanying satellites in terms of weight and volume. On the basis of the second configuration, we also put forward the following idea: the space station can be composed of several large independent modules if necessary.
Representative set statements for delta-matroids and the Mader delta-matroid

We present representative sets-style statements for linear delta-matroids, which are set systems that generalize matroids, with important connections to matching theory and graph embeddings. Furthermore, our proof uses a new approach of sieving polynomial families, which generalizes the linear algebra approach of the representative sets lemma to a setting of bounded-degree polynomials. The representative sets statements for linear delta-matroids then follow by analyzing the Pfaffian of the skew-symmetric matrix representing the delta-matroid. Applying the same framework to the determinant instead of the Pfaffian recovers the representative sets lemma for linear matroids. Altogether, this significantly extends the toolbox available for kernelization. As an application, we show an exact sparsification result for Mader networks: Let $G=(V,E)$ be a graph and $\mathcal{T}$ a partition of a set of terminals $T \subseteq V(G)$, $|T|=k$. A $\mathcal{T}$-path in $G$ is a path with endpoints in distinct parts of $\mathcal{T}$ and internal vertices disjoint from $T$. In polynomial time, we can derive a graph $G'=(V',E')$ with $T \subseteq V(G')$, such that for every subset $S \subseteq T$ there is a packing of $\mathcal{T}$-paths with endpoints $S$ in $G$ if and only if there is one in $G'$, and $|V(G')|=O(k^3)$. This generalizes the (undirected version of the) cut-covering lemma, which corresponds to the case that $\mathcal{T}$ contains only two blocks. To prove the Mader network sparsification result, we furthermore define the class of Mader delta-matroids, and show that they have linear representations. This should be of independent interest.
Understanding the Duration of Solar and Stellar Flares at Various Wavelengths

Recent irradiance measurements from numerous heliophysics and astrophysics missions including Solar Dynamics Observatory (SDO), GOES, Kepler, TESS, Chandra, the X-ray Multi-Mirror Mission, and NICER have provided critical input into understanding the physics of the most powerful transient events on the Sun and magnetically active stars:solar and stellar flares. The light curves of flare events from the Sun and stars show remarkably similar shapes, typically with a sharp rise and protracted decay phase. The duration of solar and stellar flares has been found to be correlated with the intensity of the event in some wavelengths, such as white light, but not in other wavelengths, such as soft X-rays, but it is not evident why this is the case. In this study, we use a radiative hydrodynamics code to examine factors affecting the duration of flare emission at various wavelengths. The duration of a light curve depends on the temperature of the plasma, the height in the atmosphere at which the emission forms, and the relative importance of cooling due to radiation, thermal conduction, and enthalpy flux. We find that there is a clear distinction between emission that forms low in the atmosphere and responds directly to heating, and emission that forms in the corona, indirectly responding to heating-induced chromospheric evaporation, a facet of the Neupert effect. We discuss the implications of our results for a wide range of flare energies.
Deciding minimal distinguishing DFAs is NP-complete

In this paper, we present a proof of the NP-completeness of computing the smallest Deterministic Finite Automaton (DFA) that distinguishes two given regular languages as DFAs. A distinguishing DFA is an automaton that recognizes a language which is a subset of exactly one of the given languages. We establish the NP-hardness of this decision problem by providing a reduction from the Boolean Satisfiability Problem (SAT) to deciding the existence of a distinguishing automaton of a specific size.
Sampling and Ranking for Digital Ink Generation on a tight computational budget

Digital ink (online handwriting) generation has a number of potential applications for creating user-visible content, such as handwriting autocompletion, spelling correction, and beautification. Writing is personal and usually the processing is done on-device. Ink generative models thus need to produce high quality content quickly, in a resource constrained environment. In this work, we study ways to maximize the quality of the output of a trained digital ink generative model, while staying within an inference time budget. We use and compare the effect of multiple sampling and ranking techniques, in the first ablation study of its kind in the digital ink domain. We confirm our findings on multiple datasets - writing in English and Vietnamese, as well as mathematical formulas - using two model types and two common ink data representations. In all combinations, we report a meaningful improvement in the recognizability of the synthetic inks, in some cases more than halving the character error rate metric, and describe a way to select the optimal combination of sampling and ranking techniques for any given computational budget.