Miracle-dz/UniFAR-FTUs huggingface dataset:
The maximum mass and deformation of rotating strange quark stars with strong magnetic fields

We study the structure and total energy of a strange quark star (SQS) endowed with a strong magnetic field with different rotational frequencies. The MIT bag model is used, with the density-dependent bag constant for the equation of state (EOS). The EOS is computed considering the Landau quantization effect regarding the strong magnetic fields (up to $5\times10^{17}$ G) in the interior of the strange quark star. Using the LORENE library, we calculate the structural parameters of SQS for different setups of magnetic field strengths and rotational frequencies. In each setup, we perform calculations for $51$ stellar configurations, with specified central enthalpy values. We investigate the configurations with the maximum gravitational mass of SQS in each setup. Our models of SQSs are compared in the maximum gravitational mass, binding energy, compactness, and deformation of the star. We show that the gravitational mass might exceed $2.3 M_\odot$ in some models, which is comparable with the mass of the recently detected ``black widow'' pulsar \emph{PSR J0952-0607} and the mass of \emph{GW190814} detected by the LIGO/Virgo collaboration. The deformation and maximum gravitational mass of SQS can be characterized by simple functions that have been fitted to account for variations in both magnetic field strength and frequency. Rapidly rotating strange stars have a minimum gravitational mass given by the equatorial mass-shedding limit.
Optimizing Hyperparameters with Conformal Quantile Regression

Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capture uncertainty but they make strong assumptions about the observation noise, which might not be warranted in practice. In this work, we propose to leverage conformalized quantile regression which makes minimal assumptions about the observation noise and, as a result, models the target function in a more realistic and robust fashion which translates to quicker HPO convergence on empirical benchmarks. To apply our method in a multi-fidelity setting, we propose a simple, yet effective, technique that aggregates observed results across different resource levels and outperforms conventional methods across many empirical tasks.
Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation

The issue of word sense ambiguity poses a significant challenge in natural language processing due to the scarcity of annotated data to feed machine learning models to face the challenge. Therefore, unsupervised word sense disambiguation methods have been developed to overcome that challenge without relying on annotated data. This research proposes a new context-aware approach to unsupervised word sense disambiguation, which provides a flexible mechanism for incorporating contextual information into the similarity measurement process. We experiment with a popular benchmark dataset to evaluate the proposed strategy and compare its performance with state-of-the-art unsupervised word sense disambiguation techniques. The experimental results indicate that our approach substantially enhances disambiguation accuracy and surpasses the performance of several existing techniques. Our findings underscore the significance of integrating contextual information in semantic similarity measurements to manage word sense ambiguity in unsupervised scenarios effectively.
Dynamic DH-MBIR for Phase-Error Estimation from Streaming Digital-Holography Data

Directed energy applications require the estimation of digital-holographic (DH) phase errors due to atmospheric turbulence in order to accurately focus the outgoing beam. These phase error estimates must be computed with very low latency to keep pace with changing atmospheric parameters, which requires that phase errors be estimated in a single shot of DH data. The digital holography model-based iterative reconstruction (DH-MBIR) algorithm is capable of accurately estimating phase errors in a single shot using the expectation maximization (EM) algorithm. However, existing implementations of DH-MBIR require hundreds of iterations, which is not practical for real-time applications. In this paper, we present the Dynamic DH-MBIR (DDH-MBIR) algorithm for estimating isoplanatic phase errors from streaming single-shot data with extremely low latency. The Dynamic DH-MBIR algorithm reduces the computation and latency by orders of magnitude relative to conventional DH-MBIR, making real-time throughput and latency feasible in applications. Using simulated data that models frozen flow of atmospheric turbulence, we show that our algorithm can achieve a consistently high Strehl ratio with realistic simulation parameters using only 1 iteration per timestep.
Structural Group Unfairness: Measurement and Mitigation by means of the Effective Resistance

Social networks contribute to the distribution of social capital, defined as the relationships, norms of trust and reciprocity within a community or society that facilitate cooperation and collective action. Social capital exists in the relations among individuals, such that better positioned members in a social network benefit from faster access to diverse information and higher influence on information dissemination. A variety of methods have been proposed in the literature to measure social capital at an individual level. However, there is a lack of methods to quantify social capital at a group level, which is particularly important when the groups are defined on the grounds of protected attributes. Furthermore, state-of-the-art approaches fail to model the role of long-range interactions between nodes in the network and their contributions to social capital. To fill this gap, we propose to measure the social capital of a group of nodes by means of their information flow and emphasize the importance of considering the whole network topology. Grounded in spectral graph theory, we introduce three effective resistance-based measures of group social capital, namely group isolation, group diameter and group control. We denote the social capital disparity among different groups in a network as structural group unfairness, and propose to mitigate it by means of a budgeted edge augmentation heuristic that systematically increases the social capital of the most disadvantaged group. In experiments on real networks, we uncover significant levels of structural group unfairness when using gender as the protected attribute, with females being the most disadvantaged group in comparison to males. We also illustrate how our proposed edge augmentation approach is able to not only effectively mitigate the structural group unfairness but also increase the social capital of all groups in the network.
Positive Approach to Student Success: Review of a Program for Reaching Students with Emotional Disturbances

A number of schools in the southern portion of Texas, primarily in and around the Houston metropolitan area and Kentucky have started using a program known as Positive Approach to Student Success, or PASS. PASS is a program designed to provide educational services within the mainstream classroom setting for students identified with emotional disturbances and behavior disorders (EBD). The program was founded on the belief that youth benefit behaviorally from educational experiences with their non-handicapped peers and academically from participation in the general curriculum where there is access to a “highly qualified” (NCLB, 2001) instructors (Poole and Caperton-Brown, 2005). The author will begin with an introduction and a brief program description, and conclude with the author’s suggestions and concerns regarding this program. This author’s primary goal is to review this program in an effort to better understand and explain its goals, objectives, and success.
Sum-of-Local-Effects Data Structures for Separable Graphs

It is not difficult to think of applications that can be modelled as graph problems in which placing some facility or commodity at a vertex has some positive or negative effect on the values of all the vertices out to some distance, and we want to be able to calculate quickly the cumulative effect on any vertex's value at any time or the list of the most beneficial or most detrimential effects on a vertex. In this paper we show how, given an edge-weighted graph with constant-size separators, we can support the following operations on it in time polylogarithmic in the number of vertices and the number of facilities placed on the vertices, where distances between vertices are measured with respect to the edge weights: Add (v, f, w, d) places a facility of weight w and with effect radius d onto vertex v. Remove (v, f) removes a facility f previously placed on v using Add from v. Sum (v) or Sum (v, d) returns the total weight of all facilities affecting v or, with a distance parameter d, the total weight of all facilities whose effect region intersects the ``circle'' with radius d around v. Top (v, k) or Top (v, k, d) returns the k facilities of greatest weight that affect v or, with a distance parameter d, whose effect region intersects the ``circle'' with radius d around v. The weights of the facilities and the operation that Sum uses to ``sum'' them must form a semigroup. For Top queries, the weights must be drawn from a total order.
Randomly twisted hypercubes -- between structure and randomness

Twisted hypercubes are generalizations of the Boolean hypercube, obtained by iteratively connecting two instances of a graph by a uniformly random perfect matching. Dudek et al. showed that when the two instances are independent, these graphs have optimal diameter. We study twisted hypercubes in the setting where the instances can have general dependence, and also in the particular case where they are identical. We show that the resultant graph shares properties with random regular graphs, including small diameter, large vertex expansion, a semicircle law for its eigenvalues and no non-trivial automorphisms. However, in contrast to random regular graphs, twisted hypercubes allow for short routing schemes.
Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography

White matter hyperintensity (WMH) lesions on T2 fluid‐attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and changes in adjacent normal‐appearing white matter can disrupt computerized tract reconstruction and result in inaccurate measures of structural brain connectivity. The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH. To assess the impact of using young versus older subject diffusion MRI data for virtual lesion tractography, we leveraged recently available diffusion MRI data from the Human Connectome Project (HCP) Lifespan database. Neuroimaging data from 50 healthy young (39.2 ± 1.6 years) and 46 healthy older (74.2 ± 2.5 years) subjects were obtained from the publicly available HCP‐Aging database. Three WMH masks with low, moderate, and high lesion burdens were extracted from the WMH lesion frequency map of locally acquired FLAIR MRI data. Deterministic tractography was conducted to extract streamlines in 21 WM bundles with and without the WMH masks as regions of avoidance in both young and older cohorts. For intact tractography without virtual lesion masks, 7 out of 21 WM pathways showed a significantly lower number of streamlines in older subjects compared to young subjects. A decrease in streamline count with higher native lesion burden was found in corpus callosum, corticostriatal tract, and fornix pathways. Comparable percentages of affected streamlines were obtained in young and older groups with virtual lesion tractography using the three WMH lesion masks of increasing severity. We conclude that using normative diffusion MRI data from young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age‐matched normative data.
Variable Horizon Robust Model Predictive Control for Time-varying Uncertain Systems

By analyzing the feasibility and stability of systems with polytopic description,we proposed a variable horizon robust model predictive control algorithm.Retaining the same feasible set of initial conditions with the existing algorithm,our algorithm guarantees the robust stability of the closed-loop system once the problem is initially feasible.The effectiveness of this algorithm is illustrated by a simulation example.
Loss research on wireless sensor network signal transmission in persimmon orchard

To explore a wireless sensor network(WSN) system and node deployment in persimmon orchards, this paper focused on the wireless network signal transmission under the 2.4 GHz radio channel in the persimmon orchard when it was in the germination stage, young leaves stage, and flowering stage, three growth periods. A row of persimmon tress in the growth period was selected. By adjusting the height and distance of the node and father node, the distance from three different heights from 0.80 meters at the bottom of the tree and 1.80 m at the most luxuriant foliage location to 2.80 meters at the top of the canopy wee measured. Persimmon trees were measured for each growth period resulting in 72 groups of data. Results showed the LQI value with the change of the distance, the sine curve type attenuation trend, and the difference was obvious in the three growth periods. The radio signal attenuation data measured the worst minimal in the flowering stage, because the branches and leaves of signal reflection, refraction, and absorption was the most serious in this period. It is showed that the best effective transmission distance of each column of trees was 38 m when child nodes and father nodes are on the top of the canopy(2.80 m) in germination, while in the young leaves stage, the height of the child nodes and father nodes are located in the top of the canopy, when the node is the best place to pitch 32 m. In the flowering stage, child nodes and father nodes are on the top of the canopy, and the best distance is 26 m. In the case when plant height is not very high, when the device is placed on top of the node, the wireless sensor networks optimal signal transmission distance changes as the degree of lush foliage plants is smaller and smaller. During the experiment, because the orchard in the wild environment was susceptible to the wind, the corresponding external factors, such as the difference of the instantaneous value, sometimes the LQI was big. Based on two data sets, curve fittings were established in the 2.4GHz channel signal attenuation model, which cubic polynomial model maximum correlation was coefficient R squared for an optimal model. A model of R value was the biggest of all. In the germination stage, R squared was 0.754-0.958; in the young leaves stage it was 0.805-0.979; and in the flowering stage it was 0.758-0.991. Wireless sensor network research on orchard signal transmission loss provided the technical basis for the deployment of wireless sensor network nodes in orchards.
Memory Efficient APP Decoders for Low-density parity-check codes

In this paper we propose two memory efficient a posteriori probability (APP) decoders for the decoding of lowdensity parity-check (LDPC) codes. Proposed decoders require memory that is linear in the number of nodes in the Tanner graph of the code. This is a significant saving compared to the existing APP decoder, which requires memory that is at least proportional to the number of edges. We derive the precise expressions for the memory and computational complexity of the decoders in terms of the the number of real operations and basic memory units required for the decoding and consider the decoders throughput dependency on the memory complexity.
Evaluation of total leukocytes count by flow cytometry with Beckman Coulter® DXH900 in the presence of platelet aggregates

Abstract The Beckman Coulter® DXH900 uses the impedance method to measure the total leukocytes count. In presence of platelet aggregates, the device identifies the structural changes and associates an alarm with the leukocytes result. The aim of this study was to evaluate the influence of platelet aggregates using the principle of flow cytometry as a second assessment of the white blood cell count. Total leukocytes count was evaluated in 49 specimens with presence of platelet aggregates and 32 without anomaly. The differences between total leukocyte count by the two automatic methods (impedance and flow cytometry) and the microscopic method were compared. Without platelet aggregates, the median values were 5.6 (microscopic cell count), 5.4 (impedance) and 5.4 (flow cytometry) and no discordance was observed. In presence of platelet aggregates, the median values were 5.6, 6.4 and 5.1 respectively. The graphical analysis with the allowable total error range of ± 25.7% showed substantial analytical discrepancies (15/49) using impedance method while the flow cytometry method revealed minor disagreements (3/49). Analytical discordances versus WBC reference ranges showed 88% agreement and a substantial Kappa coefficient of 0.70 by impedance, while the flow cytometry method had 94% agreement and a perfect Kappa coefficient of 0.83. The formation of platelet aggregates increased the total leukocyte count performed DXH900 impedance method. Our study has shown that DXH 900 flow cytometry method may be an alternative to exclude the presence of pseudoleukocytosis. In case flags are generated, the microscopic method may be needed for the confirmation of WBC count.
Tracing the impacts of recent rapid sea ice changes and the A68 megaberg on the surface freshwater balance of the Weddell and Scotia Seas

The Southern Ocean upper-layer freshwater balance exerts a global climatic influence by modulating density stratification and biological productivity, and hence the exchange of heat and carbon between the atmosphere and the ocean interior. It is thus important to understand and quantify the time-varying freshwater inputs, which is challenging from measurements of salinity alone. Here we use seawater oxygen isotopes from samples collected between 2016 and 2021 along a transect spanning the Scotia and northern Weddell Seas to separate the freshwater contributions from sea ice and meteoric sources. The unprecedented retreat of sea ice in 2016 is evidenced as a strong increase in sea ice melt across the northern Weddell Sea, with surface values increasing approximately two percentage points between 2016 and 2018 and column inventories increasing approximately 1 to 2 m. Surface meteoric water concentrations exceeded 4% in early 2021 close to South Georgia due to meltwater from the A68 megaberg; smaller icebergs may influence meteoric water at other times also. Both these inputs highlight the importance of a changing cryosphere for upper-ocean freshening; potential future sea ice retreats and increases in iceberg calving would enhance the impacts of these freshwater sources on the ocean and climate. This article is part of a discussion meeting issue ‘Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities’.
General stability conditions for zonal flows in a one-layer model on the β-plane or the sphere

Sufficient stability conditions are derived for a zonal flow on the β-plane or the sphere. Two conditions guarantee both shear stability (to perturbations with vanishing zonal average) and inertial stability (to longitude-independent perturbations). These conditions are not restricted to normal-mode disturbances, and are derived without making use of the quasi-geostrophic approximation. The main limitation of the model is to have only one layer. On the β-plane, the conditions are: (i) that the product of the meridional gradient of potential vorticity and the difference between an arbitrary constant and the zonal velocity be everywhere non-negative; and (ii) that the absolute value of this difference be nowhere larger than the local phase speed of long gravity waves. Inertial stability is independently assured if the Cariolis parameter and the potential vorticity are everywhere of the same sign (this well-known condition can be easily violated near the equator, but the flow may nonetheless be stable). If the meridional gradient of potential vorticity has everywhere the same sign, then conditions (i) and (ii) can be shown to be consequences of the conservation of a total pseudo-energy E0 and pseudomomentum P0, defined so that their lowest-order contribution is quadratic in the deviation from the fundamental state (even in the case that the perturbation is longitude-independent). Thus, if there exists a value of α such that the integral of E0 − αP0 is positive-definite, then the flow is stable. In this case, the stability conditions are valid for small, rather than infinitesimal, perturbations. The parameters of stable flows, as guaranteed by these conditions, are investigated for the family of Gaussian jets centred at the equator; both the cases of an unbounded ocean and a semi-infinite ocean, poleward from a zonal wall, are considered. Easterlies with the width of a Kelvin wave and westerlies with that width or wider may be unstable, even though the gradient of potential vorticity is positive for any strength of the jet.
An Empirical Approach to Enhance Performance for Scalable CORDIC-Based Deep Neural Networks

Practical implementation of deep neural networks (DNNs) demands significant hardware resources, necessitating high computational power and memory bandwidth. While existing field-programmable gate array (FPGA)–based DNN accelerators are primarily optimized for fast single-task performance, cost, energy efficiency, and overall throughput are crucial considerations for their practical use in various applications. This article proposes a performance-centric pipeline Coordinate Rotation Digital Computer (CORDIC)–based MAC unit and implements a scalable CORDIC-based DNN architecture that is area- and power-efficient and has high throughput. The CORDIC-based neuron engine uses bit-rounding to maintain input-output precision and minimal hardware resource overhead. The results demonstrate the versatility of the proposed pipelined MAC, which operates at 460 MHz and allows for higher network throughput. A software-based implementation platform evaluates the proposed MAC operation’s accuracy for more extensive neural networks and complex datasets. The DNN accelerator with parameterized and modular layer-multiplexed architecture is designed. Empirical evaluation through Pareto analysis is used to improve the efficiency of DNN implementations by fixing the arithmetic precision and optimal pipeline stages. The proposed architecture utilizes layer-multiplexing, a technique that effectively reuses a single DNN layer to enhance efficiency while maintaining modularity and adaptability for integrating various network configurations. The proposed CORDIC MAC-based DNN architecture is scalable for any bit-precision network size, and the DNN accelerator is prototyped using the Xilinx Virtex-7 VC707 FPGA board, operating at 66 MHz. The proposed design does not use any Xilinx macros, making it easily adaptable for ASIC implementation. Compared with state-of-the-art designs, the proposed design reduces resource use by 45% and power consumption by 4× without sacrificing performance. The accelerator is validated using the MNIST dataset, achieving 95.06% accuracy, only 0.35% less than other cutting-edge implementations.