Miracle-dz/UniFAR-FTUs huggingface dataset:
Orbit harmonics for the union of two orbits

Garsia and Procesi, in their study of Springer’s representation, proved that the cohomology ring of a Springer ﬁber is isomorphic to the associated graded ring of the coordinate ring of the S n orbit of a single point in C n . This construction was an essential tool in their analysis of the Springer representation, and variations of it have reappeared recently in several other combinatorial and geometric contexts under the name orbit harmonics . In this article, we analyze the orbit harmonics of a union of two S n orbits. We prove that when the coordinate sums of the two orbits are diﬀerent, the corresponding graded S n representation is a direct sum of two Springer representations, one of which is shifted in degree by 1.
Machine learning and deep learning—A review for ecologists

The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI) has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML and DL algorithms are often perceived as opaque, and their relationship to classical data analysis tools remains debated. Although it is often assumed that ML and DL excel primarily at making predictions, ML and DL can also be used for analytical tasks traditionally addressed with statistical models. Moreover, most recent discussions and reviews on ML focus mainly on DL, failing to synthesise the wealth of ML algorithms with different advantages and general principles. Here, we provide a comprehensive overview of the field of ML and DL, starting by summarizing its historical developments, existing algorithm families, differences to traditional statistical tools, and universal ML principles. We then discuss why and when ML and DL models excel at prediction tasks and where they could offer alternatives to traditional statistical methods for inference, highlighting current and emerging applications for ecological problems. Finally, we summarize emerging trends such as scientific and causal ML, explainable AI, and responsible AI that may significantly impact ecological data analysis in the future. We conclude that ML and DL are powerful new tools for predictive modelling and data analysis. The superior performance of ML and DL algorithms compared to statistical models can be explained by their higher flexibility and automatic data‐dependent complexity optimization. However, their use for causal inference is still disputed as the focus of ML and DL methods on predictions creates challenges for the interpretation of these models. Nevertheless, we expect ML and DL to become an indispensable tool in ecology and evolution, comparable to other traditional statistical tools.
Generalised Mathematical Formulations for Non-Linear Optimized Scheduling

—In practice, most of the optimization problems are non-linear requiring certain interactive solutions and approaches to model. In 5G Advanced and Beyond network slicing, math- ematically modeling the users, type of service distributions and it’s adaptive SLAs are complex due to several dependencies. To facilitate the above, in this paper, we present novel Non-linear mathematical formulations and results that will form the base to achieve Optimized Scheduling.
Grey wolf optimization (GWO) with the convolution neural network (CNN)-based pattern recognition system

ABSTRACT The dynamic video frame dataset’s automated feature analysis addresses the complexity of intensity mapping with normal and abnormal classes. Iterative modelling is needed to learn the component of a video frame in several patterns for various video frame data types for threshold-based data clustering and feature analysis. GWO optimises the Convoluted Pattern of Wavelet Transform (CPWT) feature vectors employed in this paper's CNN feature analysis technique. A median filter reduces noise and smooths the video frame before normalising it. Edge information represents the video frame's bright spot boundary. Neural network based video frame classification clusters pixels using feature recurrent learning with minimal dataset training. The filtered video frame's features were evaluated using complex wavelet transformation feature extraction algorithms. These features demonstrate video frame spatial and textural classifications. CNN classifiers help analyse video frame instances and classify action labels. Categorization improves with the fewest training datasets. This strategy may be beneficial if compared to optimal practises.
Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship

This paper describes a method to enrich lexical resources with content relating to linguistic diversity, based on knowledge from the field of lexical typology. We capture the phenomenon of diversity through the notion of lexical gap and use a systematic method to infer gaps semi-automatically on a large scale, which we demonstrate on the kinship domain. The resulting free diversity-aware terminological resource consists of 198 concepts, 1,911 words, and 37,370 gaps in 699 languages. We see great potential in the use of resources such as ours for the improvement of a variety of cross-lingual NLP tasks, which we illustrate through an application in the evaluation of machine translation systems.
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets

In addition to generating data and annotations, devising sensible data splitting strategies and evaluation metrics is essential for the creation of a benchmark dataset. This practice ensures consensus on the usage of the data, homogeneous assessment, and uniform comparison of research methods on the dataset. This study focuses on CholecT50, which is a 50 video surgical dataset that formalizes surgical activities as triplets of. In this paper, we introduce the standard splits for the CholecT50 and CholecT45 datasets and show how they compare with existing use of the dataset. CholecT45 is the first public release of 45 videos of CholecT50 dataset. We also develop a metrics library, ivtmetrics, for model evaluation on surgical triplets. Furthermore, we conduct a benchmark study by reproducing baseline methods in the most predominantly used deep learning frameworks (PyTorch and TensorFlow) to evaluate them using the proposed data splits and metrics and release them publicly to support future research. The proposed data splits and evaluation metrics will enable global tracking of research progress on the dataset and facilitate optimal model selection for further deployment.
Autonomous Mobile Clinics: Empowering Affordable Anywhere, Anytime Healthcare Access

Autonomous mobile clinics (AMCs) solve the healthcare access problem by bringing healthcare services to the patient by the order of the patient's fingertips. Nevertheless, to enable a universal AMC network, a three-stage technical roadmap needs to be achieved. In stage one, we focus on solving the inequity challenge in the existing healthcare system by combining autonomous mobility and telemedicine. In stage two, we develop an artificial intelligence (AI) doctor for primary care, which we foster from infancy to adulthood with clean healthcare data. With the AI doctor, we can solve the inefficiency problem. In stage three, after we have proven that the AMC network can truly solve the target clinical use cases, we shall open up the platform for all medical verticals, thus enabling universal healthcare through this whole new system.
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares

We consider potentially non-convex optimization problems, for which optimal rates of approximation depend on the dimension of the parameter space and the smoothness of the function to be optimized. In this paper, we propose an algorithm that achieves close to optimal a priori computational guarantees, while also providing a posteriori certificates of optimality. Our general formulation builds on infinite-dimensional sums-of-squares and Fourier analysis, and is instantiated on the minimization of multivariate periodic functions.
A Selection of Hα Emitters at z = 2.1–2.5 Using the K s -band Photometry of ZFOURGE

Large and less-biased samples of star-forming galaxies are essential to investigate galaxy evolution. Hα emission line is one of the most reliable tracers of star-forming galaxies because its strength is directly related to recent star formation. However, it is observationally expensive to construct large samples of Hα emitters by spectroscopic or narrowband imaging survey at high redshifts. In this work, we demonstrate a method to extract Hα fluxes of galaxies at z = 2.1–2.5 from K s broadband photometry of ZFOURGE catalog. Combined with 25–39 other filters, we estimate the emission-line fluxes by SED fitting with stellar population models that incorporate emission-line strengths. 2005 galaxies are selected as Hα emitters by our method and their fluxes show good agreement with previous measurements in the literature. On the other hand, there are more Hα luminous galaxies than previously reported. The discrepancy can be explained by extended Hα profiles of massive galaxies and a luminosity dependence of dust attenuation, which are not taken into account in the previous work. We also find that there are a large number of low-mass galaxies with a much higher specific star formation rate (sSFR) than expected from the extrapolated star formation main sequence. Such low-mass galaxies exhibit larger ratios between Hα and UV fluxes compared to more massive high sSFR galaxies. This result implies that a “starburst” mode may differ among galaxies: low-mass galaxies appear to assemble their stellar mass via short-duration bursts, while more massive galaxies tend to experience longer-duration (>10 Myr) bursts.
On Turing-Turing bifurcation of partial functional differential equations and its induced superposition patterns

When two Turing modes interact, i.e., Turing-Turing bifurcation occurs, superposition patterns revealing complex dynamical phenomena appear. In this paper, superposition patterns resulting from Turing-Turing bifurcation are investigated in theory. Firstly, the third-order normal form locally topologically equivalent to original partial functional di ﬀ erential equations (PFDEs) is derived. When selecting 1D domain and Neumann boundary conditions, three normal forms describing di ﬀ erent spatial patterns are deduced from original third-order normal form. Also, formulas for computing coe ﬃ cients of these normal forms are given, which are expressed in explicit form of original system parameters. With the aid of three normal forms, spatial patterns of a di ﬀ usive predator-prey system with Crowley-Martin functional response near Turing-Turing singularity are investigated. For one set of parameters, di ﬀ usive system supports the coexistence of four stable steady states with di ﬀ erent single characteristic wavelengths, which demonstrates our previous conjecture. For another set of parameters, superposition patterns, tri-stable patterns that a pair of stable superposition steady states coexists with the stable coexistence equilibrium or another stable steady state, as well as quad-stable patterns that a pair of stable superposition steady states and another pair of stable steady states coexist, arise. Finally, numerical simulations are shown to support theory analysis.
Valley polarization fluctuations, bistability, and switching in two-dimensional semiconductors

We study theoretically nonlinear valley polarization dynamics of excitons in atom-thin semiconductors. The presence of significant polarization slows down valley relaxation due to an effective magnetic field resulting from exciton-exciton interactions. We address temporal dynamics of valley polarized excitons and study the steady states of the polarized exciton gas. We demonstrate bistability of the valley polarization where two steady states with low and high valley polarization are formed. We study the effects of fluctuations and noise in such system. We evaluate valley polarization autocorrelation functions and demonstrate that for a high-polarization regime the fluctuations are characterized by high amplitude and long relaxation time. We study the switching between the low- and high-valley polarized states caused by the noise in the system and demonstrate that the state with high valley polarization is preferential in a wide range of pumping rates.
Assessing hierarchies by their consistent segmentations

Current approaches to generic segmentation start by creating a hierarchy of nested image partitions and then specifying a segmentation from it. Our first contribution is to describe several ways, most of them new, for specifying segmentations using the hierarchy elements. Then, we consider the best hierarchy-induced segmentation specified by a limited number of hierarchy elements. We focus on a common quality measure for binary segmentations, the Jaccard index (also known as IoU). Optimizing the Jaccard index is highly nontrivial, and yet we propose an efficient approach for doing exactly that. This way we get algorithm-independent upper bounds on the quality of any segmentation created from the hierarchy. We found that the obtainable segmentation quality varies significantly depending on the way that the segments are specified by the hierarchy elements, and that representing a segmentation with only a few hierarchy elements is often possible.
Full one-loop radiative corrections to 
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We compute the full one-loop radiative corrections for charged scalar pair production $e^{+}e^{-}\to H^{+}H^{-}$ in the inert doublet model. The on-shell renormalization scheme has been used. We take into account both the weak contributions as well as the soft and hard QED corrections. We compute both the real emission and the one-loop virtual corrections using the Feynman diagrammatic method. The resummed cross section is introduced to cure the Coulomb singularity which occurs in the QED corrections. We have analyzed the parameter space of the inert doublet model in three scenarios after taking into account theoretical constraints, the collider experimental bounds, and dark matter search bounds as well. It is found that the weak interaction dominates the radiative corrections, and its size is determined by the triple Higgs coupling $\lambda_{h^0 H^+ H^-}$, which is further connected to the mass of the charged scalar. In the scenario where all the constraints are taken into account, we find that for $\sqrt{s}=250$ GeV and $\sqrt{s}=500$ GeV, the weak corrections are around $-6\% \sim-5\%$ and $-10\% \sim -3\%$, respectively. While for $\sqrt{s}=1000$ GeV, the weak corrections can reach $-15\% \sim +25\%$. The new feature is that the weak corrections can be positive near the threshold when the charged scalar is heavier than 470 GeV. Six benchmark points for future collider searches have been proposed.
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data

A Virtual Patient (VP) is a powerful tool for training medical students to take patient histories, where responding to a diverse set of spoken questions is essential to simulate natural conversations with a student. The performance of such a Spoken Language Understanding system (SLU) can be adversely affected by both the presence of Automatic Speech Recognition (ASR) errors in the test data and a high degree of class imbalance in the SLU training data. While these two issues have been addressed separately in prior work, we develop a novel two-step training methodology that tackles both these issues effectively in a single dialog agent. As it is difﬁcult to collect spoken data from users without a functioning SLU system, our method does not rely on spoken data for training, rather we use an ASR error predictor to “speechify” the text data. Our method shows signiﬁcant improvements over strong baselines on the VP intent classiﬁcation task at various word error rate settings.
Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios

When learning to behave in a stochastic environment where safety is critical, such as driving a vehicle in traffic, it is natural for human drivers to plan fallback strategies as a backup to use if ever there is an unexpected change in the environment. Knowing to expect the unexpected, and planning for such outcomes, increases our capability for being robust to unseen scenarios and may help prevent catastrophic failures. Control of Autonomous Vehicles (AVs) has a particular interest in knowing when and how to use fallback strategies in the interest of safety. Due to imperfect information available to an AV about its environment, it is important to have alternate strategies at the ready which might not have been deduced from the original training data distribution. In this paper we present a principled approach for a model-free Reinforcement Learning (RL) agent to capture multiple modes of behaviour in an environment. We introduce an extra pseudo-reward term to the reward model, to encourage exploration to areas of state-space different from areas privileged by the optimal policy. We base this reward term on a distance metric between the trajectories of agents, in order to force policies to focus on different areas of state-space than the initial exploring agent. Throughout the paper, we refer to this particular training paradigm as learning fallback strategies. We apply this method to an autonomous driving scenario and show that we are able to learn useful policies that would have otherwise been missed out on during training, and would have been unavailable to the agent when executing the control algorithm.
VWR2A: a very-wide-register reconfigurable-array architecture for low-power embedded devices

Edge-computing requires high-performance energy-efficient embedded systems. Fixed-function or custom accelerators, such as FFT or FIR filter engines, are very efficient at implementing a particular functionality for a given set of constraints. However, they are inflexible when facing application-wide optimizations or functionality upgrades. Conversely, programmable cores offer higher flexibility, but often with a penalty in area, performance, and, above all, energy consumption. In this paper, we propose VWR2A, an architecture that integrates high computational density and low power memory structures (i.e., very-wide registers and scratchpad memories). VWR2A narrows the energy gap with similar or better performance on FFT kernels with respect to an FFT accelerator. Moreover, VWR2A flexibility allows to accelerate multiple kernels, resulting in significant energy savings at the application level.
Numerical Optimization of a Nanophotonic Cavity by Machine Learning for Near-Unity Photon Indistinguishability at Room Temperature

Room-temperature (RT), on-chip deterministic generation of indistinguishable photons coupled to photonic integrated circuits is key for quantum photonic applications. Nevertheless, high indistinguishability (I) at RT is difficult to obtain due to the intrinsic dephasing of most deterministic single-photon sources (SPS). Here, we present a numerical demonstration of the design and optimization of a hybrid slot-Bragg nanophotonic cavity that achieves a theoretical near-unity I and a high coupling efficiency (β) at RT for a variety of single-photon emitters. Our numerical simulations predict modal volumes in the order of 10–3(λ/2n)3, allowing for strong coupling of quantum photonic emitters that can be heterogeneously integrated. We show that high I and β should be possible by fine-tuning the quality factor (Q) depending on the intrinsic properties of the single-photon emitter. Furthermore, we perform a machine learning optimization based on the combination of a deep neural network and a genetic algorithm (GA) to further decrease the modal volume by almost 3 times while relaxing the tight dimensions of the slot width required for strong coupling. The optimized device has a slot width of 20 nm. The design requires fabrication resolution in the limit of the current state-of-the-art technology. Also, the condition for high I and β requires a positioning accuracy of the quantum emitter at the nanometer level. Although the proposal is not a scalable technology, it can be suitable for experimental demonstration of single-photon operation.
Undergraduate Engineers Engaging and Reflecting in a Professional Practice Simulation

Cynthia D’Angelo, Ph.D. has a background in physics and science education. She has always been interested in improving science instruction and most recently, using simulations and games to help facilitate learning. Among other things, she is interested in how students make use of multimedia representations of scientific concepts in games. She is currently the research director for the Epistemic Games Group at the University of Wisconsin Madison.
Law School of the Air: An Example of Adaptive Technology in Legal Education

Charles Darwin University (CDU) is unique in having the only law school in Australia whose distance program does not require a residential component. A key and innovative feature in teaching law externally at this regional and remote university is the use of web based audio conferencing to conduct tutorials. This paper is an exploratory reflection on the implementation of this educational technology. It describes the background to the introduction of the technology, how it works and practical experience to date. Within the online learning context and the place and value of synchronous and asynchronous components, the authors conclude that there is a need for further research into the effectiveness of the media and teaching methods being employed. This evidence will then be able to inform decisions about the future design of the Law School’s external program.
Realising social value within the design and delivery of Highway England infrastructure projects: final report

Executive Summary: This report presents the outcome of a study undertaken to open up the debate around social value (SV) and what it means for the Highways England (HE) and its supply chain (SC) companies with regard to scheme design and delivery of the strategic road network (SRN). The report did not evaluate current performance, but recommends the actions needed to deliver SV requirements on schemes via a structured framework.
Research on Modeling and Simulation of Information Cocoon Based on Opinion Dynamics

In order to excavate the internal mechanism of information cocoon, according to the theory of system science, the paper built information cocoon model from the perspective of opinion dynamics and the model was simulated by NetLogo. Based on community structure, the study set the interaction background, which introduced the trust threshold between communities, so that different communities could interact with each other according to certain threshold. The different values of different communities are expressed by the difference of trust thresholds inside and outside communities. The simulation results show that information cocoon exist and have deep and shallow levels; Increasing the tolerance of communication with heterogeneous communities has a positive effect on restraining cocoon; To establish a mechanism to increase the connection between heterogeneous communities is helpful to inhibit the occurrence of cocoon phenomenon; The opinion tends to be close to the mainstream opinion in the final state, and targeted “cocoon-breaking” measures have been proposed.