Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of near-millimeter waveguiding structures using surface magnetopiasmons.

ANALYSIS OF NEAR-MILLIMETER WAVEGUIDING STRUCTURES USING SURFACE MAGNETOPLASMONS Together with the advantagious use in integrated circuit technology, doped semiconductors show interesting anisotropic characteristics under the application of a magnetic biasing field when exposed to electromagnetic excitations. Parameters can be chosen such that a near-millimeter wave interacts with the semiconducting material to exhibit non-reciprocal propagation. Such a behavior is considered in the fundamental case of the plane interface between dielectric and semiconductor regions to exploit the similarity in the field displacement effects of magnetoplasmons and edge-guided waves in ferrites (EGW). Subsequently, the curved single interface is analysed. The exact dispersion relation is derived and approximate expressions are given for the additional to the attenuation constant due to curvature effects. The last structure considered includes five regions defining two semiconducting slabs surrounded by dielectrics. The results of an exact analysis of the complex dispersion relation are given to provide the basic modal behavior. This is then used in the analysis of isolation and phase-shift capabilities. TABLE I: 1 1ST OF SYMBOLS to = angular frequency (rad/s) to =yne/e m* (plasma frequency, rad/s) to = eB /m* (cyclotron frequency, rad/s) n = carrier concentration (m ) e = electron charge (coulombs) m* = electron effective mass. (Kg) = 0.067 me (for GaAs) m = electron rest mass (Kg) B = d.c. magnetizing field (v/eb/m) o'o = vacuum permittivity and permeability, respectively E^ 0 ' = static dielectric constant of the semiconducting material = 12.95 (for GaAs) v = 1/x (collision frequency, rad/s) T = momentum relaxation time of the semiconducting material Y = a + j3 (complex propagation constant) a = attenuation constant 3 = phase constant k= transverse wave number of the i-th region e. = relative dielectric constant of the i-th region ee(u) = effective relative dielectric constant of the semiconducti medium (e (w) = E2) 1'2'3 = Wldtns °f tne ^ regions ng
An order aggregation and scheduling problem for meal delivery

We address a single-machine scheduling problem motivated by a last-mile-delivery setting for a food company. Customers place orders, each characterized by a delivery point (customer location) and an ideal delivery time. An order is considered on time if it is delivered to the customer within a time window given by the ideal delivery time ±δ, where δ is the same for all orders. A single courier (machine) is in charge of delivery to all customers. Orders are either delivered individually, or two orders can be aggregated in a single courier trip. All trips start and end at the restaurant, so no routing decisions are needed. The problem is to schedule courier trips so that the number of late orders is minimum. We show that the problem with order aggregation is NP-hard and propose a combinatorial branch and bound algorithm for its solution. The algorithm performance is assessed through a computational study on instances derived by a real-life application and on randomly generated instances. The behavior of the combinatorial algorithm is compared with that of the best ILP formulation known for the problem. Through another set of computational experiments, we also show that an appropriate choice of design parameters allows applying the algorithm to a dynamic context, with orders arriving over time.
ComEx: Comment Exploration on Online News Platforms

The comment sections of online news platforms have shaped the way in which people express their opinion online. However, due to the overwhelming number of comments, no in-depth discussions emerge. To foster more interactive and engaging discussions, we propose our ComEx interface for the exploration of reader comments on online news platforms. Potential discussion participants can get a quick overview and are not discouraged by an abundance of comments. It is our goal to represent the discussion in a graph of comments that can be used in an interactive user interface for exploration. To this end, a processing pipeline fetches comments from several different platforms and adds edges in the graph based on topical similarity or meta-data and ranks nodes on metrics such as controversy or toxicity. By interacting with the graph, users can explore and react to single comments or entire threads they are interested in.
Strategies to Improve Antimicrobial Utilization with a Special Focus on Developing Countries

Antimicrobial resistance (AMR) is a high priority across countries as it increases morbidity, mortality and costs. Concerns with AMR have resulted in multiple initiatives internationally, nationally and regionally to enhance appropriate antibiotic utilization across sectors to reduce AMR, with the overuse of antibiotics exacerbated by the COVID-19 pandemic. Effectively tackling AMR is crucial for all countries. Principally a narrative review of ongoing activities across sectors was undertaken to improve antimicrobial use and address issues with vaccines including COVID-19. Point prevalence surveys have been successful in hospitals to identify areas for quality improvement programs, principally centering on antimicrobial stewardship programs. These include reducing prolonged antibiotic use to prevent surgical site infections. Multiple activities centering on education have been successful in reducing inappropriate prescribing and dispensing of antimicrobials in ambulatory care for essentially viral infections such as acute respiratory infections. It is imperative to develop new quality indicators for ambulatory care given current concerns, and instigate programs with clear public health messaging to reduce misinformation, essential for pandemics. Regular access to effective treatments is needed to reduce resistance to treatments for HIV, malaria and tuberculosis. Key stakeholder groups can instigate multiple initiatives to reduce AMR. These need to be followed up.
Relaxation Behaviour and Dipole Moment of Some Nonionics. I. CMC and Thermodynamics of Ethoxylated Oleic Acid in Nonaqueous Medium

Some homologous oleic acid ethoxylates have been prepared and their critical micelle concentrations (CMC) in a nonaqueous medium have been determined by the electrical conductivity method, dipole moment calculations and dielectric relaxation measurements. The relaxation behaviour of these surfactants in benzene solutions from microwave absorption measurements has been studied over the frequency region 0.2 to 18 GHz at different temperatures. The effect of the polyoxyethylene (POE) chain length in the hydrophilic head on the CMC values has been investigated. Thermodynamic parameters up to the concentrations where micellization occurs have been discussed.
Performance of Mutual Funds Management of Bangladesh-Evidence from Close End Mutual Funds in Dhaka Stock Exchange Limited (DSE)

The mutual fund market in Bangladesh is still small though the concept of the mutual fund was introduced in the year of 1980. Mutual fund sector is lucrative at all over the world where it has much impact on GDP and total market capitalization but Bangladesh is lagging behind in this sector and unable to make proper use of this sector. Open end mutual fund performance is quite good rather than close end mutual fund in Bangladesh. Weekly NAV data of 10 close end mutual fund has been taken in this paper. Not all the funds are doing bad, still few funds‟ performance are extraordinary. Management's inefficiency, lack of trust of investors and poor governance by regulator are the main reasons behind the poor performance of mutual fund sector in Bangladesh. Moreover, the recent unusual upsurge in the stock markets, the redemption of the matured funds, unstable margin rule, unskilled investors, asymmetric information, declaration of re-investment units as dividend have made the mutual fund industry much tougher than ever before. To bring back the confidence of the investors in the mutual fund sector this paper is bringing some recommendations to reduce the stressors up to a certain level.
Grandparental Childcare, Maternal Labour Force Participation, and Birth of the Second Child: Evidence from China

This study investigates the effect of grandparental childcare on the second birth in China when considering the mediating role of maternal labor participation, drawing on the China Migrants Dynamic Survey in 2016. By controlling sample selection bias and endogeneity with an endogenous switching Probit regression model, our analysis finds that grandparental childcare of the first child has a positive and significant effect on the second birth regardless of whether the mother participates in the labor market or not. To further understand how the positive effect comes to be, we make a mediation analysis which demonstrates the mediating effect of maternal labor force participation. It indicates a significant and negative effect of grandparenting on the second birth through promoting maternal employment. However, due to the positive, direct effect of the grandparental childcare on the second birth, the total effect is still positive.
Partisan Fertility and Presidential Elections

Changes in political leadership drive large changes in economic optimism. We exploit the surprise 2016 election of Trump to identify the effects of a shift in political power on one of the most consequential household decisions: whether to have a child. Republican-leaning counties experience a sharp and persistent increase in fertility relative to Democratic counties: a 1.1 to 2.6 percentage point difference in annual births, depending on the intensity of partisanship. In addition, Hispanics see fertility fall relative to non-Hispanics, especially compared to rural or evangelical whites. Further, following Trump pre-election campaign visits, relative Hispanic fertility declines.
Dynamical Perturbations of Tetrahydroborate Anions in LiBH4 due to Nanoconfinement in Controlled-Pore Carbon Scaffolds

Neutron vibrational spectroscopy and quasielastic neutron scattering (QENS) were used to probe the dynamical properties of BH4– anions in both bulk LiBH4 and LiBH4 confined in nanoporous carbons (NPCs) having ≤4-nm-diameter, hexagonally arranged, cylindrical pores. The BH4– torsional band of the confined LiBH4 is significantly broadened relative to that of bulk LiBH4, reflecting a disruption of the bulk crystal lattice and thus a broader distribution of BH4– rotational potentials. QENS measurements of bulk orthorhombic LiBH4 indicate a single quasielastic component yielding an activation energy for localized BH4– jump reorientation of 19.2 ± 0.8 kJ/mol, consistent with previous QENS and NMR results. At room temperature, the measurements are in good agreement with BH4– reorientational jumps about a single C2 or C3 tetrahedral symmetry axis, with evidence for multiaxis rotations emerging as the temperature increases. In contrast, the QENS spectra of the NPC-confined LiBH4 exhibit two quasielastic components.
On the Optimality and Convergence Properties of the Iterative Learning Model Predictive Controller

In this technical article, we analyze the performance improvement and optimality properties of the learning model predictive control (LMPC) strategy for linear deterministic systems. The LMPC framework is a policy iteration scheme where closed-loop trajectories are used to update the control policy for the next execution of the control task. We show that, when a linear independence constraint qualification (LICQ) condition holds, the LMPC scheme guarantees strict iterative performance improvement and optimality, meaning that the closed-loop cost evaluated over the entire task converges asymptotically to the optimal cost of the infinite-horizon control problem. Compared to previous works, this sufficient LICQ condition can be easily checked, it holds for a larger class of systems and it can be used to adaptively select the prediction horizon of the controller, as demonstrated by a numerical example.
Finiteness properties for relatives of braided Higman–Thompson groups

We study the finiteness properties of the braided Higman--Thompson group $bV_{d,r}(H)$ with labels in $H\leq B_d$, and $bF_{d,r}(H)$ and $bT_{d,r}(H)$ with labels in $H\leq PB_d$ where $B_d$ is the braid group with $d$ strings and $PB_d$ is its pure braid subgroup. We show that for all $d\geq 2$ and $r\geq 1$, the group $bV_{d,r}(H)$ (resp. $bT_{d,r}(H)$ or $bF_{d,r}(H)$) is of type $F_n$ if and only if $H$ is. Our result in particular confirms a recent conjecture of Aroca and Cumplido.
Data-driven, structure-preserving approximations to entropy-based moment closures for kinetic equations

We present a data-driven approach to construct entropy-based closures for the moment system from kinetic equations. The proposed closure learns the entropy function by fitting the map between the moments and the entropy of the moment system, and thus does not depend on the space-time discretization of the moment system and specific problem configurations such as initial and boundary conditions. With convex and $C^2$ approximations, this data-driven closure inherits several structural properties from entropy-based closures, such as entropy dissipation, hyperbolicity, and H-Theorem. We construct convex approximations to the Maxwell-Boltzmann entropy using convex splines and neural networks, test them on the plane source benchmark problem for linear transport in slab geometry, and compare the results to the standard, optimization-based M$_N$ closures. Numerical results indicate that these data-driven closures provide accurate solutions in much less computation time than the M$_N$ closures.
WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution

Audio super-resolution is the task of constructing a high-resolution (HR) audio from a low-resolution (LR) audio by adding the missing band. Previous methods based on convolutional neural networks and mean squared error training objective have relatively low performance, while adversarial generative models are difficult to train and tune. Recently, normalizing flow has attracted a lot of attention for its high performance, simple training and fast inference. In this paper, we propose WSRGlow, a Glow-based waveform generative model to perform audio super-resolution. Specifically, 1) we integrate WaveNet and Glow to directly maximize the exact likelihood of the target HR audio conditioned on LR information; and 2) to exploit the audio information from low-resolution audio, we propose an LR audio encoder and an STFT encoder, which encode the LR information from the time domain and frequency domain respectively. The experimental results show that the proposed model is easier to train and outperforms the previous works in terms of both objective and perceptual quality. WSRGlow is also the first model to produce 48kHz waveforms from 12kHz LR audio.
Quantum spin compass models in two-dimensional electronic topological metasurfaces

We consider a metasurface consisting of a square lattice of cylindrical antidots in a two-dimensional topological insulator (2DTI). Each antidot supports a degenerate Kramer’s pair of eigenstates formed by the helical topological edge states. We show that the on-site Coulomb repulsion leads to the onset of the Mott insulator phase in the system in a certain range of experimentally relevant parameters. Intrinsic strong spin-orbit coupling characteristic for the 2DTI supports a rich class of the emerging low-energy spin Hamiltonians which can be emulated in the considered system, which makes it an appealing solid state platform for quantum simulations of strongly correlated electron systems.
GemNet: Universal Directional Graph Neural Networks for Molecules

Effectively predicting molecular interactions has the potential to accelerate molecular dynamics by multiple orders of magnitude and thus revolutionize chemical simulations. Graph neural networks (GNNs) have recently shown great successes for this task, overtaking classical methods based on fixed molecular kernels. However, they still appear very limited from a theoretical perspective, since regular GNNs cannot distinguish certain types of graphs. In this work we close this gap between theory and practice. We show that GNNs with directed edge embeddings and two-hop message passing are indeed universal approximators for predictions that are invariant to translation, and equivariant to permutation and rotation. We then leverage these insights and multiple structural improvements to propose the geometric message passing neural network (GemNet). We demonstrate the benefits of the proposed changes in multiple ablation studies. GemNet outperforms previous models on the COLL, MD17, and OC20 datasets by 34%, 41%, and 20%, respectively, and performs especially well on the most challenging molecules. Our implementation is available online.
Uncorrelated problem-specific samples of quantum states from zero-mean Wishart distributions

Random samples of quantum states are an important resource for various tasks in quantum information science, and samples in accordance with a problem-specific distribution can be indispensable ingredients. Some algorithms generate random samples by a lottery that follows certain rules and yield samples from the set of distributions that the lottery can access. Other algorithms, which use random walks in the state space, can be tailored to any distribution, at the price of autocorrelations in the sample and with restrictions to low-dimensional systems in practical implementations. In this paper, we present a two-step algorithm for sampling from the quantum state space that overcomes some of these limitations. We first produce a CPU-cheap large proposal sample, of uncorrelated entries, by drawing from the family of complex Wishart distributions, and then reject or accept the entries in the proposal sample such that the accepted sample is strictly in accordance with the target distribution. We establish the explicit form of the induced Wishart distribution for quantum states. This enables us to generate a proposal sample that mimics the target distribution and, therefore, the efficiency of the algorithm, measured by the acceptance rate, can be many orders of magnitude larger than that for a uniform sample as the proposal. We demonstrate that this sampling algorithm is very efficient for one-qubit and two-qubit states, and reasonably efficient for three-qubit states, while it suffers from the"curse of dimensionality"when sampling from structured distributions of four-qubit states.
A Spiking Neural Network for Image Segmentation

We seek to investigate the scalability of neuromorphic computing for computer vision, with the objective of replicating non-neuromorphic performance on computer vision tasks while reducing power consumption. We convert the deep Artificial Neural Network (ANN) architecture U-Net to a Spiking Neural Network (SNN) architecture using the Nengo framework. Both rate-based and spike-based models are trained and optimized for benchmarking performance and power, using a modified version of the ISBI 2D EM Segmentation dataset consisting of microscope images of cells. We propose a partitioning method to optimize inter-chip communication to improve speed and energy efficiency when deploying multi-chip networks on the Loihi neuromorphic chip. We explore the advantages of regularizing firing rates of Loihi neurons for converting ANN to SNN with minimum accuracy loss and optimized energy consumption. We propose a percentile based regularization loss function to limit the spiking rate of the neuron between a desired range. The SNN is converted directly from the corresponding ANN, and demonstrates similar semantic segmentation as the ANN using the same number of neurons and weights. However, the neuromorphic implementation on the Intel Loihi neuromorphic chip is over 2x more energy-efficient than conventional hardware (CPU, GPU) when running online (one image at a time). These power improvements are achieved without sacrificing the task performance accuracy of the network, and when all weights (Loihi, CPU, and GPU networks) are quantized to 8 bits.
Pricing and Risk Analysis in Hyperbolic Local Volatility Model with Quasi Monte Carlo

Local volatility models usually capture the surface of implied volatilities more accurately than other approaches, such as stochastic volatility models. We present the results of application of Monte Carlo (MC) and Quasi Monte Carlo (QMC) methods for derivative pricing and risk analysis based on Hyperbolic Local Volatility Model. In high-dimensional integration QMC shows a superior performance over MC if the effective dimension of an integrand is not too large. In application to derivative pricing and computation of Greeks effective dimensions depend on path discretization algorithms. The results presented for the Asian option show the superior performance of the Quasi Monte Carlo methods especially for the Brownian Bridge discretization scheme.
Multilinear Dirichlet Processes

Dependent Dirichlet processes (DDP) have been widely applied to model data from distributions over collections of measures which are correlated in some way. On the other hand, in recent years, increasing research efforts in machine learning and data mining have been dedicated to dealing with data involving interactions from two or more factors. However, few researchers have addressed the heterogeneous relationship in data brought by modulation of multiple factors using techniques of DDP. In this paper, we propose a novel technique, MultiLinear Dirichlet Processes (MLDP), to constructing DDPs by combining DP with a state-of-the-art factor analysis technique, multilinear factor analyzers (MLFA). We have evaluated MLDP on real-word data sets for different applications and have achieved state-of-the-art performance.
Analysis and Optimisation of Bellman Residual Errors with Neural Function Approximation

Recent development of Deep Reinforcement Learning (DRL) has demonstrated superior performance of neural networks in solving challenging problems with large or even continuous state spaces. One specific approach is to deploy neural networks to approximate value functions by minimising the Mean Squared Bellman Error (MSBE) function. Despite great successes of DRL, development of reliable and efficient numerical algorithms to minimise the MSBE is still of great scientific interest and practical demand. Such a challenge is partially due to the underlying optimisation problem being highly non-convex or using incomplete gradient information as done in Semi-Gradient algorithms. In this work, we analyse the MSBE from a smooth optimisation perspective and develop an efficient Approximate Newton's algorithm. First, we conduct a critical point analysis of the error function and provide technical insights on optimisation and design choices for neural networks. When the existence of global minima is assumed and the objective fulfils certain conditions, suboptimal local minima can be avoided when using over-parametrised neural networks. We construct a Gauss Newton Residual Gradient algorithm based on the analysis in two variations. The first variation applies to discrete state spaces and exact learning. We confirm theoretical properties of this algorithm such as being locally quadratically convergent to a global minimum numerically. The second employs sampling and can be used in the continuous setting. We demonstrate feasibility and generalisation capabilities of the proposed algorithm empirically using continuous control problems and provide a numerical verification of our critical point analysis. We outline the difficulties of combining Semi-Gradient approaches with Hessian information. To benefit from second-order information complete derivatives of the MSBE must be considered during training.