Miracle-dz/UniFAR-FTUs huggingface dataset:
QoE-based Semantic-Aware Resource Allocation for Multi-Task Networks

In semantic communications, only task-relevant information is transmitted, yielding significant performance gains over conventional communications. To satisfy user requirements for different tasks, we investigate the semantic-aware resource allocation in a multi-cell network for serving multiple tasks in this paper. First, semantic entropy is defined and quantified to measure the semantic information for different tasks. Then, we develop a novel quality-of-experience (QoE) model to formulate the semantic-aware resource allocation problem in terms of semantic compression, channel assignment, and transmit power allocation. To solve the formulated problem, we first decouple it into two subproblems. The first one is to optimize semantic compression with given channel assignment and power allocation results, which is solved by a developed deep Q-network (DQN) based method. The second one is to optimize the channel assignment and transmit power, which is modeled as a many-to-one matching game and solved by a proposed low-complexity matching algorithm. Simulation results validate the effectiveness and superiority of the proposed semantic-aware resource allocation method, as well as its compatibility with conventional and semantic communications.
Functional renormalization group without functional integrals: Implementing Hilbert space projections for strongly correlated electrons via Hubbard X-operators

Exact functional renormalization group (FRG) flow equations for quantum systems can be derived directly within an operator formalism without using functional integrals. This simple insight opens new possibilities for applying FRG methods to models for strongly correlated electrons with projected Hilbert spaces, such as quantum spin models, the $t$-$J$ model, or the Hubbard model at infinite on-site repulsion. By representing these models in terms of Hubbard X-operators, we derive exact flow equations for the time-ordered correlation functions of the X-operators (X-FRG), which allow us to calculate the electronic correlation functions in the projected Hilbert space of these models. The Hubbard-I approximation for the single-particle Green function of the Hubbard model is recovered from a trivial truncation of the flow equations where the two-point vertex is approximated by its atomic limit. We use our approach to calculate the quasi-particle residue and damping in the ``hidden Fermi liquid'' state of the Hubbard model at infinite on-site repulsion where the Hamiltonian consists only of the projected kinetic energy.
On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm

When fitting the learning data of an individual to algorithm-like learning models, the observations are so dependent and non-stationary that one may wonder what the classical Maximum Likelihood Estimator (MLE) could do, even if it is the usual tool applied to experimental cognition. Our objective in this work is to show that the estimation of the learning rate cannot be efficient if the learning rate is constant in the classical Exp3 (Exponential weights for Exploration and Exploitation) algorithm. Secondly, we show that if the learning rate decreases polynomially with the sample size, then the prediction error and in some cases the estimation error of the MLE satisfy bounds in probability that decrease at a polynomial rate.
Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits

The Implicitly Normalized Forecaster (INF) algorithm is considered to be an optimal solution for adversarial multi-armed bandit (MAB) problems. However, most of the existing complexity results for INF rely on restrictive assumptions, such as bounded rewards. Recently, a related algorithm was proposed that works for both adversarial and stochastic heavy-tailed MAB settings. However, this algorithm fails to fully exploit the available data. In this paper, we propose a new version of INF called the Implicitly Normalized Forecaster with clipping (INF-clip) for MAB problems with heavy-tailed reward distributions. We establish convergence results under mild assumptions on the rewards distribution and demonstrate that INF-clip is optimal for linear heavy-tailed stochastic MAB problems and works well for non-linear ones. Furthermore, we show that INF-clip outperforms the best-of-both-worlds algorithm in cases where it is difficult to distinguish between different arms.
Emergence of non-Abelian SU(2) invariance in Abelian frustrated fermionic ladders

We consider a system of interacting spinless fermions on a two-leg triangular ladder with $\pi/2$ magnetic flux per triangular plaquette. Microscopically, the system exhibits a U(1) symmetry corresponding to the conservation of total fermionic charge, and a discrete $\mathbb{Z}_2$ symmetry -- a product of parity transformation and chain permutation. Using bosonization, we show that, in the low-energy limit, the system is described by the quantum double-frequency sine-Gordon model. On the basis of this correspondence, a rich phase diagram of the system is obtained. It includes trivial and topological band insulators for weak interactions, separated by a Gaussian critical line, whereas at larger interactions a strongly correlated phase with spontaneously broken $\mathbb{Z}_2$ symmetry sets in, exhibiting a net charge imbalance and non-zero total current. At the intersection of the three phases, the system features a critical point with an emergent SU(2) symmetry. This non-Abelian symmetry, absent in the microscopic description, is realized at low-energies as a combined effect of the magnetic flux, frustration, and many-body correlations. The criticality belongs to the SU(2)$_1$ Wess-Zumino-Novikov-Witten universality class. The critical point bifurcates into two Ising critical lines that separate the band insulators from the strong-coupling symmetry broken phase. We establish an analytical connection between the low-energy description of our model around the critical bifurcation point on one hand, and the Ashkin-Teller model and a weakly dimerized XXZ spin-1/2 chain on the other. We complement our field-theory understanding via tensor network simulations, providing compelling quantitative evidences of all bosonization predictions. Our findings are of interest to up-to-date cold atom experiments utilizing Rydberg dressing, that have already demonstrated correlated ladder dynamics.
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families

Even though dropout is a popular regularization technique, its theoretical properties are not fully understood. In this paper we study dropout regularization in extended generalized linear models based on double exponential families, for which the dispersion parameter can vary with the features. A theoretical analysis shows that dropout regularization prefers rare but important features in both the mean and dispersion, generalizing an earlier result for conventional generalized linear models. Training is performed using stochastic gradient descent with adaptive learning rate. To illustrate, we apply dropout to adaptive smoothing with B-splines, where both the mean and dispersion parameters are modelled flexibly. The important B-spline basis functions can be thought of as rare features, and we confirm in experiments that dropout is an effective form of regularization for mean and dispersion parameters that improves on a penalized maximum likelihood approach with an explicit smoothness penalty.
Big-PERCIVAL: Exploring the Native Use of 64-Bit Posit Arithmetic in Scientific Computing

The accuracy requirements in many scientific computing workloads result in the use of double-precision floating-point arithmetic in the execution kernels. Nevertheless, emerging real-number representations, such as posit arithmetic, show promise in delivering even higher accuracy in such computations. In this work, we explore the native use of 64-bit posits in a series of numerical benchmarks and compare their timing performance, accuracy and hardware cost to IEEE 754 doubles. In addition, we also study the conjugate gradient method for numerically solving systems of linear equations in real-world applications. For this, we extend the PERCIVAL RISC-V core and the Xposit custom RISC-V extension with posit64 and quire operations. Results show that posit64 can obtain up to 4 orders of magnitude lower mean square error than doubles. This leads to a reduction in the number of iterations required for convergence in some iterative solvers. However, leveraging the quire accumulator register can limit the order of some operations such as matrix multiplications. Furthermore, detailed FPGA and ASIC synthesis results highlight the significant hardware cost of 64-bit posit arithmetic and quire. Despite this, the large accuracy improvements achieved with the same memory bandwidth suggest that posit arithmetic may provide a potential alternative representation for scientific computing.
Regularization Properties of Dual Subgradient Flow

Dual gradient descent combined with early stopping represents an efficient alternative to the Tikhonov variational approach when the regularizer is strongly convex. However, for many relevant applications, it is crucial to deal with regularizers which are only convex. In this setting, the dual problem is non smooth, and dual gradient descent cannot be used. In this paper, we study the regularization properties of a subgradient dual flow, and we show that the proposed procedure achieves the same recovery accuracy as penalization methods, while being more efficient from the computational perspective.
Prediction of Burning Rate of HTPB Propellant by Artificial Neural Network Model

Using the existing burning rate test data of RDX/AP/Al/HTPB propellants containing nano-structure Fe_2O_3 catalyst,an artificial neural network(ANN) model based on back propagation(BP) arithmetic was built to predict the burning rate of this type of propellant,and the nonlinear relation between the content of catalysts in propellants and its burning rate was simulated.As the results,the network structure was ascertained to be the type of 3-4-1,the learning rate and momentum constant were 0.75 and 0.45 respectively,and the mean square error of network arithmetic converged to 1.0×10~(-4) after 56910 iterations.The burning rate of HTPB propellant containing nano-structure Fe_2O_3 catalyst was predicted by the trained network model,and the output of this model had good agreement with the experiment data,there was only one with larger relative error(-9.19%) among prediction values, the other prediction values all had the relative error less than 3.5%. It was suggested that the built network model has the preferable ability in aspects of remembrance and forecast to predict the burning rate of above mentioned propellants.
Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach

We develop the first active learning method in the predict-then-optimize framework. Specifically, we develop a learning method that sequentially decides whether to request the"labels"of feature samples from an unlabeled data stream, where the labels correspond to the parameters of an optimization model for decision-making. Our active learning method is the first to be directly informed by the decision error induced by the predicted parameters, which is referred to as the Smart Predict-then-Optimize (SPO) loss. Motivated by the structure of the SPO loss, our algorithm adopts a margin-based criterion utilizing the concept of distance to degeneracy and minimizes a tractable surrogate of the SPO loss on the collected data. In particular, we develop an efficient active learning algorithm with both hard and soft rejection variants, each with theoretical excess risk (i.e., generalization) guarantees. We further derive bounds on the label complexity, which refers to the number of samples whose labels are acquired to achieve a desired small level of SPO risk. Under some natural low-noise conditions, we show that these bounds can be better than the naive supervised learning approach that labels all samples. Furthermore, when using the SPO+ loss function, a specialized surrogate of the SPO loss, we derive a significantly smaller label complexity under separability conditions. We also present numerical evidence showing the practical value of our proposed algorithms in the settings of personalized pricing and the shortest path problem.
On‐Demand Indistinguishable and Entangled Photons Using Tailored Cavity Designs

The biexciton‐exciton emission cascade commonly used in quantum‐dot systems to generate polarization entanglement yields photons with intrinsically limited indistinguishability. In the present work, it focuses on the generation of pairs of photons with high degrees of polarization entanglement and simultaneously high indistinguishability. It achieves this goal by selectively reducing the biexciton lifetime with an optical resonator. It demonstrates that a suitably tailored circular Bragg reflector fulfills the requirements of sufficient selective Purcell enhancement of biexciton emission paired with spectrally broad photon extraction and twofold degenerate optical modes. The in‐depth theoretical study combines (i) the optimization of realistic photonic structures solving Maxwell's equations from which model parameters are extracted as input for (ii) microscopic simulations of quantum‐dot cavity excitation dynamics with full access to photon properties. It reports non‐trivial dependencies on system parameters and use the predictive power of the combined theoretical approach to determine the optimal range of Purcell enhancement that maximizes indistinguishability and entanglement to near unity values, here specifically for the telecom C‐band at 1550 nm.
Cosmological constraints on R2-AB model

Nowadays, efforts are being devoted to the study of alternative cosmological scenarios, in which, modifications of General Relativity (GR) theory have been proposed to explain the late cosmic acceleration, without assuming the existence of the dark energy (DE) component. We investigate the $R^2$-corrected Appleby-Battye model, or $R^2$-AB model, which consists of an $f(R)$ model with only one extra free parameter $b$, besides the cosmological parameters of the flat-$\Lambda$CDM model: $H_0$ and $\Omega_{m,0}$. Regarding this model, it was already shown that a positive value for $b$ is required for the model to be consistent with Solar System tests, moreover, the condition for the existence of a de~Sitter state requires $b \ge 1.6$. To impose observational constraints on the $R^2$-AB model, we consider in our analyses two data sets: cosmic chronometer $H(z)$ data for the background level, and $[f\sigma_8](z)$ data, for the perturbative level. The first one provides $b = 1.6^{+3.1}_{-0.0}$ and the cosmological parameters $\{H_0 ,\Omega_{m,0}\}$ in agreement to Planck values, while the second one, indicates $b = 1.76^{+2.91}_{-0.15}$ and the parameters $\{\Omega_{m,0},\sigma_{8,0} \}$ also in agreement to Planck values; in the last case the data was marginalized over the parameter $H_0$. Additionally, we perform illustrative analyses that compare this $f(R)$ model with the flat-$\Lambda$CDM model, considering several values of the parameter $b$, for diverse cosmological functions like the Hubble function $H(z)$, the equation of state $w_{eff}(z)$, the parametrized growth rate of cosmic structures $[f \sigma_8](z)$, and $\sigma_8(z)$.
A Vritual Force-Based Community Detecting Algorithm for Complex Networks

In view of the existing community partition algorithms too granular and the limitation of optimization based on the modularity,this paper proposes a community partition algorithm based on gravity,which the relation between the adjacent vertices is considered as attraction while the relation between the non-adjacent vertices is repulsion.The community structure is formed by the self-organization of vertices which are influenced by the virtual force from their neighbors.Compared with GN and CNM algorithm in the reality of the network which known community structure,the algorithm in this paper has high reliability and nearly linear time complexity.
An automatic music generation and evaluation method based on transfer learning

In recent years, deep learning has seen remarkable progress in many fields, especially with many excellent pre-training models emerged in Natural Language Processing(NLP). However, these pre-training models can not be used directly in music generation tasks due to the different representations between music symbols and text. Compared with the traditional presentation method of music melody that only includes the pitch relationship between single notes, the text-like representation method proposed in this paper contains more melody information, including pitch, rhythm and pauses, which expresses the melody in a form similar to text and makes it possible to use existing pre-training models in symbolic melody generation. In this paper, based on the generative pre-training-2(GPT-2) text generation model and transfer learning we propose MT-GPT-2(music textual GPT-2) model that is used in music melody generation. Then, a symbolic music evaluation method(MEM) is proposed through the combination of mathematical statistics, music theory knowledge and signal processing methods, which is more objective than the manual evaluation method. Based on this evaluation method and music theories, the music generation model in this paper are compared with other models (such as long short-term memory (LSTM) model,Leak-GAN model and Music SketchNet). The results show that the melody generated by the proposed model is closer to real music.
Accretion onto Protoplanetary Discs: Implications for Globular Cluster Evolution

Abstract In the past decade, observational evidence that Globular Clusters (GCs) harbour multiple stellar populations has grown steadily. These observations are hard to reconcile with the classical picture of star formation in GCs, which approximates them as a single generation of stars. Bastian et al. recently suggested an evolutionary scenario in which a second, chemically distinct, population is formed by the accretion of chemically enriched material onto the protoplanetary disc of low-mass stars in the initial GC population. Using assumptions that represent the (dynamical) conditions in a typical GC, we investigate whether a low-mass star surrounded by a protoplanetary disc can accrete sufficient enriched material to account for the observed abundances in ‘second generation’ stars. We compare the outcome of two different smoothed particle hydrodynamics codes and focus on the lifetime and stability of the disc and on the gas accretion rate onto both the star and the disc.
Seasonal patterns of bird and bat collision fatalities at wind turbines

Information on when birds and bats die from collisions with wind turbines can help refine efforts to minimize fatalities via curtailment of energy productions and can offer insight into the risk factors associated with collision fatalities. Using data pooled from 114 post-construction monitoring studies conducted at wind facilities across the United States, we described seasonal patterns of fatalities among birds and bats. Bat fatalities peaked in the fall. Silver-haired bat (Lasionycteris noctivagans), a long-distance migrant, and Mexican free-tailed bat (Tadarida brasiliensis) both showed maximum fatality counts later in the year–October and November, respectively–than any other bat species. The other common species in our sample–hoary bat (Aeorestes cinereus), Eastern red bat (Lasiurus borealis), and big brown bat (Eptesicus fuscus)–showed broadly overlapping peaks of fatality counts in August. Fatalities of silver-haired bat showed a smaller spring peak in some ecoregions; no other bat species exhibited this pattern. Seasonal patterns of bird fatalities varied among guilds. Woodland birds, many of which were long-distance migrants, showed two peaks in fatalities corresponding to spring and fall migration. Grassland birds and soaring birds, most of which were resident or short-distance migrants, did not exhibit strong seasonal peaks in fatalities. Species in these guilds tend to inhabit regions with extensive wind-energy development year-round, which may explain the more consistent numbers of fatalities that we observed. Our results highlight the value of pooling data to develop science-based solutions to reduce conflicts between wind-energy development and wildlife but also emphasize the need for more extensive data and standardization of post-construction monitoring to support more robust inferences regarding wind-wildlife interactions and collision risk.
PHYTOCHROME C regulation of photoperiodic flowering via PHOTOPERIOD1 is mediated by EARLY FLOWERING 3 in Brachypodium distachyon

Daylength sensing in many plants is critical for coordinating the timing of flowering with the appropriate season. Temperate climate-adapted grasses such as Brachypodium distachyon flower during the spring when days are becoming longer. The photoreceptor PHYTOCHROME C is essential for long-day (LD) flowering in B. distachyon. PHYC is required for the LD activation of a suite of genes in the photoperiod pathway including PHOTOPERIOD1 (PPD1) that, in turn, result in the activation of FLOWERING LOCUS T (FT1)/FLORIGEN, which causes flowering. Thus, B. distachyon phyC mutants are extremely delayed in flowering. Here we show that PHYC-mediated activation of PPD1 occurs via EARLY FLOWERING 3 (ELF3), a component of the evening complex in the circadian clock. The extreme delay of flowering of the phyC mutant disappears when combined with an elf3 loss-of-function mutation. Moreover, the dampened PPD1 expression in phyC mutant plants is elevated in phyC/elf3 mutant plants consistent with the rapid flowering of the double mutant. We show that loss of PPD1 function also results in reduced FT1 expression and extremely delayed flowering consistent with results from wheat and barley. Additionally, elf3 mutant plants have elevated expression levels of PPD1, and we show that overexpression of ELF3 results in delayed flowering associated with a reduction of PPD1 and FT1 expression, indicating that ELF3 represses PPD1 transcription consistent with previous studies showing that ELF3 binds to the PPD1 promoter. Indeed, PPD1 is the main target of ELF3-mediated flowering as elf3/ppd1 double mutant plants are delayed flowering. Our results indicate that ELF3 operates downstream from PHYC and acts as a repressor of PPD1 in the photoperiod flowering pathway of B. distachyon.
Localization of outdoor mobile estimation using a single base station scattering distance technique

Outdoor localization estimation method for mobile based on single base station scattering distance in an urban area environment is proposed in this paper. This new approach is deployed for both Line‐of‐site and NonLine‐of‐site by using a single base station technique based on the measured scattering distances which are computed from the numerous multipath components signals. Those signals are coming from the scatterers around the mobile station (MS) and the base station (BS) by utilizing the time of arrival and the Angle of arrival effects. Site of the student's hostel at Universiti Malaysia Perlis was chosen to be our proposed scenario. A proper technique depending on the prior knowledge about the geometrical coordinates of some fixed obstacles surrounds both the subscriber and only one BS is suggested to identify the MS location. As a result, the localization of 70% from the subscribers can be identified which is more satisfactory compared to 67% U.S. Federal Communications Commission standard within 125 m. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:1546–1551, 2016