Miracle-dz/UniFAR-FTUs huggingface dataset:
AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties

Network slicing (NwS) is one of the main technologies in the  h-generation of mobile communication and beyond (5G+). One of the important challenges in the NwS is information uncertainty which mainly involves demand and channel state information (CSI). Demand uncertainty is divided into three types: number of users requests, amount of bandwidth, and requested virtual network functions workloads. Moreover, the CSI uncertainty is modeled by three methods: worst-case, probabilistic, and hybrid. In this paper, our goal is to maximize the utility of the infrastructure provider by exploiting deep reinforcement learning algorithms in end-to-end NwS resource allocation under demand and CSI uncertainties. e proposed formulation is a nonconvex mixed-integer non-linear programming problem. To perform robust resource allocation in problems that involve uncertainty, we need a history of previous information. To this end, we use a recurrent deterministic policy gradient (RDPG) algorithm, a recurrent and memory-based approach in deep reinforcement learning. en, we compare the RDPG method in dierent scenarios with so actor-critic (SAC), deep deterministic policy gradient (DDPG), distributed, and greedy algorithms. e simulation results show that the SAC method is better than the DDPG, distributed, and greedy methods, respectively. Moreover, the RDPG method out performs the SAC approach on average by 70%. Index Terms— End-to-end network slicing, Resource allocation, So ware-dened networking (SDN), Network function virtualization (NFV), Demand uncertainty, Channel state information (CSI) uncertainty, Recurrent deterministic policy gradient (RDPG).
Towards Assessing and Characterizing the Semantic Robustness of Face Recognition

Deep Neural Networks (DNNs) lack robustness against imperceptible perturbations to their input. Face Recognition Models (FRMs) based on DNNs inherit this vulnerability. We propose a methodology for assessing and characterizing the robustness of FRMs against semantic perturbations to their input. Our methodology causes FRMs to malfunction by designing adversarial attacks that search for identity-preserving modifications to faces. In particular, given a face, our attacks find identity-preserving variants of the face such that an FRM fails to recognize the images belonging to the same identity. We model these identity-preserving semantic modifications via direction- and magnitude-constrained perturbations in the latent space of StyleGAN. We further propose to characterize the semantic robustness of an FRM by statistically describing the perturbations that induce the FRM to malfunction. Finally, we combine our methodology with a certification technique, thus providing (i) theoretical guarantees on the performance of an FRM, and (ii) a formal description of how an FRM may model the notion of face identity.
P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints

We develop a class of mixed-integer formulations for disjunctive constraints intermediate to the big-M and convex hull formulations in terms of relaxation strength. The main idea is to capture the best of both the big-M and convex hull formulations: a computationally light formulation with a tight relaxation. The"$P$-split"formulations are based on a lifted transformation that splits convex additively separable constraints into $P$ partitions and forms the convex hull of the linearized and partitioned disjunction. We analyze the continuous relaxation of the $P$-split formulations and show that, under certain assumptions, the formulations form a hierarchy starting from a big-M equivalent and converging to the convex hull. The goal of the $P$-split formulations is to form a strong approximation of the convex hull through a computationally simpler formulation. We computationally compare the $P$-split formulations against big-M and convex hull formulations on 320 test instances. The test problems include K-means clustering, P_ball problems, and optimization over trained ReLU neural networks. The computational results show promising potential of the $P$-split formulations. For many of the test problems, $P$-split formulations are solved with a similar number of explored nodes as the convex hull formulation, while reducing the solution time by an order of magnitude and outperforming big-M both in time and number of explored nodes.
A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective

In this survey paper, we overview major deep learning methods used in Natural Language Processing (NLP) and source code over the last 35 years. Next, we present a survey of the applications of Artificial Intelligence (AI) for source code, also known as Code Intelligence (CI) and Programming Language Processing (PLP). We survey over 287 publications and present a software-engineering centered taxonomy for CI placing each of the works into one category describing how it best assists the software development cycle. Then, we overview the field of conversational assistants and their applications in software engineering and education. Lastly, we highlight research opportunities at the intersection of AI for code and conversational assistants and provide future directions for researching conversational assistants with CI capabilities.
The Transfer Performance of Economic Models

Economists often estimate models using data from a particular domain, e.g. estimating risk preferences in a particular subject pool or for a specific class of lotteries. Whether a model's predictions extrapolate well across domains depends on whether the estimated model has captured generalizable structure. We provide a tractable formulation for this"out-of-domain"prediction problem and define the transfer error of a model based on how well it performs on data from a new domain. We derive finite-sample forecast intervals that are guaranteed to cover realized transfer errors with a user-selected probability when domains are iid, and use these intervals to compare the transferability of economic models and black box algorithms for predicting certainty equivalents. We find that in this application, the black box algorithms we consider outperform standard economic models when estimated and tested on data from the same domain, but the economic models generalize across domains better than the black-box algorithms do.
Small examples of discriminantal arrangements associated to non-very generic arrangements

Discriminantal arrangements introduced by Manin and Schectmanin in 1989 are induced arrangements in the space of all translations of an original arrangement A. The combinatorics of the discriminantal arrangement depends on the original arrangement A therefore in 1997 Bayer and Brandt defined two classes of original arrangements. One consists of the arrangements such that the intersection lattice of the induced discriminantal arrangement has maximum cardinality. Those arrangements are called very generic. The others are called non-very generic. In this paper, we will provide both a characterization and a classification of few non-very generic arrangements in low dimensional space in terms of permutation groups.
On the almost sure scattering for the energy-critical cubic wave equation with supercritical data

<p style='text-indent:20px;'>In this article we study the defocusing energy-critical nonlinear wave equation on <inline-formula><tex-math id="M1">\begin{document}$ {\mathbb{R}}^4 $\end{document}</tex-math></inline-formula> with scaling supercritical data. We prove almost sure scattering for randomized initial data in <inline-formula><tex-math id="M2">\begin{document}$ H^s( {\mathbb{R}}^4) \times H^{s-1}( {\mathbb{R}}^4) $\end{document}</tex-math></inline-formula> with <inline-formula><tex-math id="M3">\begin{document}$ \frac{5}{6} < s < 1 $\end{document}</tex-math></inline-formula>. The proof relies on new probabilistic estimates for the linear flow of the wave equation with randomized data, where the randomization is based on a unit-scale decomposition in frequency space, a decomposition in the angular variable, and a unit-scale decomposition of physical space. In particular, we show that the solution to the linear wave equation with randomized data almost surely belongs to <inline-formula><tex-math id="M4">\begin{document}$ L^1_t L^\infty_x $\end{document}</tex-math></inline-formula>. </p>
Syntomic complexes and p-adic étale Tate twists

Abstract The primary goal of this paper is to identify syntomic complexes with the p-adic étale Tate twists of Geisser–Sato–Schneider on regular p-torsion-free schemes. Our methods apply naturally to a broader class of schemes that we call ‘F-smooth’. The F-smoothness of regular schemes leads to new results on the absolute prismatic cohomology of regular schemes.
A candidate short-period sub-Earth orbiting Proxima Centauri

Context. Proxima Centauri is the closest star to the Sun. This small, low-mass, mid M dwarf is known to host an Earth-mass exoplanet with an orbital period of 11.2 days within the habitable zone, as well as a long-period planet candidate with an orbital period of close to 5 yr. Aims. We report on the analysis of a large set of observations taken with the ESPRESSO spectrograph at the VLT aimed at a thorough evaluation of the presence of a third low-mass planetary companion, which started emerging during a previous campaign. Methods. Radial velocities (RVs) were calculated using both a cross-correlation function (CCF) and a template matching approach. The RV analysis includes a component to model Proxima’s activity using a Gaussian process (GP). We use the CCF’s full width at half maximum to help constrain the GP, and we study other simultaneous observables as activity indicators in order to assess the nature of any potential RV signals. Results. We detect a signal at 5 . 12 ± 0 . 04 days with a semi-amplitude of 39 ± 7 cms − 1 . The analysis of subsets of the ESPRESSO data, the activity indicators, and chromatic RVs suggest that this signal is not caused by stellar variability but instead by a planetary companion with a minimum mass of 0 . 26 ± 0 . 05 M ⊕ (about twice the mass of Mars) orbiting at 0.029 au from the star. The orbital eccentricity is well constrained and compatible with a circular orbit.
A Bayesian Population Model for the Observed Dust Attenuation in Galaxies

Dust plays a pivotal role in determining the observed spectral energy distribution (SED) of galaxies. Yet our understanding of dust attenuation is limited and our observations suffer from the dust–metallicity–age degeneracy in SED fitting (single galaxies), large individual variances (ensemble measurements), and the difficulty in properly dealing with uncertainties (statistical considerations). In this study, we create a population Bayesian model to rigorously account for correlated variables and non-Gaussian error distributions and demonstrate the improvement over a simple Bayesian model. We employ a flexible 5D linear interpolation model for the parameters that control dust attenuation curves as a function of stellar mass, star formation rate (SFR), metallicity, redshift, and inclination. Our setup allows us to determine the complex relationships between dust attenuation and these galaxy properties simultaneously. Using Prospector fits of nearly 30,000 3D–Hubble Space Telescope galaxies, we find that the attenuation slope (n) flattens with increasing optical depth (τ), though less so than in previous studies. τ increases strongly with SFR, though when log(SFR)≲0 , τ remains roughly constant over a wide range of stellar masses. Edge-on galaxies tend to have larger τ than face-on galaxies, but only for log(M*)≳10 , reflecting the lack of triaxiality for low-mass galaxies. Redshift evolution of dust attenuation is strongest for low-mass, low-SFR galaxies, with higher optical depths but flatter curves at high redshift. Finally, n has a complex relationship with stellar mass, highlighting the intricacies of the star–dust geometry. We have publicly released software for users to access our population model.
On an Asymptotic Criterion for Blockchain Design: The Asynchronous Composition Model

Inspired by blockchains, we introduce a dynamically growing model of rooted Directed Acyclic Graphs (DAGs) referred to as the asynchronous composition model, subject to i.i. d. random delays (ξt)t with finite mean. The new vertex at time t is connected to vertices chosen from the graph G(t−ξt)+ according to a construction function f and the graph is updated by taking union with the graph Gt−1. This process corresponds to adding new blocks in a blockchain, where the delays arise due to network communication. The main question of interest is the end structure of the asynchronous limit of the graph sequence as time increases to infinity. We consider the following construction functions of interest, a) Nakamoto construction fNak, in which a vertex is uniformly selected from those furthest from the root, resulting in a tree, and b) mixture of construction functions (fk)16k6∞, where in fk a random set of k leaves (all if there are less than k in total) is chosen without replacement. The main idea behind the analysis is decoupling the time-delay process from the DAG process and constructing an appropriate regenerative structure in the time-delay process giving rise to Markovian behavior for a functional of the DAG process. We establish that the asynchronous limits for fNak, (fk)k>2, and any non-trivial mixture f are one-ended, while the asynchronous limit for f1 has infinitely many ends, almost surely. We also study fundamental growth properties of the longest path for the sequence of graphs for fNak. In addition, we prove a phase transition on the (time and sample-path dependent) probability of choosing f1 such that the asynchronous limit either has one or infinitely many ends. Finally, we show that the construction f∞ is an appropriate limit of the (fk)k. MSC2020 subject classifications: Primary 60G50, 60J20, 05C80; Secondary 60F05.
Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals

Many real-world optimisation problems are defined over both categorical and continuous variables, yet efficient optimisation methods such asBayesian Optimisation (BO) are not designed tohandle such mixed-variable search spaces. Recent approaches to this problem cast the selection of the categorical variables as a bandit problem, operating independently alongside a BO component which optimises the continuous variables. In this paper, we adopt a holistic view and aim to consolidate optimisation of the categorical and continuous sub-spaces under a single acquisition metric. We derive candidates from the ExpectedImprovement criterion, which we call value proposals, and use these proposals to make selections on both the categorical and continuous components of the input. We show that this unified approach significantly outperforms existing mixed-variable optimisation approaches across several mixed-variable black-box optimisation tasks.
A P ] 1 0 Fe b 20 22 POROUS MEDIUM EQUATION AS LIMIT OF NONLOCAL INTERACTION

Abstract. This paper studies the convergence of solutions of a nonlocal interaction equation to the solution of the quadratic porous medium equation in the limit of a localising interaction kernel. The analysis is carried out at the level of the (nonlocal) partial differential equations and we use the gradient flow structure of the equations to derive bounds on energy, second order moments, and logarithmic entropy. The dissipation of the latter yields sufficient regularity to obtain compactness results and pass to the limit in the localised convolutions. The strategy we propose relies on a discretisation scheme which could be slightly modified in order to extend our result to PDEs with no gradient flow structure. Our analysis allows to treat the case of limiting weak solutions of the non-viscous porous medium equation at relevant low regularity, assuming the initial value to have finite energy and entropy. However, the latter excludes particle solutions of the nonlocal interaction equation.
Role of Oxygen States in the Low Valence Nickelate La$_4$Ni$_3$O$_8$

Y. Shen, ∗ J. Sears, G. Fabbris, J. Li, J. Pelliciari, I. Jarrige, Xi He, 4 I. Božović, 4 M. Mitrano, Junjie Zhang, 7 J. F. Mitchell, A. S. Botana, V. Bisogni, M. R. Norman, S. Johnston, 10 and M. P. M. Dean † Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, New York 11973, USA Advanced Photon Source, Argonne National Laboratory, Lemont, Illinois 60439, USA National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, New York 11973, USA Department of Chemistry, Yale University, New Haven, Connecticut 06520, USA Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA Institute of Crystal Materials, Shandong University, Jinan, Shandong 250100, China Department of Physics, Arizona State University, Tempe, Arizona 85287, USA Department of Physics and Astronomy, The University of Tennessee, Knoxville, Tennessee 37966, USA Institute of Advanced Materials and Manufacturing, The University of Tennessee, Knoxville, Tennessee 37996, USA (Dated: February 11, 2022)
Random Horizon Principal-Agent Problems

We consider a general formulation of the random horizon Principal-Agent problem with a continuous payment and a lump-sum payment at termination. In the European version of the problem, the random horizon is chosen solely by the principal with no other possible action from the agent than exerting effort on the dynamics of the output process. We also consider the American version of the contract, which covers the seminal Sannikov's model, where the agent can also quit by optimally choosing the termination time of the contract. Our main result reduces such non-zero-sum stochastic differential games to appropriate stochastic control problems which may be solved by standard methods of stochastic control theory. This reduction is obtained by following Sannikov's approach, further developed by Cvitanic, Possamai, and Touzi. We first introduce an appropriate class of contracts for which the agent's optimal effort is immediately characterized by the standard verification argument in stochastic control theory. We then show that this class of contracts is dense in an appropriate sense so that the optimization over this restricted family of contracts represents no loss of generality. The result is obtained by using the recent well-posedness result of random horizon second-order backward SDE.
Understanding Riemannian Acceleration via a Proximal Extragradient Framework

We contribute to advancing the understanding of Riemannian accelerated gradient methods. In particular, we revisit Accelerated Hybrid Proximal Extragradient(A-HPE), a powerful framework for obtaining Euclidean accelerated methods \citep{monteiro2013accelerated}. Building on A-HPE, we then propose and analyze Riemannian A-HPE. The core of our analysis consists of two key components: (i) a set of new insights into Euclidean A-HPE itself; and (ii) a careful control of metric distortion caused by Riemannian geometry. We illustrate our framework by obtaining a few existing and new Riemannian accelerated gradient methods as special cases, while characterizing their acceleration as corollaries of our main results.
Environmental Physical Activity Cues and Children’s Active vs. Sedentary Recreation

Physical activity (PA) benefits health, and intensive environmental modifications can increase children’s PA. Research has not yet addressed if subtle environmental cues, such as posters depicting PA, increase child PA. In the current study, it was hypothesized that children exposed to active posters (vs. nature posters) would spend a larger proportion of free play time engaging with active toys (relative to sedentary toys). Participants were randomly assigned to one of two conditions in which posters on a laboratory wall depicted 1. People being active, or 2. Nature scenes. Children aged 5–10 years (N = 175) could play with up to eight toys (four active, four sedentary) while parents completed study-related surveys. The proportion of playtime that was active was compared between groups. Poster type did not have a significant effect on proportion of active playtime. Previous environmental interventions that increase children’s PA have done so through enhancing access to active opportunities, rather than via signage. It is possible that poster interventions such as this may not influence children’s PA, or perhaps other types of cues would have been more effective. Future research should investigate subtle environmental cues that match both the target audience and the accessible PA options (e.g., posters depicting children playing with available active toys) and explore other low-investment environmental modifications to boost children’s PA.
Association of Physical Activity with Incidence of Dementia Is Attenuated by Air Pollution

ABSTRACT Introduction Physical activity (PA) is recognized as one of the key lifestyle behaviors that reduces risk of developing dementia late in life. However, PA also leads to increased respiration, and in areas with high levels of air pollution, PA may increase exposure to pollutants linked with higher risk of developing dementia. Here, we investigate whether air pollution attenuates the association between PA and dementia risk. Methods This prospective cohort study included 35,562 adults 60 yrs and older from the UK Biobank. Average acceleration magnitude (ACCave) from wrist-worn accelerometers was used to assess PA levels. Air pollution levels (NO, NO2, PM10, PM2.5, PM2.5–10, and PM2.5 absorbance) were estimated with land use regression methods. Incident all-cause dementia was derived from inpatient hospital records and death registry data. Results In adjusted models, ACCave was associated with reduced risk of developing dementia (HR = 0.71, 95% confidence interval [CI] = 0.60–0.83), whereas air pollution variables were not associated with dementia risk. There were significant interactions between ACCave and PM2.5 (HRinteraction = 1.33, 95% CI = 1.13–1.57) and PM2.5 absorbance (HRinteraction = 1.24, 95% CI = 1.07–1.45) on incident dementia. At the lowest tertiles of pollution, ACCave was associated with reduced risk of incident dementia (HRPM 2.5 = 0.66, 95% CI = 0.49–0.91; HRPM 2.5 absorbance = 0.60, 95% CI = 0.44–0.81). At the highest tertiles of these pollutants, there was no significant association of ACCave with incident dementia (HRPM 2.5 = 0.88, 95% CI = 0.68–1.14; HRPM 2.5 absorbance = 0.79, 95% CI = 0.60–1.04). Conclusions PA is associated with reduced risk of developing all-cause dementia. However, exposure to even moderate levels of air pollution attenuates the benefits of PA on risk of dementia.