Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive MIMO precoding and spectral shaping with low resolution DACs and active constellation extension

Nonlinear precoding and pulse shaping are jointly considered in multi-user massive multiple-input multiple-output (MIMO) systems with low-resolution D/A-converters (DACs) in terms of algorithmic approach as well as large system performance. Two design criteria are investigated: the mean square error (MSE) with active constellation extension (ACE) and the symbol error rate (SER). Both formulations are solved based on a modified version of the generalized approximate message passing (GAMP) algorithm. Furthermore, theoretical performance results are derived based on the state evolution analysis of the GAMP algorithm. The MSE based technique is extended to jointly perform over-the-air (OTA) spectral shaping and precoding for frequency-selective channels, in which the spectral performance is characterized at the transmitter and at the receiver. Simulation and analytical results demonstrate that the MSE based approach yields the same performance as the SER based formulation in terms of uncoded SER. The analytical results provide good performance predictions up to medium SNR. Substantial improvements in detection, as well as spectral performance, are obtained from the proposed combined pulse shaping and precoding approach compared to standard linear methods.
Ancient Very Metal-poor Stars Associated with the Galactic Disk in the H3 Survey

Ancient, very metal-poor (VMP) stars offer a window into the earliest epochs of galaxy formation and assembly. We combine data from the H3 Spectroscopic Survey and Gaia to measure metallicities, abundances of α elements, stellar ages, and orbital properties of a sample of 482 VMP ([Fe/H] < −2) stars in order to constrain their origins. This sample is confined to 1 ≲ ∣Z∣ ≲ 3 kpc from the Galactic plane. We find that >70% of VMP stars near the disk are on prograde orbits and this fraction increases toward lower metallicities. This result is unexpected if metal-poor stars are predominantly accreted from many small systems with no preferred orientation, as such a scenario would imply a mostly isotropic distribution. Furthermore, we find there is some evidence for higher fractions of prograde orbits among stars with lower [α/Fe]. Isochrone-based ages for main-sequence turn-off stars reveal that these VMP stars are uniformly old (≈12 Gyr) irrespective of the α abundance and metallicity, suggesting that the metal-poor population was not born from the same well-mixed gas disk. We speculate that the VMP population has a heterogeneous origin, including both in situ formation in the ancient disk and accretion from a satellite with the same direction of rotation as the ancient disk at early times. Our precisely measured ages for these VMP stars on prograde orbits show that the Galaxy has had a relatively quiescent merging history over most of cosmic time, and implies the angular momentum alignment of the Galaxy has been in place for at least 12 Gyr.
General Invertible Transformations for Flow-based Generative Modeling

In this paper, we present a new class of invertible transformations. We indicate that many well-known invertible tranformations in reversible logic and reversible neural networks could be derived from our proposition. Next, we propose two new coupling layers that are important building blocks of flow-based generative models. In the preliminary experiments on toy digit data, we present how these new coupling layers could be used in Integer Discrete Flows (IDF), and that they achieve better results than standard coupling layers used in IDF and RealNVP.
Decentralized Multi-Agent Linear Bandits with Safety Constraints

We study decentralized stochastic linear bandits, where a network of N agents acts cooperatively to efficiently solve a linear bandit-optimization problem over a d-dimensional space. For this problem, we propose DLUCB: a fully decentralized algorithm that minimizes the cumulative regret over the entire network. At each round of the algorithm each agent chooses its actions following an upper confidence bound (UCB) strategy and agents share information with their immediate neighbors through a carefully designed consensus procedure that repeats over cycles. Our analysis adjusts the duration of these communication cycles ensuring near-optimal regret performance O(d \log{NT}\sqrt{NT}) at a communication rate of O(dN^2) per round. The structure of the network affects the regret performance via a small additive term – coined the regret of delay – that depends on the spectral gap of the underlying graph. Notably, our results apply to arbitrary network topologies without a requirement for a dedicated agent acting as a server. In consideration of situations with high communication cost, we propose RC-DLUCB: a modification of DLUCB with rare communication among agents. The new algorithm trades off regret performance for a significantly reduced total communication cost of O(d^3N^5/2) over all T rounds. Finally, we show that our ideas extend naturally to the emerging, albeit more challenging, setting of safe bandits. For the recently studied problem of linear bandits with unknown linear safety constraints, we propose the first safe decentralized algorithm. Our study contributes towards applying bandit techniques in safety-critical distributed systems that repeatedly deal with unknown stochastic environments. We present numerical simulations for various network topologies that corroborate our theoretical findings.
Aging-Aware Request Scheduling for Non-Volatile Main Memory

Modern computing systems are embracing non-volatile memory (NVM) to implement high-capacity and low-cost main memory. Elevated operating voltages of NVM accelerate the aging of CMOS transistors in the peripheral circuitry of each memory bank. Aggressive device scaling increases power density and temperature, which further accelerates aging, challenging the reliable operation of NVM-based main memory. We propose HEBE, an architectural technique to mitigate the circuit aging-related problems of NVM-based main memory. HEBE is built on three contributions. First, we propose a new analytical model that can dynamically track the aging in the peripheral circuitry of each memory bank based on the bank’s utilization. Second, we develop an intelligent memory request scheduler that exploits this aging model at run time to de-stress the peripheral circuitry of a memory bank only when its aging exceeds a critical threshold. Third, we introduce an isolation transistor to decouple parts of a peripheral circuit operating at different voltages, allowing the decoupled logic blocks to undergo long-latency de-stress operations independently and off the critical path of memory read and write accesses, improving performance. We evaluate HEBE with workloads from the SPEC CPU2017 Benchmark suite. Our results show that HEBE significantly improves both performance and lifetime of NVM-based main memory.
The FEDHC Bayesian network learning algorithm

The paper proposes a new hybrid Bayesian network learning algorithm, termed Forward Early Dropping Hill Climbing (FEDHC), devised to work with either continuous or categorical variables. Further, the paper manifests that the only implementation of MMHC in the statistical software R is prohibitively expensive, and a new implementation is offered. Further, specifically for the case of continuous data, a robust to outliers version of FEDHC, which can be adopted by other BN learning algorithms, is proposed. The FEDHC is tested via Monte Carlo simulations that distinctly show that it is computationally efficient, and that it produces Bayesian networks of similar to, or of higher accuracy than MMHC and PCHC. Finally, an application of FEDHC, PCHC and MMHC algorithms to real data, from the field of economics, is demonstrated using the statistical software R.
Lightweight Encryption for the Low Powered IoT Devices

The internet of things refers to the network of devices connected to the internet and can communicate with each other. The term things is to refer non-conventional devices that are usually not connected to the internet. The network of such devices or things is growing at an enormous rate. The security and privacy of the data flowing through these things is a major concern. The devices are low powered and the conventional encryption algorithms are not suitable to be employed on these devices. In this correspondence a survey of the contemporary lightweight encryption algorithms suitable for use in the IoT environment has been presented.
A large scale randomized controlled trial on herding in peer-review discussions

Peer review is the backbone of academia and humans constitute a cornerstone of this process, being responsible for reviewing submissions and making the final acceptance/rejection decisions. Given that human decision-making is known to be susceptible to various cognitive biases, it is important to understand which (if any) biases are present in the peer-review process, and design the pipeline such that the impact of these biases is minimized. In this work, we focus on the dynamics of discussions between reviewers and investigate the presence of herding behaviour therein. Specifically, we aim to understand whether reviewers and discussion chairs get disproportionately influenced by the first argument presented in the discussion when (in case of reviewers) they form an independent opinion about the paper before discussing it with others. In conjunction with the review process of a large, top tier machine learning conference, we design and execute a randomized controlled trial that involves 1,544 papers and 2,797 reviewers with the goal of testing for the conditional causal effect of the discussion initiator’s opinion on the outcome of a paper. Our experiment reveals no evidence of herding in peer-review discussions. This observation is in contrast with past work that has documented an undue influence of the first piece of information on the final decision (e.g., anchoring effect) and analyzed herding behaviour in other applications (e.g., financial markets). Regarding policy implications, the absence of the herding effect suggests that the current status quo of the absence of a unified policy towards discussion initiation does not result in an increased arbitrariness of the resulting decisions.
Prospects for Galactic and stellar astrophysics with asteroseismology of giant stars in the TESS continuous viewing zones and beyond

The NASA Transiting Exoplanet Survey Satellite (NASA-TESS) mission presents a treasure trove for understanding the stars it observes and the Milky Way, in which they reside. We present a first look at the prospects for Galactic and stellar astrophysics by performing initial asteroseismic analyses of bright (G < 11) red giant stars in the TESS southern continuous viewing zone (SCVZ). Using three independent pipelines, we detect νmax and Δν in 41 per cent of the 15 405 star parent sample (6388 stars), with consistency at a level of $\sim \! 2{{\ \rm per\ cent}}$ in νmax and $\sim \! 5{{\ \rm per\ cent}}$ in Δν. Based on this, we predict that seismology will be attainable for ∼3 × 105 giants across the whole sky and at least 104 giants with ≥1 yr of observations in the TESS-CVZs, subject to improvements in analysis and data reduction techniques. The best quality TESS-CVZ data, for 5574 stars where pipelines returned consistent results, provide high-quality power spectra across a number of stellar evolutionary states. This makes possible studies of, for example, the asymptotic giant branch bump. Furthermore, we demonstrate that mixed ℓ = 1 modes and rotational splitting are cleanly observed in the 1-yr data set. By combining TESS-CVZ data with TESS-HERMES, SkyMapper, APOGEE, and Gaia, we demonstrate its strong potential for Galactic archaeology studies, providing good age precision and accuracy that reproduces well the age of high [α/Fe] stars and relationships between mass and kinematics from previous studies based on e.g. Kepler. Better quality astrometry and simpler target selection than the Kepler sample makes this data ideal for studies of the local star formation history and evolution of the Galactic disc. These results provide a strong case for detailed spectroscopic follow-up in the CVZs to complement that which has been (or will be) collected by current surveys.
Introducing Inter-Relatedness between Wikipedia Articles in Explicit Semantic Analysis

Explicit Semantic Analysis (ESA) is a technique used to represent a piece of text as a vector in the space of concepts, such as Articles found in Wikipedia. We propose a methodology to incorporate knowledge of Inter-relatedness between Wikipedia Articles to the vectors obtained from ESA using a technique called Retrofitting to improve the performance of subsequent tasks that use ESA to form vector embeddings. Especially we use an undirected Graph to represent this knowledge with nodes as Articles and edges as inter relations between two Articles. Here, we also emphasize how the ESA step could be seen as a predominantly bottom-up approach using a corpus to come up with vector representations and the incorporation of top-down knowledge which is the relations between Articles to further improve it. We test our hypothesis on several smaller subsets of the Wikipedia corpus and show that our proposed methodology leads to decent improvements in performance measures including Spearman's Rank correlation coefficient in most cases.
Unifying inflation with early and late dark energy epochs in axion 
F(R)
 gravity

We provide a theoretical model of $F(R)$ gravity in which it is possible to describe in a unified way inflation, an early and a late dark energy era, in the presence of a light axion particle which plays the role of the dark matter component of the Universe. Particularly, the early time phenomenology is dominated by an ${R}^{2}$ term, while the presence of the other terms $f(R)$ ensure the occurrence of the early and late-time dark energy eras. The inflationary phenomenology is compatible with the Planck 2018 data for inflation, while the late-time dark energy era is compatible with the Planck 2018 constraints on the cosmological parameters. Also, the model exhibits an early dark energy era, at $z\ensuremath{\sim}2.5$ approximately, followed by a deceleration era, which starts at approximately $z\ensuremath{\sim}1.5$, which in turn is followed by a late-time dark energy era for redshifts $z\ensuremath{\sim}0.5$, which lasts for approximately 5 billion years up to present time. A notable feature of our model is that the dark energy era is free from dark energy oscillations, at least in the redshift interval $z=[0,10]$. In addition, we also discuss several features related to observational data at $z\ensuremath{\sim}2.34$, at which redshift intricate observational data exist in the literature. Moreover, the numerical code for the dark energy phenomenology, written in python3, is presented in the end of the article. Finally, the model has another interesting characteristic, a sudden jump of the value of the Hubble rate in the redshift interval $z\ensuremath{\sim}[2,2.6]$ where its value suddenly increases and then decreases until $z\ensuremath{\sim}0$.
End-to-End UAV Simulation for Visual SLAM and Navigation

Visual Simultaneous Localization and Mapping (v-SLAM) and navigation of multirotor Unmanned Aerial Vehicles (UAV) in an unknown environment have grown in popularity for both research and education. However, due to the complex hardware setup, safety precautions, and battery constraints, extensive physical testing can be expensive and time-consuming. As an alternative solution, simulation tools lower the barrier to carry out the algorithm testing and validation before field trials. In this letter, we customize the ROS-Gazebo-PX4 simulator in deep and provide an end-to-end simulation solution for the UAV v-SLAM and navigation study. A set of localization, mapping, and path planning kits were also integrated into the simulation platform. In our simulation, various aspects, including complex environments and onboard sensors, can simultaneously interact with our navigation framework to achieve specific surveillance missions. In this end-to-end simulation, we achieved click and fly level autonomy UAV navigation. The source code is open to the research community.
Complex powers of the wave operator and the spectral action on Lorentzian scattering spaces

We consider the class of non-trapping Lorentzian scattering spaces, on which the wave operator $\square_g$ is known to be essentially self-adjoint [108]. We define complex powers $(\square_g-i\varepsilon)^{-\alpha}$ by functional calculus, and show that the trace density exists as a meromorphic function of $\alpha$. We relate its poles to geometric quantities, in particular to the scalar curvature, proving therefore a Lorentzian analogue of the Kastler--Kalau--Walze theorem. We interpret this result in terms of a spectral action principle, which serves as a simple Lorentzian model for the bosonic part of the Chamseddine--Connes action. Our proof combines microlocal resolvent estimates, including radial propagation estimates, with uniform estimates for the Hadamard parametrix. The arguments operate in Lorentzian signature directly and do not rely on a transition from the Euclidean setting.
Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?

Longitudinal biomedical data are often characterized by a sparse time grid and individual‐specific development patterns. Specifically, in epidemiological cohort studies and clinical registries we are facing the question of what can be learned from the data in an early phase of the study, when only a baseline characterization and one follow‐up measurement are available. Inspired by recent advances that allow to combine deep learning with dynamic modeling, we investigate whether such approaches can be useful for uncovering complex structure, in particular for an extreme small data setting with only two observations time points for each individual. Irregular spacing in time could then be used to gain more information on individual dynamics by leveraging similarity of individuals. We provide a brief overview of how variational autoencoders (VAEs), as a deep learning approach, can be linked to ordinary differential equations (ODEs) for dynamic modeling, and then specifically investigate the feasibility of such an approach that infers individual‐specific latent trajectories by including regularity assumptions and individuals' similarity. We also provide a description of this deep learning approach as a filtering task to give a statistical perspective. Using simulated data, we show to what extent the approach can recover individual trajectories from ODE systems with two and four unknown parameters and infer groups of individuals with similar trajectories, and where it breaks down. The results show that such dynamic deep learning approaches can be useful even in extreme small data settings, but need to be carefully adapted.
A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior.

We present a review and taxonomy of 200 models from the literature on driver behavior modeling. We begin by introducing a mathematical framework for describing the dynamics of interactive multi-agent traffic. Based on the partially observable stochastic game, this framework provides a basis for discussing different driver modeling techniques. Our taxonomy is constructed around the core modeling tasks of state estimation, intention estimation, trait estimation, and motion prediction, and also discusses the auxiliary tasks of risk estimation, anomaly detection, behavior imitation and microscopic traffic simulation. Existing driver models are categorized based on the specific tasks they address and key attributes of their approach.
Studies of heavy-ion collisions using PYTHIA Angantyr and UrQMD.

In this paper, we show predictions from a new QGP-free, no-equilibration, improved baseline model for heavy-ion collisions. It is comprised of the PYTHIA Angantyr event generator coupled to UrQMD, as a hadronic cascade simulator, and compared to ALICE and CMS data from Pb-Pb collisions at $\sqrt{s_{\rm{NN}}}$ = 2.76 TeV. This coupling is made possible due to a new implementation of the hadron vertex model in PYTHIA Angantyr. Hadronic rescattering in UrQMD is shown to lead to a significant suppression of mid- to high-$p_\perp$ particle yields that is qualitatively consistent with measurements of nuclear modification factors. We further study the effect of hadronic rescatterings on high-$p_\perp$ particles by using two-particle correlations and show that some suppression of away-side jets occurs in the hadronic phase even without any partonic energy loss. Finally, the decorrelation of dijet structures at high momenta also leads to a reduction of the elliptic flow coefficient $v_{2}\{2\}$. These findings suggest that significant jet-quenching-like effects may still originate in the hadronic, as opposed to the partonic phase and prove that the usual Pb-Pb baseline, composed of a superposition of incoherent pp collisions, ignores coherent phenomena that are not strictly related to the QGP but may still be highly relevant.
Lagrangian–Eulerian multidensity topology optimization with the material point method

In this paper, a hybrid Lagrangian–Eulerian topology optimization (LETO) method is proposed to solve the elastic force equilibrium with the Material Point Method (MPM). LETO transfers density information from freely movable Lagrangian carrier particles to a fixed set of Eulerian quadrature points. This transfer is based on a smooth radial kernel involved in the compliance objective to avoid the artificial checkerboard pattern. The quadrature points act as MPM particles embedded in a lower‐resolution grid and enable a subcell multidensity resolution of intricate structures with a reduced computational cost. A quadrature‐level connectivity graph‐based method is adopted to avoid the artificial checkerboard issues commonly existing in multiresolution topology optimization methods. Numerical experiments are provided to demonstrate the efficacy of the proposed approach.
Study of Carbon Dioxide Adsorption on Mesoporous Aminopropylsilane-Functionalized Silica and Titania Combining Microcalorimetry and in Situ Infrared Spectroscopy

Two calcined mesoporous supports, silica and titania, were functionalized with aminopropylsilane (APS). The samples were characterized using ATR (attenuated total reflectance), nitrogen sorption at 77 K, and thermogravimetric analysis. The functionalized silica and titania are mesoporous and were grafted with 1.4 and 1.6 molecules of APS per nm2, respectively. Infrared measurements propose that the properties of the amine sites are affected by the chemical properties of the support. For example, the NH2 bending vibration δ(NH) was shifted to lower wavenumbers from 1597 to 1575 cm−1 from the silica to the titania grafted sample, respectively. This could be explained by different interactions with the surface hydroxyl groups of silica and titania. The grafted samples were investigated for carbon dioxide adsorption by combining microcalorimetry and in situ FTIR spectroscopy. Their CO2 adsorption properties are presented in comparison to the nongrafted support materials. Microcalorimetric measurements show im.