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Resilience and power consumption are two important performance metrics for many modern communication systems, and it is therefore important to define, analyze, and optimize them. In this work, we consider a wireless communication system with secret-key generation, in which the secret-key bits are added to and used from a pool of available key bits. We propose novel physical layer resilience metrics for the survivability of such systems. In addition, we propose multiple power allocation schemes and analyze their trade-off between resilience and power consumption. In particular, we investigate and compare constant power allocation, an adaptive analytical algorithm, and a reinforcement learning-based solution. It is shown how the transmit power can be minimized such that a specified resilience is guaranteed. These results can be used directly by designers of such systems to optimize the system parameters for the desired performance in terms of reliability, security, and resilience., Comment: 13 pages, 11 figures

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challenging channel characteristics. We show that mmWave CF mMIMO optimization is largely an assignment problem between access points (APs) and users due to the high path loss of mmWave channels, the limited output power of the amplifier, and the almost orthogonal channels between users given a large number of AP antennas. The combinatorial nature of the assignment problem, the requirement for scalability, and the distributed implementation of CF mMIMO make this problem difficult. In this work, we propose an unsupervised machine learning (ML) enabled solution. In particular, a graph neural network (GNN) customized for scalability and distributed implementation is introduced. Moreover, the customized GNN architecture is hierarchically permutation-equivariant (HPE), i.e., if the APs or users of an AP are permuted, the output assignment is automatically permuted in the same way. To address the combinatorial problem, we relax it to a continuous problem, and introduce an information entropy-inspired penalty term. The training objective is then formulated using the augmented Lagrangian method (ALM). The test results show that the realized sum-rate outperforms that of the generalized serial dictatorship (GSD) algorithm and is very close to the upper bound in a small network scenario, while the upper bound is impossible to obtain in a large network scenario.

Earning a college degree has long been critical to unlocking many high-paying jobs -- and, as a result, to economic mobility and security. Increasingly, however, the traditional "norm" of a college student--one who enrolls straight out of high school, receives some support from their parents, lives on campus, and does not have substantial work or family responsibilities outside of school--does not fit the reality of much of the student population. Today, students who are themselves parents make up a significant percentage of those enrolled in college. While promoting student-parent success has mainly been left to institutions of higher education, several states have considered or adopted a range of policy initiatives to support student parents in postsecondary education. This paper provides state policy stakeholders, including governors, state agencies, and advocates, with a snapshot of the multitude of issues faced by student parents and with options to utilize state policy to address these issues.

The effect of annealing on the superconducting properties of niobium single crystals cut from the same master boule was studied by local and global magnetic measurements, as well as scanning tunneling microscopy (STM). The formation of large hydride precipitates was observed in unannealed samples. The variation in structural and magnetic properties was studied after annealing under high vacuum at 800 C, 1400 C, and near the melting point of niobium (2477 C) for a few seconds. The initial samples had a high hydrogen content. Polarized optics and magneto-optical studies show that the formation of large niobium hydride precipitates is suppressed already by 800 C annealing. However, the overall superconducting properties in the annealed samples did not improve after annealing, and in fact, worsened. The superconducting transition temperature decreased, the upper critical field increased, and the pinning strength increased. Parallel studies were conducted using STM, where the sample was annealed initially at 400 C, measured, annealed again at 1700 C, and measured again. These studies revealed a ``dirty'' superconducting gap with a significant spatial variation of tunneling conductance after annealing at 400 C. The clean gap was recovered after annealing at 1700 C. It is likely that these results are due to oxygen redistribution near the surface, which is always covered by oxide layers in as-grown crystals. Overall, the results indicate that vacuum annealing at least up to 1400 C, while expected to remove a large amount of hydrogen, introduces additional nanosized defects, perhaps hydride precipitates, that act as efficient pair-breaking and pinning centers.

Multiple access techniques are cornerstones of wireless communications. Their performance depends on the channel properties, which can be improved by reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize MA precoding at the base station (BS) and RIS configuration. We tackle difficulties of mutual coupling between RIS elements, scalability to more than 1000 RIS elements, and channel estimation. We first derive an RIS-assisted channel model considering mutual coupling, then propose an unsupervised machine learning (ML) approach to optimize the RIS. In particular, we design a dedicated neural network (NN) architecture RISnet with good scalability and desired symmetry. Moreover, we combine ML-enabled RIS configuration and analytical precoding at BS since there exist analytical precoding schemes. Furthermore, we propose another variant of RISnet, which requires the channel state information (CSI) of a small portion of RIS elements (in this work, 16 out of 1296 elements) if the channel comprises a few specular propagation paths. More generally, this work is an early contribution to combine ML technique and domain knowledge in communication for NN architecture design. Compared to generic ML, the problem-specific ML can achieve higher performance, lower complexity and symmetry., Comment: 13 pages, 16 figures

In modern cell-less wireless networks, mobility management is undergoing a significant transformation, transitioning from single-link handover management to a more adaptable multi-connectivity cluster reconfiguration approach, including often conflicting objectives like energy-efficient power allocation and satisfying varying reliability requirements. In this work, we address the challenge of dynamic clustering and power allocation for unmanned aerial vehicle (UAV) communication in wireless interference networks. Our objective encompasses meeting varying reliability demands, minimizing power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we introduce a novel approach based on reinforcement learning using a masked soft actor-critic algorithm, specifically tailored for dynamic clustering and power allocation., Comment: 6 pages, 4 figures

A consistent and explicit spectral comparison between heterodyne (HD) and direct detection (DD) derived from first principles including the atmospheric transmission and low beam-filling factors could not be found yet in literature but is needed for decisions in technology planification for future infrared interferometry facilities which are e.g. focused on planet formation. This task requires both, high sensitivity continuum and Doppler-resolved emission and absorption line detection in the mid-IR range (N- and Q-bands) at lower source temperatures (300-1000 K). The signal-to-noise ratios (SNRs) are derived for both schemes within the same semi-classical theory, which consists of classical mode theory for coupling to an antenna and occupation of these modes by quanta of three radiation fields, the thermal signal, the thermal background, and for HD also the coherent local oscillator (LO). The effects of very small beam filling factors (interferometry) and atmospheric absorption/emission could be consistently incorporated this way, as well as quantum-noise propagation which allows in HD the consideration of balanced mixers with cross-correlation (CC). Especially, the transition from pre- to post-detection SNRs was considered meticulously. We do this all because the usually cited SNR-expressions were derived for a too simple and unrealistic case, and moreover contain some wrong assumptions. We introduce a novel HD scheme for astronomical interferometry gaining an order of magnitude in sensitivity against conventional HD and calculate that it should trespass the sensitivity of DD interferometry in the N- and Q-bands for a spectral resolution of R=10000, and should do also for R=300 with doable technical improvements. This result encourages to develop broad-band heterodyne technologies for future mid-infrared interferometry facilities and for new instruments at existing facilities.

Modern communication systems need to fulfill multiple and often conflicting objectives at the same time. In particular, new applications require high reliability while operating at low transmit powers. Moreover, reliability constraints may vary over time depending on the current state of the system. One solution to address this problem is to use joint transmissions from a number of base stations (BSs) to meet the reliability requirements. However, this approach is inefficient when considering the overall total transmit power. In this work, we propose a reinforcement learning-based power allocation scheme for an unmanned aerial vehicle (UAV) communication system with varying communication reliability requirements. In particular, the proposed scheme aims to minimize the total transmit power of all BSs while achieving an outage probability that is less than a tolerated threshold. This threshold varies over time, e.g., when the UAV enters a critical zone with high-reliability requirements. Our results show that the proposed learning scheme uses dynamic power allocation to meet varying reliability requirements, thus effectively conserving energy., Comment: 5 pages, 3 figures

The reconfigurable intelligent surface (RIS) is a promising technology that enables wireless communication systems to achieve improved performance by intelligently manipulating wireless channels. In this paper, we consider the sum-rate maximization problem in a downlink multi-user multi-input-single-output (MISO) channel via space-division multiple access (SDMA). Two major challenges of this problem are the high dimensionality due to the large number of RIS elements and the difficulty to obtain the full channel state information (CSI), which is assumed known in many algorithms proposed in the literature. Instead, we propose a hybrid machine learning approach using the weighted minimum mean squared error (WMMSE) precoder at the base station (BS) and a dedicated neural network (NN) architecture, RISnet, for RIS configuration. The RISnet has a good scalability to optimize 1296 RIS elements and requires partial CSI of only 16 RIS elements as input. We show it achieves a high performance with low requirement for channel estimation for geometric channel models obtained with ray-tracing simulation. The unsupervised learning lets the RISnet find an optimized RIS configuration by itself. Numerical results show that a trained model configures the RIS with low computational effort, considerably outperforms the baselines, and can work with discrete phase shifts.

We consider a wireless communication system with a passive eavesdropper, in which a transmitter and legitimate receiver generate and use key bits to secure the transmission of their data. These bits are added to and used from a pool of available key bits. In this work, we analyze the reliability of the system in terms of the probability that the budget of available key bits will be exhausted. In addition, we investigate the latency before a transmission can take place. Since security, reliability, and latency are three important metrics for modern communication systems, it is of great interest to jointly analyze them in relation to the system parameters. In particular, we show under what conditions the system may remain in an active state indefinitely, i.e., never run out of available secret-key bits. The results presented in this work will allow system designers to adjust the system parameters in such a way that the requirements of the application in terms of both reliability and latency are met., Comment: 12 pages, 6 figures

The interaction of rarefied gases with functionalized surfaces is of great importance in technical applications such as gas separation membranes and catalysis. To investigate the influence of functionalization and rarefaction on gas flow rate in a defined geometry, pressure-driven gas flow experiments with helium and carbon dioxide through plain and alkyl-functionalized microchannels are performed. The experiments cover Knudsen numbers from 0.01 to 200 and therefore the slip flow regime up to free molecular flow. To minimize the experimental uncertainty which is prevalent in micro flow experiments, a methodology is developed to make optimal use of the measurement data. The results are compared to an analytical model predicting rarefied gas flow in straight channels and to numerical simulations of the S-model and BGK equations. The experimental data shows no significant difference between plain and functionalized channels. This stands in contrast to previous measurements in smaller geometries and demonstrates that the surface-to-volume ratio seems to be too small for the functionalization to have an influence and highlights the importance of geometric scale for surface effects. These results also shed light on the molecular reflection characteristics described by the TMAC.

The eighteenth data release of the Sloan Digital Sky Surveys (SDSS) is the first one for SDSS-V, the fifth generation of the survey. SDSS-V comprises three primary scientific programs, or "Mappers": Milky Way Mapper (MWM), Black Hole Mapper (BHM), and Local Volume Mapper (LVM). This data release contains extensive targeting information for the two multi-object spectroscopy programs (MWM and BHM), including input catalogs and selection functions for their numerous scientific objectives. We describe the production of the targeting databases and their calibration- and scientifically-focused components. DR18 also includes ~25,000 new SDSS spectra and supplemental information for X-ray sources identified by eROSITA in its eFEDS field. We present updates to some of the SDSS software pipelines and preview changes anticipated for DR19. We also describe three value-added catalogs (VACs) based on SDSS-IV data that have been published since DR17, and one VAC based on the SDSS-V data in the eFEDS field., Comment: Accepted to ApJS

This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. This optimization problem is non-convex and its global optimum is difficult to find. In the literature, either simple but suboptimal approaches or optimal methods with high complexity and poor scalability are proposed. In contrast, we propose a machine learning framework to approach the global optimum. While the neural network (NN) training takes moderate time, application with the trained model requires very low computational complexity. In particular, we introduce a novel objective function based on stochastic actions to solve the non-convex optimization problem. Besides, we design a dedicated NN architecture for the multi-cell network optimization problems that is permutation-equivariant. It classifies channels according to their roles in the EE computation. In this way, we encode our domain knowledge into the NN design and shed light into the black box of machine learning. Training and testing results show that the proposed method without supervision and with reasonable computational effort achieves an EE close to the global optimum found by the branch-and-bound algorithm. Hence, the proposed approach balances between computational complexity and performance.

QMKPy provides a Python framework for modeling and solving the quadratic multiple knapsack problem (QMKP). It is primarily aimed at researchers who develop new solution algorithms for the QMKP. QMKPy therefore mostly functions as a testbed to quickly implement novel algorithms and compare their results with existing ones. However, the package also already includes implementations of established algorithms for those who only need to solve a QMKP as part of their research., Comment: 3 pages

We consider a multi-user two-ray ground reflection scenario with unknown distances between transmitter and receivers. By using two frequencies per user in parallel, we can mitigate possible destructive interference and ensure ultra-reliability with only very limited knowledge at the transmitter. In this work, we consider the problem of assigning two frequencies to each receiver in a multi-user communication system such that the average minimum receive power is maximized. In order to solve this problem, we introduce a generalization of the quadratic multiple knapsack problem to include heterogeneous profits and develop an algorithm to solve it. Compared to random frequency assignment, we report a gain of around 6 dB in numerical simulations., Comment: 8 pages, 5 figures. arXiv admin note: substantial text overlap with arXiv:2206.13459

Lucy Calkins' curriculum "Units of Study for Writing" is a process approach to writing pedagogy used in thousands of elementary and middle schools in the United States and internationally (Teachers College Reading Writing Project, 2020). Process approaches have been highly influential on writing pedagogy for the past 30 years (Brisk, 2015; Hyland, 2003) and continue to be popular in the United States today, particularly in the context of culturally sustaining pedagogy (Alim & Paris, 2017). However, as with all movements in education, we need to step back and critically examine the role of process approaches in promoting equity in the present context. Do this approach and curriculum still meet the needs of today's learners, in particular multilingual learners? Most importantly, do they promote and maintain equitable learning outcomes for all students? This paper examines how Calkins' curriculum conceptualizes the teaching of writing in the disciplines; how one teacher made sense of the curriculum; and what is missing from the curriculum that may limit its curricular and pedagogical appropriateness to promote equity. We focus on one of Calkins' units, "Bringing History to Life" (Calkins & Cockerille, 2013) and one elementary school teacher. We found that in this unit, writing was conceptualized as a cognitive process of thinking, imagining, planning, and noticing. The main writing activities were drafting and free writing, without explicit attention to language. The privileged pedagogy promoted by this unit of study was discovery-led, with achievement based on students' prior knowledge of language and exposure to the genres. As a result, access to the knowledge necessary for achievement was not equally distributed. In this way, we found the "Bringing History to Life" script to be potentially complicit in both creating and maintaining opportunity gaps in writing development, as the students who made progress did so because they did not need help from the teacher, and those who needed explicit language teaching to make progress, did not get enough support. We conclude with recommendations for teaching writing with a more visible pedagogy that makes the rules for being a successful writer explicit, visible, and accessible to all.