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This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an average accelerating gradient of 20 MV/m. After reviewing the attributes and performance of the previous 6 GeV CEBAF accelerator, we discuss the upgraded CEBAF accelerator system in detail with particular attention paid to the new beam acceleration systems. In addition to doubling the acceleration in each linac, the upgrade included improving the beam recirculation magnets, adding more helium cooling capacity to allow the newly installed modules to run cold, adding a new experimental hall, and improving numerous other accelerator components. We review several of the techniques deployed to operate and analyze the accelerator performance, and document system operating experience and performance. In the final portion of the document, we present much of the current planning regarding projects to improve accelerator performance and enhance operating margins, and our plans for ensuring CEBAF operates reliably into the future. For the benefit of potential users of CEBAF, the performance and quality measures for beam delivered to each of the experimental halls is summarized in the appendix., Comment: 66 pages, 73 figures, 21 tables

Interband Cascade Lasers (ICL) have matured into a versatile technological platform in the mid-infrared spectral domain. To broaden their applicability even further, ongoing research pursues the emission of ultrashort pulses. The most promising approach is passive mode-locking with a fast saturable absorber. However, despite vast attempts no passive mode-locked ICL has been demonstrated up to date. In this study we perform pump-probe measurements on the ICL gain and show that its dynamics are mainly ($\sim$70%) governed by a fast recovery time of 2 ps. Our findings explain why passive mode-locking has not succeeded so far, shedding a new light on ICL dynamics as we propose strategies to overcome the current limitations.

Performance prediction has been a key part of the neural architecture search (NAS) process, allowing to speed up NAS algorithms by avoiding resource-consuming network training. Although many performance predictors correlate well with ground truth performance, they require training data in the form of trained networks. Recently, zero-cost proxies have been proposed as an efficient method to estimate network performance without any training. However, they are still poorly understood, exhibit biases with network properties, and their performance is limited. Inspired by the drawbacks of zero-cost proxies, we propose neural graph features (GRAF), simple to compute properties of architectural graphs. GRAF offers fast and interpretable performance prediction while outperforming zero-cost proxies and other common encodings. In combination with other zero-cost proxies, GRAF outperforms most existing performance predictors at a fraction of the cost., Comment: ICML 2024. Code at https://github.com/gabikadlecova/zc_combine , blog post: https://gabikadlecova.github.io/blog/2024/graf/

The goal of product mapping is to decide, whether two listings from two different e-shops describe the same products. Existing datasets of matching and non-matching pairs of products, however, often suffer from incomplete product information or contain only very distant non-matching products. Therefore, while predictive models trained on these datasets achieve good results on them, in practice, they are unusable as they cannot distinguish very similar but non-matching pairs of products. This paper introduces two new datasets for product mapping: ProMapCz consisting of 1,495 Czech product pairs and ProMapEn consisting of 1,555 English product pairs of matching and non-matching products manually scraped from two pairs of e-shops. The datasets contain both images and textual descriptions of the products, including their specifications, making them one of the most complete datasets for product mapping. Additionally, the non-matching products were selected in two phases, creating two types of non-matches -- close non-matches and medium non-matches. Even the medium non-matches are pairs of products that are much more similar than non-matches in other datasets -- for example, they still need to have the same brand and similar name and price. After simple data preprocessing, several machine learning algorithms were trained on these and two the other datasets to demonstrate the complexity and completeness of ProMap datasets. ProMap datasets are presented as a golden standard for further research of product mapping filling the gaps in existing ones.

Recent years witnessed rapid progress of chip-scale integrated optical frequency comb sources. Among them, two classes are particularly significant -- semiconductor Fabry-Per\'{o}t lasers and passive ring Kerr microresonators. Here, we merge the two technologies in a ring semiconductor laser and demonstrate a new paradigm for free-running soliton formation, called Nozaki-Bekki soliton. These dissipative waveforms emerge in a family of traveling localized dark pulses, known within the famed complex Ginzburg-Landau equation. We show that Nozaki-Bekki solitons are structurally-stable in a ring laser and form spontaneously with tuning of the laser bias -- eliminating the need for an external optical pump. By combining conclusive experimental findings and a complementary elaborate theoretical model, we reveal the salient characteristics of these solitons and provide a guideline for their generation. Beyond the fundamental soliton circulating inside the ring laser, we demonstrate multisoliton states as well, verifying their localized nature and offering an insight into formation of soliton crystals. Our results consolidate a monolithic electrically-driven platform for direct soliton generation and open a door for a new research field at the junction of laser multimode dynamics and Kerr parametric processes.

The wide span and high density of lines in its rovibrational spectrum render hydrogen cyanide a useful spectroscopic media for referencing absolute frequencies of lasers in optical communication and dimensional metrology. We determined, for the first time to the best of our knowledge, the molecular transitions' center frequencies of the H13C14N isotope in the range from 1526 nm to 1566 nm with 1,3e-10 fractional uncertainty. We investigated the molecular transitions with a highly coherent and widely tunable scanning laser that was precisely referenced to a Hydrogen maser through an optical frequency comb. We demonstrated an approach to stabilize the operational conditions needed to maintain the constantly low pressure of the hydrogen cyanide to carry out the saturated spectroscopy with the third-harmonic synchronous demodulation. We demonstrated approximately a fourtyfold improvement in the line centers' resolution compared to the previous result., Comment: 9 pp, 4 figures. Originally submited to Optics Letters. Version 2 - please note: the measured center frequencies of the spectroscopic features were refined compared to Version 1

The problem of coordinating the charging of electric vehicles gains more importance as the number of such vehicles grows. In this paper, we develop a method for the training of controllers for the coordination of EV charging. In contrast to most existing works on this topic, we require the controllers to preserve the privacy of the users, therefore we do not allow any communication from the controller to any third party. In order to train the controllers, we use the idea of imitation learning -- we first find an optimum solution for a relaxed version of the problem using quadratic optimization and then train the controllers to imitate this solution. We also investigate the effects of regularization of the optimum solution on the performance of the controllers. The method is evaluated on realistic data and shows improved performance and training speed compared to similar controllers trained using evolutionary algorithms., Comment: Submitted to ICTAI 2021

The linewidth enhancement factor (LEF) has recently moved into the spotlight of research on frequency comb generation in semiconductor lasers. Here we present a novel modulation experiment, which enables the direct measurement of the spectrally resolved LEF in a laser frequency comb. By utilizing a phase-sensitive technique, we are able to extract the LEF for each comb mode. We first investigate and verify this universally applicable technique using Maxwell-Bloch simulations and then present the experimental demonstration on a quantum cascade laser frequency comb.

Several concepts have been brought forward to determine where terrestrial planets are likely to remain habitable in multi-stellar environments. Isophote-based habitable zones, for instance, rely on insolation geometry to predict habitability, whereas radiative habitable zones take the orbital motion of a potentially habitable planet into account. Dynamically informed habitable zones include gravitational perturbations on planetary orbits, and full scale, self consistent simulations promise detailed insights into the evolution of select terrestrial worlds. All of the above approaches agree that stellar multiplicity does not preclude habitability. Predictions on where to look for habitable worlds in such environments can differ between concepts. The aim of this article is to provide an overview of current approaches and present simple analytic estimates for the various types of habitable zones in binary star systems., Comment: accepted; MDPI galaxies

After the detection of extrasolar planets in binary and multiple star systems questions arose about their dynamics and habitability. In this study we investigate a five-dimensional parameter space for wide binary stars with a massive planet beyond the habitable zone (HZ). Our aim is to reveal those orbital and physical parameter combinations that expose bodies in the primary star's HZ to secular perturbations. Building upon an established semi-analytical model, we combine two separate analytical models into a new one to treat the restricted four-body problem. We apply this new Combined Analytical Model (CAM) to different synthetic systems and systematically map the occurrence of secular resonances (SR). These maps are then visualized as two-dimensional sections of the parameter space. The CAM model has a median error below 3% relative to numerical reference simulations. We also derive a simplified CAM that performs well for hierarchical systems with moderate to large separations between the bodies. Our results show that SR appear in the HZ even for large secondary star distances (up to >1000 au) if either (i) the planet's distance is larger than Jupiter's, or (ii) its mass is about Saturn's or lower. Changes in the secondary star's eccentricity by stellar flybys or galactic tides can push a formerly dynamically quiet HZ to a high-eccentricity state. Based on these results we provide the easy-to-use online tool "SHaDoS" that evaluates the CAM for a given user input and traces the locations of SR in the HZ in two-dimensional parameter space plots., Comment: 54 pages, 13 figures, 6 tables; accepted for publication in Astronomical Journal

The SARS-CoV-2 (Covid-19) pandemic has caused significant strain on public health institutions around the world. Contact tracing is an essential tool to change the course of the Covid-19 pandemic. Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile apps has the potential to shift the paradigm. Some countries have deployed centralized tracking systems, but more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or for-profit corporations. Machine learning methods can circumvent some of the limitations of standard digital tracing by incorporating many clues and their uncertainty into a more graded and precise estimation of infection risk. The estimated risk can provide early risk awareness, personalized recommendations and relevant information to the user. Finally, non-identifying risk data can inform epidemiological models trained jointly with the machine learning predictor. These models can provide statistical evidence for the importance of factors involved in disease transmission. They can also be used to monitor, evaluate and optimize health policy and (de)confinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of `COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada., Comment: 64 pages, 1 figure