We theoretically predict a new working principle for optical amplification, based on Weyl semimetals: when a Weyl semimetal is suitably irradiated at two frequencies, electrons close to the Weyl points convert energy between the frequencies through the mechanism of topological frequency conversion from [Martin et al, PRX 7 041008 (2017)]. Each electron converts energy at a quantized rate given by an integer multiple of Planck's constant multiplied by the product of the two frequencies. In simulations, we show that optimal, but feasible band structures can support topological frequency conversion in the "THz gap" at intensities down to $ 2{\rm W}/{\rm mm^2}$; the gain from the effect can exceed the dissipative loss when the frequencies are larger than the relaxation time of the system. Topological frequency conversion provides a new paradigm for optical amplification, and further extends Weyl semimetals' promise for technological applications. less

Anomalies are occurrences in a dataset that are in some way unusual and do not fit the general patterns. The concept of the anomaly is typically ill-defined and perceived as vague and domain-dependent. Moreover, despite some 250 years of publications on the topic, no comprehensive and concrete overviews of the different types of anomalies have hitherto been published. By means of an extensive literature review this study therefore offers the first theoretically principled and domain-independent typology of data anomalies and presents a full overview of anomaly types and subtypes. To concretely define the concept of the anomaly and its different manifestations, the typology employs five dimensions: data type, cardinality of relationship, anomaly level, data structure, and data distribution. These fundamental and data-centric dimensions naturally yield 3 broad groups, 9 basic types, and 63 subtypes of anomalies. The typology facilitates the evaluation of the functional capabilities of anomaly detection algorithms, contributes to explainable data science, and provides insights into relevant topics such as local versus global anomalies. less

Quantum computers offer the potential to efficiently simulate the dynamics of quantum systems, a task whose difficulty scales exponentially with system size on classical devices. To assess the potential for near-term quantum computers to simulate many-body systems we develop a model-independent formalism to straightforwardly compute bounds on the number of Trotter steps needed to accurately simulate the system's time evolution based on the first-order commutator scaling. We apply this formalism to two closely related many-body models prominent in condensed matter physics, the Hubbard and t-J models. We find that, while a naive comparison of the Trotter depth first seems to favor the Hubbard model, careful consideration of the model parameters and the allowable error for accurate simulation leads to a substantial advantage in favor of the t-J model. These results and formalism set the stage for significant improvements in quantum simulation costs. less

The ANITA collaboration have reported observation of two anomalous events that appear to be $\varepsilon_{\rm cr} \approx 0.6$ EeV cosmic ray showers emerging from the Earth with exit angles of $27^\circ$ and $35^\circ$, respectively. While EeV-scale upgoing showers have been anticipated as a result of astrophysical tau neutrinos converting to tau leptons during Earth passage, the observed exit angles are much steeper than expected in Standard Model (SM) scenarios. Indeed, under conservative extrapolations of the SM interactions, there is no particle that can propagate through the Earth with probability $p > 10^{-6}$ at these energies and exit angles. We explore here whether "beyond the Standard Model" (BSM) particles are required to explain the ANITA events, if correctly interpreted, and conclude that they are. Seeking confirmation or refutation of the physical phenomenon of sub-EeV Earth-emergent cosmic rays in data from other facilities, we find support for the reality of the ANITA events, and three candidate analog events, among the Extremely High Energy Northern Track neutrinos of the IceCube Neutrino Observatory. Properties of the implied BSM particle are anticipated, at least in part, by those predicted for the "stau" slepton ($\tilde{\tau}_R$) in some supersymmetric models of the fundamental interactions, wherein the stau manifests as the next-to-lowest mass supersymmetric partner particle. less

The absence of electrical resistance exhibited by superconducting materials would have enormous potential for applications if it existed at ambient temperature and pressure conditions. Despite decades of intense research efforts, such a state has yet to be realized. At ambient pressures, cuprates are the material class exhibiting superconductivity to the highest critical superconducting transition temperatures (Tc), up to about 133 K. Over the past decade, high-pressure ‘chemical precompression of hydrogen-dominant alloys has led the search for high-temperature superconductivity, with demonstrated Tc approaching the freezing point of water in binary hydrides at megabar pressures. Ternary hydrogen-rich compounds, such as carbonaceous sulfur hydride, offer an even larger chemical space to potentially improve the properties of superconducting hydrides. Here we report evidence of superconductivity on a nitrogen-doped lutetium hydride with a maximum Tc of 294 K at 10 kbar, that is, superconductivity at room temperature and near-ambient pressures. The compound was synthesized under high-pressure high-temperature conditions and then—after full recoverability—its material and superconducting properties were examined along compression pathways. These include temperature-dependent resistance with and without an applied magnetic field, the magnetization (M) versus magnetic field (H) curve, a.c. and d.c. magnetic susceptibility, as well as heat-capacity measurements. X-ray diffraction (XRD), energy-dispersive X-ray (EDX) and theoretical simulations provide some insight into the stoichiometry of the synthesized material. Nevertheless, further experiments and simulations are needed to determine the exact stoichiometry of hydrogen and nitrogen, and their respective atomistic positions, in a greater effort to further understand the superconducting state of the material. less

Population-scale and rapid testing for SARS-CoV-2 continues to be a priority for several parts of the world. We revisit the in vitro technology platforms for COVID-19 testing and diagnostics—molecular tests and rapid antigen tests, serology or antibody tests, and tests for the management of COVID-19 patients. Within each category of tests, we review the commercialized testing platforms, their analyzing systems, specimen collection protocols, testing methodologies, supply chain logistics, and related attributes. Our discussion is essentially focused on test products that have been granted emergency use authorization by the FDA to detect and diagnose COVID-19 infections. Different strategies for scaled-up and faster screening are covered here, such as pooled testing, screening programs, and surveillance testing. The near-term challenges lie in detecting subtle infectivity profiles, mapping the transmission dynamics of new variants, lowering the cost for testing, training a large healthcare workforce, and providing test kits for the masses. Through this review, we try to understand the feasibility of universal access to COVID-19 testing and diagnostics in the near future while being cognizant of the implicit tradeoffs during the development and distribution cycles of new testing platforms. less

Soybeans are an important crop for global food security. Every year, soybean yields are reduced by numerous soybean diseases, particularly the soybean cyst nematode (SCN). It is difficult to visually identify the presence of SCN in the field, let alone its population densities or numbers, as there are no obvious aboveground disease symptoms. The only definitive way to assess SCN population densities is to directly extract the SCN cysts from soil and then extract the eggs from cysts and count them. Extraction is typically conducted in commercial soil analysis laboratories and university plant diagnostic clinics and involves repeated steps of sieving, washing, collecting, grinding, and cleaning. Here we present a robotic instrument to reproduce and automate the functions of the conventional methods to extract nematode cysts from soil and subsequently extract eggs from the recovered nematode cysts. We incorporated mechanisms to actuate the stage system, manipulate positions of individual sieves using the gripper, recover cysts and cyst-sized objects from soil suspended in water, and grind the cysts to release their eggs. All system functions are controlled and operated by a touchscreen interface software. The performance of the robotic instrument is evaluated using soil samples infested with SCN from two farms at different locations and results were comparable to the conventional technique. Our new technology brings the benefits of automation to SCN soil diagnostics, a step towards long-term integrated pest management of this serious soybean pest. less

Real-time monitoring of the gastrointestinal tract in a safe and comfortable manner is valuable for the diagnosis and therapy of many diseases. Within this realm, our review captures the trends in ingestible capsule systems with a focus on hardware and software technologies used for capsule endoscopy and remote patient monitoring. We introduce the structure and functions of the gastrointestinal tract, and the FDA guidelines for ingestible wireless telemetric medical devices. We survey the advanced features incorporated in ingestible capsule systems, such as microrobotics, closed-loop feedback, physiological sensing, nerve stimulation, sampling and delivery, panoramic imaging with adaptive frame rates, and rapid reading software. Examples of experimental and commercialized capsule systems are presented with descriptions of their sensors, devices, and circuits for gastrointestinal health monitoring. We also show the recent research in biocompatible materials and batteries, edible electronics, and alternative energy sources for ingestible capsule systems. The results from clinical studies are discussed for the assessment of key performance indicators related to the safety and effectiveness of ingestible capsule procedures. Lastly, the present challenges and outlook are summarized with respect to the risks to health, clinical testing and approval process, and technology adoption. less

Contract automata have been introduced for specifying applications through behavioural contracts and for synthesising their orchestrations as finite state automata. This paper addresses the realisation of applications from contract automata specifications. We present {\tt CARE}, a new runtime environment to coordinate services implementing contracts that guarantees the adherence of the implementation to its contract. We discuss how {\tt CARE} can be adopted to realise contract-based applications, its formal guarantees, and we identify the responsibilities of the involved business actors. Experiments show the benefits of adopting {\tt CARE} with respect to manual implementations. less

We present evidence for a suppressed growth rate of large-scale structure during the dark-energy dominated era. Modeling the growth rate of perturbations with the ``growth index'' γ, we find that current cosmological data strongly prefer a higher growth index than the value γ=0.55 predicted by general relativity in a flat ΛCDM cosmology. Both the cosmic microwave background data from Planck and the large-scale structure data from weak lensing, galaxy clustering, and cosmic velocities separately favor growth suppression. When combined, they yield γ=0.633+0.025−0.024, excluding γ=0.55 at a statistical significance of 3.7σ. The combination of fσ8 and Planck measurements prefers an even higher growth index of γ=0.639+0.024−0.025, corresponding to a 4.2σ-tension with the concordance model. In Planck data, the suppressed growth rate offsets the preference for nonzero curvature and fits the data equally well as the latter model. A higher γ leads to a higher matter fluctuation amplitude S8 inferred from galaxy clustering and weak lensing measurements, and a lower S8 from Planck data, effectively resolving the S8 tension. less

Central Pattern Generators (CPGs) form the neural basis of the observed rhythmic behaviors for locomotion in legged animals. The CPG dynamics organized into networks allow the emergence of complex locomotor behaviors. In this work, we take this inspiration for developing walking behaviors in multi-legged robots. We present novel DeepCPG policies that embed CPGs as a layer in a larger neural network and facilitate end-to-end learning of locomotion behaviors in deep reinforcement learning (DRL) setup. We demonstrate the effectiveness of this approach on physics engine-based insectoid robots. We show that, compared to traditional approaches, DeepCPG policies allow sample-efficient end-to-end learning of effective locomotion strategies even in the case of high-dimensional sensor spaces (vision). We scale the DeepCPG policies using a modular robot configuration and multi-agent DRL. Our results suggest that gradual complexification with embedded priors of these policies in a modular fashion could achieve non-trivial sensor and motor integration on a robot platform. These results also indicate the efficacy of bootstrapping more complex intelligent systems from simpler ones based on biological principles. Finally, we present the experimental results for a proof-of-concept insectoid robot system for which DeepCPG learned policies initially using the simulation engine and these were afterwards transferred to real-world robots without any additional fine-tuning. less

The recent discovery of superconductivity in magic-angle twisted bilayer graphene has sparked a renewed interest in the strongly-correlated physics of sp2 carbons, in stark contrast to preliminary investigations which were dominated by the one-body physics of the massless Dirac fermions. We thus provide a self-contained, theoretical perspective of the journey of graphene from its single-particle physics-dominated regime to the strongly-correlated physics of the flat bands. Beginning from the origin of the Dirac points in condensed matter systems, we discuss the effect of the superlattice on the Fermi velocity and Van Hove singularities in graphene and how it leads naturally to investigations of the moiré pattern in van der Waals heterostructures exemplified by graphene-hexagonal boron-nitride and twisted bilayer graphene. Subsequently, we illuminate the origin of flat bands in twisted bilayer graphene at the magic angles by elaborating on a broad range of prominent theoretical works in a pedagogical way while linking them to available experimental support, where appropriate. We conclude by providing a list of topics in the study of the electronic properties of twisted bilayer graphene not covered by this review but may readily be approached with the help of this primer. less

Smart traffic engineering and intelligent transportation services are in increasing demand from governmental authorities to optimize traffic performance and thus reduce energy costs, increase the drivers’ safety and comfort, ensure traffic laws enforcement, and detect traffic violations. In this paper, we address this challenge, and we leverage the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traffic video analytics, we propose an automated object detection and tracking pipeline from video processing to advanced traffic understanding using high-resolution UAV images. TAU combines six main contributions. First, it proposes a pre-processing algorithm to adapt the high- resolution UAV image as input to the object detector without lowering the resolution. This ensures an excellent detection accuracy from high-quality features, particularly the small size of detected objects from UAV images. Second, it introduces an algorithm for recalibrating the vehicle coordinates to ensure that vehicles are uniquely identified and tracked across the multiple crops of the same frame. Third, it presents a speed calculation algorithm based on accumulating information from successive frames. Fourth, TAU counts the number of vehicles per traffic zone based on the Ray Tracing algorithm. Fifth, TAU has a fully independent algorithm for crossroad arbitration based on the data gathered from the different zones surrounding it. Sixth, TAU introduces a set of algorithms for extracting twenty-four types of insights from the raw data collected. These insights facilitate the traffic understanding using curves, histograms, heatmaps, and animations. The present work presents a valuable added value for academic researchers and transportation engineers to automate the traffic video analytics process and extract useful insights to optimize traffic performance. TAU is a ready- to-use framework for any Transportation Engineer to better understand and manage daily road traffic (video demonstrations are provided here: https://youtu.be/wXJV0H7LviU and here: https://youtu.be/kGv0gmtVEbI). The source code is available at: https://github.com/bilel-bj/TAU. less

Context. The number of known strong gravitational lenses is expected to grow substantially in the next few years. The combination of large samples of lenses has the potential to provide strong constraints on the inner structure of galaxies. Aims: We investigate the extent to which we can calibrate stellar mass measurements and constrain the average dark matter density profile of galaxies by combining strong lensing data from thousands of lenses. Methods: We generated mock samples of axisymmetric lenses. We assume that, for each lens, we have measurements of two image positions of a strongly lensed background source, as well as magnification information from full surface brightness modelling, and a stellar-population-synthesis-based estimate of the lens stellar mass. We then fitted models describing the distribution of the stellar population synthesis mismatch parameter αsps (the ratio between the true stellar mass and the stellar-population-synthesis-based estimate) and the dark matter density profile of the population of lenses to an ensemble of 1000 mock lenses. Results: We obtain the average αsps, projected dark matter mass, and dark matter density slope with greater precision and accuracy compared with current constraints. A flexible model and knowledge of the lens detection efficiency as a function of image configuration are required in order to avoid a biased inference. Conclusions: Statistical strong lensing inferences from upcoming surveys provide a way to calibrate stellar mass measurements and to constrain the inner dark matter density profile of massive galaxies. less

Context. Time-delay lensing is a powerful tool for measuring the Hubble constant H0. However, in order to obtain an accurate estimate of H0 from a sample of time-delay lenses, very good knowledge of the mass structure of the lens galaxies is needed. Strong lensing data on their own are not sufficient to break the degeneracy between H0 and the lens model parameters on a single object basis. Aims: The goal of this study is to determine whether it is possible to break the H0-lens structure degeneracy with the statistical combination of a large sample of time-delay lenses, relying purely on strong lensing data with no stellar kinematics information. Methods: I simulated a set of 100 lenses with doubly imaged quasars and related time-delay measurements. I fitted these data with a Bayesian hierarchical method and a flexible model for the lens population, emulating the lens modelling step. Results: The sample of 100 lenses on its own provides a measurement of H0 with 3% precision, but with a −4% bias. However, the addition of prior information on the lens structural parameters from a large sample of lenses with no time delays, such as that considered in Paper I, allows for a 1% level inference. Moreover, the 100 lenses allow for a 0.03 dex calibration of galaxy stellar masses, regardless of the level of prior knowledge of the Hubble constant. Conclusions: Breaking the H0-lens model degeneracy with lensing data alone is possible, but 1% measurements of H0 require either many more than 100 time-delay lenses or knowledge of the structural parameter distribution of the lens population from a separate sample of lenses. less