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Cold metals, characterized by their distinctive band structures, hold promise for innovative electronic devices such as tunnel diodes with negative differential resistance (NDR) effect and field-effect transistors (FETs) with sub-60 mV/dec subthreshold swing (SS). In this study, we employ the GW approximation and HSE06 hybrid functional to investigate the correlation effects on the electronic band structure of two-dimensional (2D) cold metallic materials, specifically focusing on MX_2 and MA_2Z_4 (M=Nb, Ta; X=S, Se, Te; A=Si, Ge; Z= N, P) compounds in 1H structure. These materials exhibit a unique band structure with an isolated metallic band around the Fermi energy, denoted as W_m, as well as two energy gaps: the internal gap E^I_g below the Fermi level and the external gap E^E_g above the Fermi level. These three electronic structure parameters play a decisive role in determining the current-voltage (I-V) characteristics of tunnel diodes, the nature of the NDR effect, and the transfer characteristics and SS value of FETs. Our calculations reveal that both GW and HSE06 methods yield consistent electronic structure properties for all studied compounds. We observed a consistent increase in both internal and external band gaps, as well as metallic bandwidths, across all pn-type cold metal systems. Notably, the internal band gap E^I_g exhibits the most substantial enhancement, highlighting the sensitivity of these materials to correlation effects. In contrast, the changes in the metallic bandwidth W_m and external band gap E^E_g are relatively modest. These findings offer valuable insights for designing and optimizing cold metal-based devices. Materials like NbSi_2N_4, NbGe_2N_4, and TaSi_2N_4 show particular promise for high-performance NDR tunnel diodes and sub-60 mV/dec SS FETs., Comment: Physical Review Materials, In Press

This study investigates the structural parameters of the thin-disk population by analyzing the spatial distribution of evolved stars in the solar neighbourhood. From the $\it Gaia$ Data Release 3 database, about 39.1 million stars within 1 kpc and with relative parallax errors $\sigma_{\varpi}/\varpi\leq 0.10$ were selected. The photometric data was corrected for extinction using a Galactic dust map. The sample was refined by considering the color-magnitude region $M_{\rm G}\times (G_{\rm BP}-G_{\rm RP})_0$ associated with evolved stars, applying a stricter parallax error limit of $\sigma_{\varpi}/\varpi\leq 0.02$, and yielding 671,600 stars. The star sample was divided into 36 regions based on their Galactic coordinates, with evolved stars in the absolute magnitude range of $-1< M_{\rm G}~{\rm (mag)}\leq 4$ further split into five one-unit magnitude intervals. This led to 180 subgroups whose space density profiles were modelled using a single-component Galaxy model. The analysis shows that the space densities are in agreement with the literature and that the scale heights vary with $200

Tracer diffusion of Na in natural alkali feldspars including sanidine, adularia and orthoclase with different Na:K ratios is measured using the radiotracer technique and applying the 22Na radioisotope. The tracer diffusion measurements along the crystallographic directions perpendicular to (001) and (010) in alularia feldspar reveled a slight (within a factor of two to three) anisotropy of Na diffusion with the faster diffusion rates along the perpendicular to (001) directions, while this difference was less prominent in orthoclase. The anisotropy of Na diffusion is specifically addressed for orthoclase and the full diffusion tensor is determined. The impact of temperature and composition on diffusion in natural alkali feldspars is discussed with respect to impurities and intrinsic defects. The state of Al/Si ordering is proposed to have a significant influence on the Na diffusion rates in alkali feldspars., Comment: conflict of interests with co-authors. All written records can be received from the corresponding author

{We present the radial metallicity gradients within the Galactic thin disc population through main-sequence stars selected on the chemical plane using GALAH DR3 accompanied with Gaia DR3 astrometric data. The [Fe/H], [$\alpha$/Fe] and [Mg/H] radial gradients are estimated for guiding radius as $-0.074\pm 0.006$, $+0.004\pm0.002$, $-0.074\pm0.006$ dex kpc$^{-1}$ and for the traceback early orbital radius as $-0.040\pm0.002$, $+0.003\pm 0.001$, $-0.039\pm 0.002$ dex kpc$^{-1}$ for 66,545 thin-disc stars, respectively. Alteration of the chemical structure within the Galactic disc caused by the radial orbital variations complicates results for the radial metallicity gradient. The effect of radial orbital variations on the metallicity gradients as a function on time indicates the following results: (i) The presence of a gradient along the disc throughout the time for which the model provides similar prediction, (ii) the radial orbital variations becomes more pronounced with the age of the stellar population and (iii) the effect of radial orbital variations on the metallicity gradients is minimal. The effect of radial orbital variations is found to be at most 6\% which does not statistically affect the radial gradient results. These findings contribute to a better understanding of the chemical evolution within the Galactic disc and provide an important basis for further research., Comment: 19 pages, including 15 figures & 1 table, accepted for publication in Astronomische Nachrichten. We would like to especially thank the editorial board for their contribution to the publication of the study

A spectre is haunting consensus protocols-the spectre of adversary majority. The literature is inconclusive, with possibilities and impossibilities running abound. Dolev and Strong in 1983 showed an early possibility for up to 99% adversaries. Yet, we have known impossibility results for adversaries above 1/2 in synchrony, and above 1/3 in partial synchrony. What gives? It is high time that we pinpoint the culprit of this confusion: the critical role of the modeling details of clients. Are the clients sleepy or always-on? Are they silent or communicating? Can validators be sleepy too? We systematize models for consensus across four dimensions (sleepy/always-on clients, silent/communicating clients, sleepy/always-on validators, and synchrony/partial-synchrony), some of which are new, and tightly characterize the achievable safety and liveness resiliences with matching possibilities and impossibilities for each of the sixteen models. To this end, we unify folklore and earlier results, and fill gaps left in the literature with new protocols and impossibility theorems.

The NOvA collaboration reports cross-section measurements for $\nu_{\mu}$ charged-current interactions with low hadronic energy (maximum kinetic energy of 250 MeV for protons and 175 MeV for pions) in the NOvA Near Detector. The results are presented as a double-differential cross section as a function of the direct observables of the final-state muon kinematics. Results are also presented as a single-differential cross section as a function of the derived square of the four-momentum transfer, $Q^{2}$, and as a function of the derived neutrino energy. The data correspond to an accumulated 8.09$\times10^{20}$ protons-on-target (POT) in the neutrino mode of the NuMI beam, with a narrow band of neutrino energies peaked at 1.8 GeV. The analysis provides a sample of neutrino-nucleus interactions with an enhanced fraction of quasi-elastic and two-particle-two-hole (2p2h) interactions. This enhancement allows quantitative comparisons with various nuclear models. We find strong disagreement between data and theory-based models in various regions of the muon kinematic phase space, especially in the forward muon direction., Comment: 20 pages, 12 figures. The second version includes an additional citation and adds four previously missing authors

Double- and single-differential cross sections for inclusive charged-current neutrino-nucleus scattering are reported for the kinematic domain 0 to 2 GeV/c in three-momentum transfer and 0 to 2 GeV in available energy, at a mean muon-neutrino energy of 1.86 GeV. The measurements are based on an estimated 995,760 muon-neutrino CC interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole reactions is identified by the cross-section excess relative to predictions for neutrino-nucleus scattering that are constrained by a data control sample. Models for 2-particle-2- hole processes are rated by chi-square comparisons of the predicted-versus-measured muon-neutrino CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based Val`encia and SuSAv2 2p2h models., Comment: 20 pages, 14 figures

Proof-of-stake (PoS) blockchains require validators to lock their tokens as collateral, slashing these tokens if they are identified as protocol violators. PoS chains have mostly been secured by their native tokens. However, using only the native token upper-bounds the value eligible for staking by the market capitalization of the native token. In contrast, the remote staking of another crypto asset from a provider chain provides an avenue to improve the consumer chain's economic security. In this paper, we present the first known remote staking protocols with guaranteed optimal economic safety: whenever there is a safety violation on the consumer chain, at least one third of the provider's stake securing the consumer chain is slashed. To achieve this goal for a broad range of provider and consumer chains, two independent contributions are made: 1) a cryptographic protocol to slash stake even without smart contracts on the provider chain; 2) a secure unbonding protocol that ensures slashing before the stake is unbonded on the provider chain if there is safety violation on the consumer chain. A major use case of this work is when the provider chain is Bitcoin, making available an asset worth more than 1.7 trillion USD to secure PoS chains. Such a Bitcoin staking protocol has been launched on the Mainnet in August 2024 and has accumulated 2.1 billion USD worth of stake thus far.

Self-driving vehicles rely on multimodal motion forecasts to effectively interact with their environment and plan safe maneuvers. We introduce SceneMotion, an attention-based model for forecasting scene-wide motion modes of multiple traffic agents. Our model transforms local agent-centric embeddings into scene-wide forecasts using a novel latent context module. This module learns a scene-wide latent space from multiple agent-centric embeddings, enabling joint forecasting and interaction modeling. The competitive performance in the Waymo Open Interaction Prediction Challenge demonstrates the effectiveness of our approach. Moreover, we cluster future waypoints in time and space to quantify the interaction between agents. We merge all modes and analyze each mode independently to determine which clusters are resolved through interaction or result in conflict. Our implementation is available at: https://github.com/kit-mrt/future-motion, Comment: ITSC'24; updated table VI

Validators in permissionless, large-scale blockchains (e.g., Ethereum) are typically payoff-maximizing, rational actors. Ethereum relies on in-protocol incentives, like rewards for validators delivering correct and timely votes, to induce honest behavior and secure the blockchain. However, external incentives, such as the block proposer's opportunity to capture maximal extractable value (MEV), may tempt validators to deviate from honest protocol participation. We show a series of commitment attacks on LMD GHOST, a core part of Ethereum's consensus mechanism. We demonstrate how a single adversarial block proposer can orchestrate long-range chain reorganizations by manipulating Ethereum's reward system for timely votes. These attacks disrupt the intended balance of power between proposers and voters: by leveraging credible threats, the adversarial proposer can coerce voters from previous slots into supporting blocks that conflict with the honest chain, enabling a chain reorganization at no cost to the adversary. In response, we introduce a novel reward mechanism that restores the voters' role as a check against proposer power. Our proposed mitigation is fairer and more decentralized -- not only in the context of these attacks -- but also practical for implementation in Ethereum.

Transformer-based models generate hidden states that are difficult to interpret. In this work, we aim to interpret these hidden states and control them at inference, with a focus on motion forecasting. We use linear probes to measure neural collapse towards interpretable motion features in hidden states. High probing accuracy implies meaningful directions and distances between hidden states of opposing features, which we use to fit interpretable control vectors for activation steering at inference. To optimize our control vectors, we use sparse autoencoders with fully-connected, convolutional, MLPMixer layers and various activation functions. Notably, we show that enforcing sparsity in hidden states leads to a more linear relationship between control vector temperatures and forecasts. Our approach enables mechanistic interpretability and zero-shot generalization to unseen dataset characteristics with negligible computational overhead. Our implementation is available at https://github.com/kit-mrt/future-motion, Comment: Add autoencoders with convolutional, MLPMixer layers, and JumpReLU activations

The demand for novel detector mediums such as Water-based Liquid Scintillator (WbLS) has increased over the last few decades due to their capability for both low energy particle interactions and higher light yield. Recently, the usage of machine learning (ML) methods in high-energy physics has also been increasing. The ML and AI methods are used in many physics projects in the field since they provide effective and sensitive results. In this study, we aimed to develop a comprehensive analysis of water Cherenkov detectors and perform physics analyses to efficiently separate Cherenkov and scintillation photons with ML algorithms using the data from the WbLS detector environment. The main goal of this study was to produce more precise solutions to physics problems, such as signal classification, by applying ML techniques to the simulation and experimental data. Here, we trained more than 20 ML models, and our results revealed that three machine learning models, XGBoost, Light GBM, and Random Forest models, and their ensemble model gave us more than 95\% accuracy for separating Cherenkov and scintillation photons with balanced and unbalanced datasets. This is a significant increase in efficiency as compared with the results of the classical method by applying simple time cuts.

Emerging wireless technologies with Gbps connectivity, such as the 5th generation (5G) and 6th generation (6G) of mobile networks, require improved and substantiating documentation for the wireless standards concerning the radio signals, systems, transmission environments used, and the radio frequency exposures created. Current challenges faced by the telecommunications sector include the lack of accurate, fast, low-cost, and traceable methods for manufacturers to demonstrate 5G and 6G product verifications matching customer specifications. This paper gives an update on the recent research and development activities from an EU Joint Research Project entitled metrology for emerging wireless standards (MEWS) in support of the above., Comment: 6 pages, 10 figures, the 45th Annual Meeting and Symposium of the Antenna Measurement Techniques Association (AMTA 2023)

This Letter reports a search for charge-parity ($CP$) symmetry violating nonstandard interactions (NSI) of neutrinos with matter using the NOvA Experiment, and examines their effects on the determination of the standard oscillation parameters. Data from $\nu_{\mu}(\bar{\nu}_{\mu})\rightarrow\nu_{\mu}(\bar{\nu}_{\mu})$ and $\nu_{\mu}(\bar{\nu}_{\mu})\rightarrow\nu_{e}(\bar{\nu}_{e})$ oscillation channels are used to measure the effect of the NSI parameters $\varepsilon_{e\mu}$ and $\varepsilon_{e\tau}$. With 90% CL the magnitudes of the NSI couplings are constrained to be $|\varepsilon_{e\mu}| \, \lesssim 0.3$ and $|\varepsilon_{e\tau}| \, \lesssim 0.4$. A degeneracy at $|\varepsilon_{e\tau}| \, \approx 1.8$ is reported, and we observe that the presence of NSI limits sensitivity to the standard $CP$ phase $\delta_{\tiny\text{CP}}$.

We present JointMotion, a self-supervised pre-training method for joint motion prediction in self-driving vehicles. Our method jointly optimizes a scene-level objective connecting motion and environments, and an instance-level objective to refine learned representations. Scene-level representations are learned via non-contrastive similarity learning of past motion sequences and environment context. At the instance level, we use masked autoencoding to refine multimodal polyline representations. We complement this with an adaptive pre-training decoder that enables JointMotion to generalize across different environment representations, fusion mechanisms, and dataset characteristics. Notably, our method reduces the joint final displacement error of Wayformer, HPTR, and Scene Transformer models by 3\%, 8\%, and 12\%, respectively; and enables transfer learning between the Waymo Open Motion and the Argoverse 2 Motion Forecasting datasets. Code: https://github.com/kit-mrt/future-motion, Comment: CoRL'24 camera-ready

Gapped metals, a recently discovered new class of materials, possess a band gap slightly above or below the Fermi level. These materials are intrinsic p- or n-type semiconductors eliminating the need for extrinsic doping. Inspired by this concept, we propose the so-called "spin-gapped metals" exhibiting intrinsic p- or n-type behavior for each spin channel independently. Their properties would be similar to the dilute magnetic semiconductors eliminating the requirement for transition metal doping. Here, we demonstrate this novel concept in semi-Heusler compounds using first principles electronic band structure calculations. We comprehensively analyze their electronic and magnetic properties, paving the way for novel technological applications of Heusler compounds.

Since the creation of Bitcoin 15 years ago, there has been an explosion in the number of permissionless blockchains. Each of these blockchains provides an open ledger that anyone can read from and write to. In this multi-chain world, an important question emerges: how can we build a more secure overlay blockchain by reading from and writing to a given set of blockchains? Drawing an analogy with switching circuits, we approach the problem by defining two basic compositional operations between blockchains, serial and triangular compositions, and use these operations as building blocks to construct general overlay blockchains. Under the partially synchronous setting, we have the following results: 1) the serial composition, between two blockchains, yields an overlay blockchain that is safe if at least one of the two underlay blockchains is safe and that is live if both of them are live; 2) the triangular composition between three blockchains, akin to parallel composition of switching circuits, yields an overlay blockchain that is safe if all underlay blockchains are safe and that is live if over half of them are live; 3) repeated composition of these two basic operations can yield all possible tradeoffs of safety and liveness for an overlay blockchain built on arbitrary number of underlay chains. The results are also extended to the synchronous setting., Comment: In Advances in Financial Technologies (AFT 2024)

We pretrain RoBERTa on a Turkish corpora using BPE tokenizer. Our model outperforms BERTurk family models on the BOUN dataset for the POS task while resulting in underperformance on the IMST dataset for the same task and achieving competitive scores on the Turkish split of the XTREME dataset for the NER task - all while being pretrained on smaller data than its competitors. We release our pretrained model and tokenizer.

In three-dimensional models obtained by photogrammetry of existing structures, all of the shapes that the eye can select cannot always find their equivalents in the geometric components of the model. However, the matching of meaningful parts and assemblages with the records acquired with rapid and detailed documentation methods will provide an advantage for the creation of information models of existing structures. While aiming to produce answers to this problem and in order to overcome the difficulties of pattern recognition in three-dimensional models, we used two-dimensional samples obtained by projection. Processing techniques such as ambient occlusion, curvature and normal maps are commonly used in modern computer graphics applications that enable the representation of three-dimensional surface properties in two-dimensional data sets. The method we propose is based on the recognition of patterns through these mappings instead of the usual light-based visualization. The first stage of the application is photogrammetric capture of a few examples of Zeugma mosaics and three-dimensional digital modeling of a set of Seljuk era brick walls based on knowledge obtained through architectural history literature. The second stage covers the creation of digital models byprocessing the surface representation obtained from this data using Alice Vision, OpenCV-Python, and Autodesk Maya to include information on aspects of the making of the walls. What is envisioned for the next stages is that the mapping data contributes and supports the knowledge for rule-based design and making processesof cultural heritage., Comment: 9 pages, in Turkish language. 6 figures. In: MSTAS 2019 - (XIII. Computational Design in Architecture National Symposium) pp. 313-326., Kocaeli, Turkey (2019)

This study aims to present a material based, second order design method that makes the rapid creation of bridging structures in order to connect two or more horizontal planes which are currently separated and three design instances created via such method. The first element of the presented method is a generative system that creates circulation surfaces through the interpolation of the curves defined on the current surfaces, and also creates the structural volumes via rheotomic (minimal) surfaces. The second constituent is the instantiation of the exposed variable, connected to a displacement algorithm inspired by allometry based on user input, contextual data, or simulation results. The method was created with applicability through additive manufacturing in consideration. The design process - similar to manufacturing - proceeds in a vertical manner, in order to reduce the generation of support geometry as much as possible. The system includes a raster data input viable for simulation results, feeding the accumulation variable in order to modify material amount or quality with the aim of improving structural performance where stress is greater. Through the application of the method, three physical models which connect different horizontal planes were obtained. The models can be evaluated with digital of physical simulations, and results can be utilized in an iterative manner, improving the results by each recursion., Comment: 8 pages, in Turkish language. 9 figures. XII. Computational Design Research in Architecture Symposium, June 21-22, 2018 Isparta, Turkey

Generative systems are becoming a crucial part of current design practice. There exist gaps however, between the digital processes, field data and designer's input. To solve this problem, multiple processes were developed in order to generate emergent and self-organizing design solutions that combine the designer's input with surface models acquired via photogrammetry and generative design tools. Different generative design methods were utilized for trials, including surface scattering based on UV coordinates, animation snapshots (similar to long exposure photography) and a particle swarm algorithm on arbitrary data, interpolated within GIS software. A large volume of adaptive forms that are complex, yet responsive to changes in parameters, user input, topography and/or various spatial data were acquired. Resulting outputs were rendered and projection mapped onto the original physical model and evaluated for further iterations., Comment: 10 pages, 10 figures. 4th International Symposium - Formal Methods in Architecture, April 3-5, 2017 Porto, Portugal

There has been a growing adoption of computer vision tools and technologies in architectural design workflows over the past decade. Notable use cases include point cloud generation, visual content analysis, and spatial awareness for robotic fabrication. Multiple image classification, object detection, and semantic pixel segmentation models have become popular for the extraction of high-level symbolic descriptions and semantic content from two-dimensional images and videos. However, a major challenge in this regard has been the extraction of high-level architectural structures (walls, floors, ceilings windows etc.) from diverse imagery where parts of these elements are occluded by furniture, people, or other non-architectural elements. This project aims to tackle this problem by proposing models that are capable of extracting architecturally meaningful semantic descriptions from two-dimensional scenes of populated interior spaces. 1000 virtual classrooms are parametrically generated, randomized along key spatial parameters such as length, width, height, and door/window positions. The positions of cameras, and non-architectural visual obstructions (furniture/objects) are also randomized. A Generative Adversarial Network (GAN) for image-to-image translation (Pix2Pix) is trained on synthetically generated rendered images of these enclosures, along with corresponding image abstractions representing high-level architectural structure. The model is then tested on unseen synthetic imagery of new enclosures, and outputs are compared to ground truth using pixel-wise comparison for evaluation. A similar model evaluation is also carried out on photographs of existing indoor enclosures, to measure its performance in real-world settings., Comment: Written for 6.869, Advances in Computer Vision at MIT, Spring 2022. 9 pages, 13 figures

Remote collaboration is a common reality of spatial design processes, but tools for computer aided design were made for single users. Via TeamCAD, we introduce a user experience where online remote collaboration experience is more like working on a table. Using speech and gesture recognition based on state of the art machine learning through webcam and microphone input, TeamCAD plugs into existing software through API's, keybindings, and mouse input. We share results from user studies conducted on graduate students from . Our user tests were run on Blender animation software, making simultaneous use of both modalities for given tasks. We mitigated challenges in terms of robustness and latency in readily available voice recognition models. Our prototype has proven to be an intuitive interface, providing a suitable denominator for collaborators with or without previous experience in three-dimensional modeling applications., Comment: Created for 6.835, Intelligent Multimodal User Interfaces at MIT, Spring 2022. Submitted to CHI 2024. 10 pages, 6 figures, 1 table

Visual design relies on seeing things in different ways, acting on them, and seeing results to act again. Parametric design tools are often not robust to design changes that result from sketching over the visualization of their output. We propose a sketch to 3d workflow as an experiment medium for evaluating neural networks and their latent spaces as a representation that is robust to overlay sketching., Comment: Written for 4.453 Creative Machine Learning at MIT, Spring 2023. 9 pages, 9 figures

Sentiment analysis methods are rapidly being adopted by the field of Urban Design and Planning, for the crowdsourced evaluation of urban environments. However, most models used within this domain are able to identify positive or negative sentiment associated with a textual appraisal as a whole, without inferring information about specific urban aspects contained within it, or the sentiment associated with them. While Aspect Based Sentiment Analysis (ABSA) is becoming increasingly popular, most existing ABSA models are trained on non-urban themes such as restaurants, electronics, consumer goods and the like. This body of research develops an ABSA model capable of extracting urban aspects contained within geo-located textual urban appraisals, along with corresponding aspect sentiment classification. We annotate a dataset of 2500 crowdsourced reviews of public parks, and train a Bidirectional Encoder Representations from Transformers (BERT) model with Local Context Focus (LCF) on this data. Our model achieves significant improvement in prediction accuracy on urban reviews, for both Aspect Term Extraction (ATE) and Aspect Sentiment Classification (ASC) tasks. For demonstrative analysis, positive and negative urban aspects across Boston are spatially visualized. We hope that this model is useful for designers and planners for fine-grained urban sentiment evaluation., Comment: Created for 6.8610, Quantitative Methods for Natural Language Processing at MIT Fall 2022. 5 pages, 4 figures

The choice of exchange functional is a critical factor in determining the energy bandgap of semiconductors. Ab initio calculations using different exchange functionals, including the conventional generalized-gradient approximation (GGA) functionals, meta-GGA functionals, and hybrid functionals, show significant differences in the calculated energy bandgap for semiconducting half-Heusler compounds. These compounds, which have 18 valence electrons per unit cell, are of great interest due to their thermoelectric properties, making them suitable for energy conversion applications. In addition, accounting for electronic correlations using the GW method also affects the calculated energy bandgaps compared to standard GGA calculations. The variations in calculated energy bandgaps are specific to each material when using different functionals. Hence, a detailed investigation of the electronic properties of each compound is necessary to determine the most appropriate functional for an accurate description of the electronic properties. Our results indicate that no general rules can be established and a comparison with experimental results is required to determine the most appropriate functional.

NOvA is a long-baseline neutrino oscillation experiment that measures oscillations in charged-current $\nu_{\mu} \rightarrow \nu_{\mu}$ (disappearance) and $\nu_{\mu} \rightarrow \nu_{e}$ (appearance) channels, and their antineutrino counterparts, using neutrinos of energies around 2 GeV over a distance of 810 km. In this work we reanalyze the dataset first examined in our previous paper [Phys. Rev. D 106, 032004 (2022)] using an alternative statistical approach based on Bayesian Markov Chain Monte Carlo. We measure oscillation parameters consistent with the previous results. We also extend our inferences to include the first NOvA measurements of the reactor mixing angle $\theta_{13}$ and the Jarlskog invariant. We use these results to quantify the strength of our inferences about CP violation, as well as to examine the effects of constraints from short-baseline measurements of $\theta_{13}$ using antineutrinos from nuclear reactors when making NOvA measurements of $\theta_{23}$. Our long-baseline measurement of $\theta_{13}$ is also shown to be consistent with the reactor measurements, supporting the general applicability and robustness of the PMNS framework for neutrino oscillations., Comment: 20 pages, 17 figures; version accepted by Phys. Rev. D. Data associated with this paper is available at https://doi.org/10.15484/2349444