cssi/SciSciGPT-SciSciNet huggingface dataset:
On Distributed Optimization in the Presence of Malicious Agents

In this paper, we consider an unconstrained distributed optimization problem over a network of agents, in which some agents are adversarial. We solve the problem via gradient-based distributed optimization algorithm and characterize the effect of the adversarial agents on the convergence of the algorithm to the optimal solution. The attack model considered is such that agents locally perturb their iterates before broadcasting it to neighbors; and we analyze the case in which the adversarial agents cooperate in perturbing their estimates and the case where each adversarial agent acts independently. Based on the attack model adopted in the paper, we show that the solution converges to the neighborhood of the optimal solution and depends on the magnitude of the attack (perturbation) term. The analyses presented establishes conditions under which the malicious agents have enough information to obstruct convergence to the optimal solution by the non-adversarial agents.
Deep Galaxy Surveys in the 9150 Å Airglow Window

We describe the current status of two complementary programs to search for objects with strong emission lines in a 300 A gap, centered at 9150 A in the strong airglow emission. Both programs are being carried out with LRIS on the Keck II telescope. The first of these uses broad-band and narrow-band filter photometry to select candidates, followed by multi-slit spectroscopy through the same narrow-band filter to limit the bandpass and allow a dense packing of slits. The second uses six parallel long slits to carry out a blind spectroscopic search through the filter isolating the 9150 A window. The total slit area covered ranges from I to 3.5 square arcmin per pointing, depending on slit width, and we can obtain 3σ detections of emission lines of < 2 x 10-18 erg cm-2 s-1 in a 12000 s observation with l″5 slits. Because, for faint objects in both programs, we are most sensitive to strong lines with large equivalent widths, most of our detections will be restricted to a few specific emission lines at certain discrete redshifts. One of the more interesting possibilities is Ly-α at z ∼ 6.5. However, even with 12000 s integrations on the Keck II telescope, our narrow-band imaging does not pick up objects with emission-line fluxes ≲ 10-17 erg cm-2 s-1. With this limit, at z 6.5, we would pick up only the most luminous of the z ∼ 5 objects discovered so far. Our blind spectroscopic search potentially has a better chance of discovering such objects, but we have not yet found any definite examples in the limited area of the sky we have covered to date. We discuss the criteria for identifying Ly-α emission in noisy spectra and emphasize how high-ionization dwarf galaxies at low redshift can be mistaken for Ly-α candidates under certain conditions.
Internet of Things (IoT) Application Model for Smart Farming

Smart Farming has brought a major transformation in the agriculture process by using the Internet of Things (IoT) devices, emerging technologies such as cloud computing, fog computing, and data analytics. It allows farmers to have real-time awareness of the farm and help them make smart and informed decisions. In this paper, we propose a distributed data flow (DDF) based model for the smart farming application that is composed of interdependent modules. We evaluate the proposed application model using two deployment strategies: cloud-based, and fog-based where the application modules are deployed on the fog and the cloud data center respectively. We compare the cloud-based and fog-based strategy in terms of end-to-end latency and network usage.
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation

The key task of physical simulation is to solve partial differential equations (PDEs) on discretized domains, which is known to be costly. In particular, high-fidelity solutions are much more expensive than low-fidelity ones. To reduce the cost, we consider novel Gaussian process (GP) models that leverage simulation examples of different fidelities to predict high-dimensional PDE solution outputs. Existing GP methods are either not scalable to high-dimensional outputs or lack effective strategies to integrate multi-fidelity examples. To address these issues, we propose Multi-Fidelity High-Order Gaussian Process (MFHoGP) that can capture complex correlations both between the outputs and between the fidelities to enhance solution estimation, and scale to large numbers of outputs. Based on a novel nonlinear coregionalization model, MFHoGP propagates bases throughout fidelities to fuse information, and places a deep matrix GP prior over the basis weights to capture the (nonlinear) relationships across the fidelities. To improve inference efficiency and quality, we use bases decomposition to largely reduce the model parameters, and layer-wise matrix Gaussian posteriors to capture the posterior dependency and to simplify the computation. Our stochastic variational learning algorithm successfully handles millions of outputs without extra sparse approximations. We show the advantages of our method in several typical applications.
A multi-vehicle covering tour problem with speed optimization

The multi-vehicle covering tour problem with time windows (MCTPTW) aims to construct a set of maximal coverage routes for a fleet of vehicles that serve (observe) a secondary set of sites given a fixed time schedule, coverage requirements, and energy restrictions. The problem is formulated as a mixed-integer second-order cone programming (MISOCP) model and is an extension of both the multi-covering tour problem and the vehicle routing problem with time windows under energy constraints. Further, we study a special case of the proposed model and develop a labeling algorithm to solve its Lagrangian relaxation problem, which exploits the combinatorial structure exhibited by an optimal solution to the Lagrangian relaxation.
ARENA: Asynchronous Reconfigurable Accelerator Ring to Enable Data-Centric Parallel Computing

The next generation HPC and data centers are likely to be reconfigurable and data-centric due to the trend of hardware specialization and the emergence of data-driven applications. In this article, we propose ARENA – an asynchronous reconfigurable accelerator ring architecture as a potential scenario on how the future HPC and data centers will be like. Despite using the coarse-grained reconfigurable arrays (CGRAs) as the substrate platform, our key contribution is not only the CGRA-cluster design itself, but also the ensemble of a new architecture and programming model that enables asynchronous tasking across a cluster of reconfigurable nodes, so as to bring specialized computation to the data rather than the reverse. We presume distributed data storage without asserting any prior knowledge on the data distribution. Hardware specialization occurs at runtime when a task finds the majority of data it requires are available at the present node. In other words, we dynamically generate specialized CGRA accelerators where the data reside. The asynchronous tasking for bringing computation to data is achieved by circulating the task token, which describes the dataflow graphs to be executed for a task, among the CGRA cluster connected by a fast ring network. Evaluations on a set of HPC and data-driven applications across different domains show that ARENA can provide better parallel scalability with reduced data movement (53.9 percent). Compared with contemporary compute-centric parallel models, ARENA can bring on average 4.37× speedup. The synthesized CGRAs and their task-dispatchers only occupy 2.93mm $^2$ 2 chip area under 45nm process technology and can run at 800MHz with on average 759.8mW power consumption. ARENA also supports the concurrent execution of multi-applications, offering ideal architectural support for future high-performance parallel computing and data analytics systems.
Stability region estimation under low voltage ride through constraints using sum of squares

The increasing penetration of inverter based renewable generation (RG) in the form of solar photo-voltaic (PV) or wind has introduced numerous operational challenges and uncertainties. According to the standards [1], [2], these generators are made to trip offline if their operating requirements are not met. In an RG-rich system, this might alter the system dynamics and/or cause shifting of the equilibrium points to the extent that a cascaded tripping scenario is manifested. The present work attempts at avoiding such scenarios by estimating the constrained stability region (CSR) inside which the system must operate using maximal level set of a Lyapunov function estimated through sum of squares (SOS) technique. A time-independent conservative approximation of the LVRT constraint is initially derived for a classical model of the power system. The proposed approach is eventually validated by evaluating the stability of a 3 machine test system with trip-able RG.
E-beam Processing of Polymer Matrix Composites for Multifunctional Radiation Shielding

Aliphatic polymers were identified as optimum radiation shielding polymeric materials for building multifunctional structural elements for in-space habitats. Conceptual damage tolerant configurations of polyolefins have been proposed, but many manufacturing issues relied on methods and materials which have sub-optimal radiation shielding characteristics (for example, epoxy matrix and adhesives). In the present approach, we shall investigate e-beam processing technologies for inclusion of high-strength aliphatic polymer reinforcement structures into a highly cross-linked polyolefin matrix. This paper reports the baseline thermo-mechanical properties of low density polyethylene and highly crystallized polyethylene.
Tribological and microstructural comparison of HIPped PM212 and PM212/Au self-lubricating composites

The feasibility of replacing the silver with the volumetric equivalent of gold in the chromium carbide-based self-lubricating composite PM212 (70 wt percent NiCo-Cr3C2, 15 percent BaF2/CaF2 eutectic) was studied. The new composite, PM212/Au has the following composition: 62 wt percent NiCo-Cr3C2, 25 percent Au, 13 percent BaF2/CaF2 eutectic. The silver was replaced with gold to minimize the potential reactivity of the composite with possible environmental contaminants such as sulfur. The composites were fabricated by hot isostatic pressing (HIPping) and machined into pin specimens. The pins were slid against nickel-based superalloy disks. Sliding velocities ranged from 0.27 to 10.0 m/s and temperatures from 25 to 900 C. Frictions coefficients ranged from 0.25 to 0.40 and wear factors for the pin and disk were typically low 10(exp -5) cu mm/N-m. HIPped PM212 measured fully dense, whereas PM212/Au had 15 percent residual porosity. Examination of the microstructures with optical and scanning electron microscopy revealed the presence of pores in PM212/Au that were not present in PM212. Though the exact reason for the residual porosity PM212/Au was not determined, it may be due to practice morphology differences between the gold and silver and their effect on powder metallurgy processing.
Modulating Language Models with Emotions

Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems. In this paper, we propose a formulation of modulated layer normalization -- a technique inspired by computer vision -- that allows us to use large-scale language models for emotional response generation. In automatic and human evaluation on the MojiTalk dataset, our proposed modulated layer normalization method outperforms prior baseline methods while maintaining diversity, fluency, and coherence. Our method also obtains competitive performance even when using only 10% of the available training data.
The Effect of Inter-firm Managerial Social Ties on Alliance Formation: How Poorly Embedded Firms Overcome Network Disadvantages

We investigate how inter-firm managerial social ties (IFMSTs) facilitate strategic alliances formation by arguing that IFMSTs serve as an information channel critical for alliance formation. Using an extensive sample of U.S. public firms from the years 2000 to 2008, we find strong evidence firms connected through IFMSTs are more likely to form alliances and the effect of these social ties on alliance formation interacts with the preexisting alliance network structure to enhance alliance formation particularly for poorly embedded firms. Furthermore, poorly embedded firms benefit most from activity-based IFMSTs as these interpersonal ties engender an alternative mechanism for information exchanges at the firm level. We integrate insights from both relational embeddedness and structural embeddedness theories and exemplify the multiplexity nature of social embeddedness in inter-organizational behaviors.
Bias-tunable two-dimensional magnetic and topological materials

The search for novel two-dimensional (2D) materials is crucial for the development of next generation technologies such as electronics, optoelectronics, electrochemistry and biomedicine. In this work, we designed a series of 2D materials based on endohedral fullerenes and revealed that many of them integrate different functions in a single system, such as ferroelectricity with large electric dipole moments, multiple magnetic phases with both strong magnetic anisotropy and high Curie temperature, and quantum spin Hall effect or quantum anomalous Hall effect with robust topologically protected edge states. We further proposed a new type of topological field-effect transistor. These findings provide a strategy for using fullerenes as building blocks for the synthesis of novel 2D materials which can be easily controlled with a local electric field.
Anytime ellipsoidal over-approximation of forward reach sets of uncertain linear systems

Computing tight over-approximation of reach sets of a controlled uncertain dynamical system is a common practice in verification of safety-critical cyber-physical systems (CPS). While several algorithms are available for this purpose, they tend to be computationally demanding in CPS applications since here, the computational resources such as processor availability tend to be scarce, time-varying and difficult to model. A natural idea then is to design "computation-aware" algorithms that can dynamically adapt with respect to the processor availability in a provably safe manner. Even though this idea should be applicable in broader context, here we focus on ellipsoidal over-approximations. We demonstrate that the algorithms for ellipsoidal over-approximation of reach sets of uncertain linear systems, are well-suited for anytime implementation in the sense the quality of the over-approximation can be dynamically traded off depending on the computational time available, all the while guaranteeing safety. We give a numerical example to illustrate the idea, and point out possible future directions.
Distance Correlation Sure Independence Screening for Accelerated Feature Selection in Parkinson’s Disease Vocal Data

With the abundance of machine learning methods and the temptation of using them all in an ensemble, having model-agnostic feature selection is incredibly alluring. Principal component analysis was developed in 1901 and has been a strong contender in this role since, but in the end is an unsupervised method. It offers no guarantee that the features selected have predictive power because it does not know what is being predicted. To this end, Peng et al. developed the minimum redundancy-maximum relevance (mRMR) method in 2005. It uses the mutual information not only between predictors but also with the response in its calculation. Estimating mutual information and entropy tend to be expensive and problematic endeavors, which leads to excessive processing times even for data that is approximately 750 by 750 in a Leave-One-Subject- Out jackknife situation. To remedy this, we use a method from 2012 called Distance Correlation Sure Independence Screening (DC-SIS) which uses the distance correlation measure of Szekely et al. to select features that have the greatest dependence with the response. We show that this method produces statistically indistinguishable results to mRMR on Parkinson’s Disease vocal diagnosis data 90 times faster.
The Effect of Privacy Regulation on the Data Industry: Empirical Evidence from GDPR

This paper studies the effects of the EU's General Data Protection Regulation (GDPR) on the ability of firms to collect consumer data, identify consumers over time, accrue revenue via online advertising, and predict their behavior. We utilize a novel dataset that spans many different firms in the online travel industry that allows us to observe consumer visits, purchases, advertisements, and the output of a commercially used algorithm that predicts consumer behavior. We make use of a difference-in-differences analysis that exploits the geographic reach of GDPR. We find a 12.5% drop in observed consumers as a result of GDPR, but at the same time that remaining set of consumers is more persistently identifiable. We provide suggestive evidence that this is driven by a selected set of consumers who substitute from pre-existing privacy means towards those offered as part of GDPR. This substitution affects the ability to predict consumer behavior and preferences as well as improves the ability of advertisers to measure the effectiveness of advertising. In sum, this differential use of privacy tools leads to an increase in the average value of remaining consumers to advertisers, offsetting some of the losses from consumers that opt-out. Our results highlight the externalities that consumer privacy decisions have both on other consumers and for firms.
The equivalence between Feynman transform and Verdier duality

The equivalence between dg duality and Verdier duality has been established for cyclic operads earlier. We propose a generalization of this correspondence from cyclic operads and dg duality to twisted modular operads and the Feynman transform. Specifically, for each twisted modular operad $$\mathcal {P}$$ (taking values in dg-vector spaces over a field k of characteristic 0), there is a certain sheaf $$\mathcal {F}$$ associated with it on the moduli space of stable metric graphs such that the Verdier dual sheaf $$D\mathcal {F}$$ is associated with the Feynman transform $$F\mathcal {P}$$ of $$\mathcal {P}$$ . In the course of the proof, we also prove a relation between cyclic operads and modular operads originally proposed in the pioneering work of Getzler and Kapranov; however, to the best knowledge of the author, no proof has appeared. This geometric interpretation in operad theory is of fundamental importance. We believe this result will illuminate many aspects of the theory of modular operads and find many applications in the future. We illustrate an application of this result, giving another proof on the homotopy properties of the Feynman transform, which is quite intuitive and simpler than the original proof.
Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN

Existing thermal-to-visible face verification approaches expect the thermal and visible face images to be of similar resolution. This is unlikely in real-world long-range surveillance systems since humans are distant from the cameras. To address this issue, we introduce the task of thermal- to-visible face verification from low-resolution thermal images. Furthermore, we propose Axial-Generative Adversarial Network (Axial-GAN) to synthesize high-resolution visible images for matching. In the proposed approach we augment the GAN framework with axial-attention layers which leverage the recent advances in transformers for modelling long-range dependencies. We demonstrate the effectiveness of the proposed method by evaluating on two different thermal-visible face datasets. When compared to related state-of-the-art works, our results show significant improvements in both image quality and face verification performance, and are also much more efficient.
An Evertse–Ferretti Nevanlinna constant and its consequences

In this paper, we introduce the notion of an Evertse–Ferretti Nevanlinna constant and compare it with the birational Nevanlinna constant introduced by the authors in a recent joint paper. We then use it to recover several previously known results. This includes a 1999 example of Faltings from his Baker’s Garden article. We also extend the theory of these Nevanlinna constants to what we call “multidivisor Nevanlinna constants,” which allow the proximity function to involve the maximum of Weil functions for finitely many divisors.
Efficient Precoding for Single Carrier Modulation in Multi-User Massive MIMO Networks

By processing in the frequency domain (FD), massive MIMO systems can approach the theoretical per-user capacity using a single carrier modulation (SCM) waveform with a cyclic prefix. Minimum mean squared error (MMSE) detection and zero forcing (ZF) precoding have been shown to effectively cancel multi-user interference while compensating for inter-symbol interference. In this paper, we present a modified downlink precoding approach in the FD based on regularized zero forcing (RZF), which reuses the matrix inverses calculated as part of the FD MMSE uplink detection. By reusing these calculations, the computational complexity of the RZF precoder is drastically lowered, compared to the ZF precoder. Introduction of the regularization in RZF leads to a bias in the detected data symbols at the user terminals. We show this bias can be removed by incorporating a scaling factor at the receiver. Furthermore, it is noted that user powers have to be optimized to strike a balance between noise and interference seen at each user terminal. The resulting performance of the RZF precoder exceeds that of the ZF precoder for low and moderate input signal-to-noise ratio (SNR) conditions, and performance is equal for high input SNR. These results are established and confirmed by analysis and simulation.
A Microarchitecture Implementation Framework for Online Learning with Temporal Neural Networks

Temporal Neural Networks (TNNs) are spiking neural networks that use time as a resource to represent and process information, similar to the mammalian neocortex. In contrast to compute-intensive deep neural networks that employ separate training and inference phases, TNNs are capable of extremely efficient online incremental/continual learning and are excellent candidates for building edge-native sensory processing units. This work proposes a microarchitecture framework for implementing TNNs using standard CMOS. Gate-level implementations of three key building blocks are presented: 1) multi-synapse neurons, 2) multi-neuron columns, and 3) unsupervised and supervised online learning algorithms based on Spike Timing Dependent Plasticity (STDP). The proposed microarchitecture is embodied in a set of characteristic scaling equations for assessing the gate count, area, delay and power for any TNN design. Post-synthesis results (in 45nm CMOS) for the proposed designs are presented, and their online incremental learning capability is demonstrated.
Broadband Directional Control of Thermal Emission

We experimentally realize broadband directional thermal emitters by introducing two subwavelength photonic structures consisting of multiple oxides that exhibit epsilon-near-zero (ENZ) regions at long-wave infrared wavelengths.
Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff

The sensitivity to blockages is a key challenge for millimeter wave and terahertz networks in 5G and beyond. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. Proaction allows the network to anticipate future blockages, especially dynamic blockages, and initiate user hand-off beforehand. This article presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by red-green-blue (RGB) cameras deployed at the base stations. In particular, the article proposes a vision-aided wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. This is mainly achieved via a deep learning algorithm that learns from visual and wireless data how to predict incoming blockages. The predictions of this algorithm are used by the wireless network to proactively initiate hand-off decisions and avoid any unnecessary latency. The algorithm is developed on a vision-wireless dataset generated using the ViWi data-generation framework. Experimental results on two basestations with different cameras indicate that the algorithm is capable of accurately detecting incoming blockages more than ${\sim} 90\%$ of the time. Such blockage prediction ability is directly reflected in the accuracy of proactive hand-off, which also approaches 87%. This highlights a promising direction for enabling high reliability and low latency in future wireless networks.
Investigations on c-boomerang uniformity and perfect nonlinearity

Abstract We defined in Ellingsen et al. (2020) a new multiplicative c -differential, and the corresponding c -differential uniformity and we characterized the known perfect nonlinear functions with respect to this new concept, as well as the inverse function in any characteristic. Here, we extend, via this differential, the boomerang uniformity concept, as introduced at Eurocrypt ’18 by Cid et al. (2018), as a differential distinguisher of S -boxes of block ciphers. and investigate it in the context of perfect nonlinearity and related functions. We first characterize the new concept via Walsh transforms. We further investigate it in the context of perfect nonlinearity and related functions and describe this uniformity for the inverse function in even, respectively, odd characteristic. The methods are combinatorial and number theoretical in nature.
Best lower bound on the probability of a binomial exceeding its expectation

Abstract Let X be a random variable distributed according to the binomial distribution with parameters n and p . It is shown that P ( X > E X ) ⩾ 1 / 4 if 1 > p ⩾ c / n , where c ≔ ln ( 4 / 3 ) , the best possible constant factor.
Impact of Data Entry Interface Design on Cognitive Workload, Documentation Correctness, and Documentation Efficiency

Clinical documentation serves as the legal record of patient care and used to guide clinical decision making. Inadequately designed data entry user-interfaces may result in unintended consequences that negatively impact patient safety and outcomes because inaccurate information is used to guide clinical decision making. This study utilized an electronic simulated documentation interface (i.e., artificial electronic health record) combined with eye-tracking hardware to analyze documentation correctness, documentation efficiency, and cognitive workload of anesthesia providers (N = 20) generating documentation using different computer-assisted data entry types (drop-down box, radio button, check-box, and free text with autocomplete suggestions). Our study methodology incorporating eye-tracking with electronic health record user interfaces to assess documentation correctness, efficiency, and cognitive workload can be translated to other health care provider types.
Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems

Abstract Spatio-temporal dynamics of physical processes are generally modeled using partial differential equations (PDEs). Though the core dynamics follows some principles of physics, real-world physical processes are often driven by unknown external sources. In such cases, developing a purely analytical model becomes very difficult and data-driven modeling can be of assistance. In this paper, we present a hybrid framework combining physics-based numerical models with deep learning for source identification and forecasting of spatio-temporal dynamical systems with unobservable time-varying external sources. We formulate our model PhICNet as a convolutional recurrent neural network (RNN) which is end-to-end trainable for spatio-temporal evolution prediction of dynamical systems and learns the source behavior as an internal state of the RNN. Experimental results show that the proposed model can forecast the dynamics for a relatively long time and identify the sources as well.
Wasserstein gradients for the temporal evolution of probability distributions

Many studies have been conducted on flows of probability measures, often in terms of gradient flows. We utilize a generalized notion of derivatives with respect to time to model the instantaneous evolution of empirically observed one-dimensional distributions that vary over time and develop consistent estimates for these derivatives. Employing local Frechet regression and working in local tangent spaces with regard to the Wasserstein metric, we derive the rate of convergence of the proposed estimators. The resulting time dynamics are illustrated with time-varying distribution data that include yearly income distributions and the evolution of mortality over calendar years.
Executive stock options and systemic risk

Abstract Employing a novel control function regression method that accounts for the endogenous matching of banks and executives, we find that equity portfolio vega, the sensitivity of executives’ equity portfolio value to their firms’ stock return volatility, leads to systemic risk that manifests during subsequent economic contractions but not expansions. We further find that vega encourages systemically risky policies, including maintaining lower common equity Tier 1 capital ratios, relying on more run-prone debt financing, and making more procyclical investments. Collectively, our evidence suggests that executives’ incentive-compensation contracts promote systemic risk-taking through banks’ lending, investing, and financing practices.
A decoupled, stable, and linear FEM for a phase-field model of variable density two-phase incompressible surface flow

Abstract The paper considers a thermodynamically consistent phase-field model of a two-phase flow of incompressible viscous fluids . The model allows for a non-linear dependence of the fluid density on the phase-field order parameter. Driven by applications in biomembrane studies, the model is written for tangential flows of fluids constrained to a surface and consists of (surface) Navier–Stokes–Cahn–Hilliard type equations. We apply an unfitted finite element method to discretize the system and introduce a fully discrete time-stepping scheme with the following properties : (i) the scheme decouples the fluid and phase-field equation solvers at each time step, (ii) the resulting two algebraic systems are linear, and (iii) the numerical solution satisfies the same stability bound as the solution of the original system under some restrictions on the discretization parameters. Numerical examples are provided to demonstrate the stability, accuracy, and overall efficiency of the approach. Our computational study of several two-phase surface flows reveals some interesting dependencies of flow statistics on the geometry.
Efficient Topology-Optimized Couplers for On-Chip Single-Photon Sources

Room temperature single-photon sources (SPSs) are critical for the emerging practical quantum applications such as on-chip photonic circuity for quantum communications systems and integrated quantum sensors. However, direct integration of an SPS into on-chip photonic systems remains challenging due to low coupling efficiencies between the SPS and the photonic circuitry that often involve size mismatch and dissimilar materials. Here, we develop an adjoint topology optimization scheme to design high-efficiency couplers between a photonic waveguide and SPS in hexagonal boron nitride (hBN). The algorithm accounts for fabrication constraints and the SPS location uncertainty. First, a library of designs for the different positions of the hBN flake containing an SPS with respect to a Si$_{3}$N$_{4}$ waveguide is generated, demonstrating an average coupling efficiency of 78%. Then, the designs are inspected with dimensionality reduction technique to investigate the relationship between the device geometry (topology) and performance. The fundamental, physics-based intuition gained from this approach could enable the design of high-performance quantum devices
Discordant humoral and T cell immune responses to SARS-CoV-2 vaccination in people with multiple sclerosis on anti-CD20 therapy.

Background Sphingosine-1-phosphate receptor (S1P) modulators and anti-CD20 therapies impair humoral responses to SARS-CoV-2 mRNA vaccines. Relatively few studies have assessed the impact of an array of disease modifying therapies (DMTs) for multiple sclerosis (MS) on T cell immune responses to SARS-CoV-2 vaccination. Methods In 101 people with MS, we measured humoral responses via an immunoassay to measure IgG against the COVID-19 spike S1 glycoprotein in serum. We also measured T cell responses using FluoroSpot assay for interferon gamma (IFN-γ) (Mabtech, Sweden) using cryopreserved rested PBMCs and then incubated in cRPMI with 1µg/ml of pooled peptides spanning the entire spike glycoprotein (Genscript, 2 pools; 158 peptides each). Plates were read on an AID iSpot Spectrum to determine the number of spot forming cells (SFC)/106 PBMCs. We tested for differences in immune responses across DMTs using linear models. Findings Humoral responses were detected in 22/39 (56.4%) participants on anti-CD20 and in 59/63 (93.6%) participants on no or other DMTs. In a subset (n=88; 87%), T cell responses were detected in 76/88 (86%), including 32/33 (96.9%) participants on anti-CD20 therapies. Anti-CD20 therapies were associated with an increase in IFN-γ SFC counts relative to those on no DMT or other DMTs (for anti-CD20 vs. no DMT: 425.9% higher [95%CI: 109.6%, 1206.6%] higher; p Interpretation We identified a robust T cell response in individuals on anti-CD20 therapies despite a reduced humoral response to SARS-CoV-2 vaccination. Follow up studies are needed to determine if this translates to protection against COVID-19 infection. Funding This study was funded partially by 1K01MH121582-01 from NIH/NIMH and TA-1805-31136 from the National MS Society (NMSS) to KCF and TA-1503-03465 and JF-2007-37655 from the NMSS to PB. This study was also supported through the generosity of the collective community of donors to the Johns Hopkins University School of Medicine for COVID research.
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning

Autonomous vehicles need to handle various traffic conditions and make safe and efficient decisions and maneuvers. However, on the one hand, a single optimization/sampling-based motion planner cannot efficiently generate safe trajectories in real time, particularly when there are many interactive vehicles near by. On the other hand, end-to-end learning methods cannot assure the safety of the outcomes. To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers. Safety is guaranteed by the low-level optimization/sampling-based controllers, while the high-level reinforcement learning algorithm makes H-CtRL an adaptive and efficient behavior planner. To train and test our proposed algorithm, we built a simulator that can reproduce traffic scenes using real-world datasets. The proposed HCtRL is proved to be effective in various realistic simulation scenarios, with satisfying performance in terms of both safety and efficiency.
Detect, Reject, Correct: Crossmodal Compensation of Corrupted Sensors

Using sensor data from multiple modalities presents an opportunity to encode redundant and complementary features that can be useful when one modality is corrupted or noisy. Humans do this everyday, relying on touch and proprioceptive feedback in visually-challenging environments. However, robots might not always know when their sensors are corrupted, as even broken sensors can return valid values. In this work, we introduce the Crossmodal Compensation Model (CCM), which can detect corrupted sensor modalities and compensate for them. CMM is a representation model learned with self-supervision that leverages unimodal reconstruction loss for corruption detection. CCM then discards the corrupted modality and compensates for it with information from the remaining sensors. We show that CCM learns rich state representations that can be used for manipulation policies learned with reinforcement learning, even when input modalities are corrupted during policy rollout in ways not seen during training.
Hamiltonian structure of the guiding-center Vlasov–Maxwell equations

The Hamiltonian structure of the guiding-center Vlasov–Maxwell equations is presented in terms of a Hamiltonian functional and a guiding-center Vlasov–Maxwell bracket. The bracket, which is shown to satisfy the Jacobi identity exactly, is used to show that the guiding-center momentum and angular-momentum conservation laws can also be expressed in the Hamiltonian form.
Mathematical Incompleteness Results in First-Order Peano Arithmetic: A Revisionist View of the Early History

In the Handbook of Mathematical Logic, the Paris-Harrington variant of Ramsey's theorem is celebrated as the first result of a long ‘search’ for a purely mathematical incompleteness result in first...
On the continuous dual Hahn process

Abstract We extend the continuous dual Hahn process ( T t ) of Corwin and Knizel from a finite time interval to the entire real line by taking a limit of a closely related Markov process ( T t ) . We also characterize processes ( T t ) by conditional means and variances under bidirectional conditioning, and we prove that continuous dual Hahn polynomials are orthogonal martingale polynomials for both processes.
Constructing equivalence bimodules between noncommutative solenoids: A two-pronged approach

Abstract We revisit and generalize the application of a method introduced by Latremoliere and Packer for constructing finitely generated projective modules over the noncommutative solenoid C*-algebras. By realizing them as direct limits of rotation algebras, the method constructs directed systems of equivalence bimodules between rotation algebras that satisfy the necessary compatibility conditions to build Morita equivalence bimodules between the direct limit C*-algebras. In the irrational case, we use a fixed projection in a matrix algebra over the rotation algebra satisfying a key condition to build an equivalence bimodule at each stage following a construction of Rieffel. From this, our main result shows that two irrational noncommutative solenoids are Morita equivalent if and only if such a projection exists. We also make additional observations about the Heisenberg bimodules construction studied by the aforementioned two authors and connect the two constructions.
Gluon Parton Distribution of the Pion from Lattice QCD

Abstract We present the first determination of the x-dependent pion gluon distribution from lattice QCD using the pseudo-PDF approach. We use lattice ensembles with 2+1+1 flavors of highly improved staggered quarks (HISQ), generated by MILC Collaboration, at two lattice spacings a ≈ 0.12 and 0.15 fm and three pion masses M π ≈ 220 , 310 and 690 MeV. We use clover fermions for the valence action and momentum smearing to achieve pion boost momentum up to 2.29 GeV. We find that the dependence of the pion gluon parton distribution on lattice spacing and pion mass is mild. We compare our results from the lightest pion mass ensemble with the determination by JAM and xFitter global fits.
Variational Bayesian Reinforcement Learning with Regret Bounds

We consider the exploration-exploitation trade-off in reinforcement learning and we show that an agent imbued with an epistemic-risk-seeking utility function is able to explore efficiently, as measured by regret. The parameter that controls how risk-seeking the agent is can be optimized to minimize regret, or annealed according to a schedule. We call the resulting algorithm K-learning and we show that the K-values that the agent maintains are optimistic for the expected optimal Q-values at each state-action pair. The utility function approach induces a natural Boltzmann exploration policy for which the 'temperature' parameter is equal to the risk-seeking parameter. This policy achieves a Bayesian regret bound of $\tilde O(L^{3/2} \sqrt{SAT})$, where L is the time horizon, S is the number of states, A is the number of actions, and T is the total number of elapsed time-steps. K-learning can be interpreted as mirror descent in the policy space, and it is similar to other well-known methods in the literature, including Q-learning, soft-Q-learning, and maximum entropy policy gradient. K-learning is simple to implement, as it only requires adding a bonus to the reward at each state-action and then solving a Bellman equation. We conclude with a numerical example demonstrating that K-learning is competitive with other state-of-the-art algorithms in practice.
Knowledge & Learning-based Adaptable System for Sensitive Information Identification and Handling

Diagnostic data such as logs and memory dumps from production systems are often shared with development teams to do root cause analysis of system crashes. Invariably such diagnostic data contains sensitive information and sharing it can lead to data leaks. To handle this problem we present Knowledge and Learning-based Adaptable System for Sensitive InFormation Identification and Handling (KLASSIFI) which is an end to end system capable of identifying and redacting sensitive information present in diagnostic data. KLASSIFI is highly customizable, allowing it to be used for various different business use cases by simply changing the configuration. KLASSIFI ensures that the output file is useful by retaining the metadata which is used by various debugging tools. Various optimizations have been done to improve the performance of KLASSIFI. Empirical evaluation of KLASSIFI shows that it is able to process large files (128 GB) in 84 minutes and its performance scales linearly with varying factors. This points to practicability of KLASSIFI.
Step-Edge Epitaxy for Borophene Growth on Insulators

Borophene─a monatomic layer of boron atoms─stands out among two-dimensional (2D) materials, with its versatile properties tantalizing for physics exploration and next-generation devices. Yet its ph...
The numerical solutions of linear semi-discrete evolution problems on the half-line using the Unified Transform Method.

We discuss a semi-discrete analogue of the Unified Transform Method, introduced by A. S. Fokas, to solve initial-boundary-value problems for linear evolution partial differential equations of constant coefficients. The semi-discrete method is applied to various spacial discretizations of several first and second-order linear equations on the half-line $x \geq 0$, producing the exact solution for the semi-discrete problem, given appropriate initial and boundary data. We additionally show how the Unified Transform Method treats derivative boundary conditions and ghost points introduced by the choice of discretization stencil. We consider the continuum limit of the semi-discrete solutions and provide several numerical examples.
Spaces of embeddings: Nonsingular bilinear maps, chirality, and their generalizations

Given a space X we study the topology of the space of embeddings of X into $\mathbb{R}^d$ through the combinatorics of triangulations of X. We give a simple combinatorial formula for upper bounds for the largest dimension of a sphere that antipodally maps into the space of embeddings. This result summarizes and extends results about the nonembeddability of complexes into $\mathbb{R}^d$, the nonexistence of nonsingular bilinear maps, and the study of embeddings into $\mathbb{R}^d$ up to isotopy, such as the chirality of spatial graphs.
Reliability of InP-based HBT IC technology for high-speed, low-power applications

We report on the reliability of an InP-based AlInAs/GaInAs heterojunction bipolar transistor technology which has applications in very high-speed and low power integrated circuits. We have performed extensive accelerated lifetest experiments under different dc bias conditions and different ambient temperatures. For high-performance devices we predict meantime-to-failures in excess of 10/sup 7/ hours at 125/spl deg/C junction temperatures. We have also investigated the effects of hydrogen on HBT device characteristics which is particularly important for integrated circuits in hermetically sealed packages. We show that the transistor performance is not sensitive to a 4% hydrogen ambient. For integrated circuits requiring precision thin-film resistors we performed lifetest experiments on tantalum-nitride resistors used in our IC process. We show that these thin-film resistors are very stable and exhibit mean-time-to-failures exceeding that of discrete transistors.
Masters, Slaves, and Exchange: Power's Purchase in the Old South

Introduction 1. Money and moralism 2. Slaves and spending 3. Servants served 4. Black markets 5. Gilt chains 6. The choice Conclusion.
Wilderness, the Courts and the Effect of Politics on Judicial Decisionmaking

Empirical analyses of cases from federal courts have attempted to determine the effect of judges’ political ideology on their decisions. This question holds interest for scholars from many disciplines. Investigating judicial review of the actions of administrative agencies should provide strong evidence on the question of political influence because applicable rules of judicial deference to administrative decisions ought to lead judges to reach politically neutral results. Yet several studies have found a strong correlation between results in these cases and proxies for political ideology. Cases involving the interpretation of environmental law have been of particular interest as a subset of this research because political ideology is also thought to predict views on environmental regulation. Nevertheless, an earlier work offered initial evidence that this phenomenon may not hold in cases involving review of agency decisions administering the Wilderness Act of 1964. Indeed, the cases showed a pro-wilderness tilt in the outcomes, rather than a pro-agency tilt. This Article builds on that earlier evidence. It first provides an overview of empirical studies of environmental decisions in federal courts and then reviews the Wilderness Act and current problems arising in the administrative application of it. The Article then analyzes whether ideological proxies employed in earlier studies strongly correlate with the outcome of the Wilderness Act cases using standard statistical analysis. The analysis shows a lack of correlation between politics and the aggregate outcome of wilderness decisions, namely a tilt in a pro-wilderness direction.