cssi/SciSciGPT-SciSciNet huggingface dataset:
SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding

Spoken language understanding (SLU) requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. To boost the models’ performance, various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. However, the inherent disparities between the two modalities necessitate a mutual analysis. In this paper, we propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules. Besides conducting a self-supervised masked language modeling task on the two individual modules using unpaired speech and text, SPLAT aligns representations from the two modules in a shared latent space using a small amount of paired speech and text. Thus, during fine-tuning, the speech module alone can produce representations carrying both acoustic information and contextual semantic knowledge of an input acoustic signal. Experimental results verify the effectiveness of our approach on various SLU tasks. For example, SPLAT improves the previous state-of-the-art performance on the Spoken SQuAD dataset by more than 10%.
Microwave Package Design for Superconducting Quantum Processors

Engineering challenges for scaling up microwave packages for superconducting qubits while simultaneously maintain high fidelity and coherence times are addressed.
When Critical Race Theory Enters the Law & Technology Frame

This essay suggests that law and technology scholarship tends to focus on fixing technologies without critically analyzing the role of law in creating the social context in which the technologies are adopted. It demonstrate how legal scholars incorporate a critical race lens to expand the discourse on law and technology as an antidote to this tendency in at least two ways. First, scholars draw upon an understanding of race as a socially and historically situated construct to critique the intersection between law and technology in society. Second, scholars illuminate how race shapes our perceptions of social reality to provide guidance on how to expand critiques of law through examination of technologies in society as well. In framing these critiques as the intersection of critical race theory and law, this essay seeks to expand the kinds of questions being interrogated by legal scholars at the intersection of race, technology and the law going forward.
Morphology Matters: A Multilingual Language Modeling Analysis

Prior studies in multilingual language modeling (e.g., Cotterell et al., 2018; Mielke et al., 2019) disagree on whether or not inflectional morphology makes languages harder to model. We attempt to resolve the disagreement and extend those studies. We compile a larger corpus of 145 Bible translations in 92 languages and a larger number of typological features. We fill in missing typological data for several languages and consider corpus-based measures of morphological complexity in addition to expert-produced typological features. We find that several morphological measures are significantly associated with higher surprisal when LSTM models are trained with BPE-segmented data. We also investigate linguistically-motivated subword segmentation strategies like Morfessor and Finite-State Transducers (FSTs) and find that these segmentation strategies yield better performance and reduce the impact of a language's morphology on language modeling.
Self-supervised Representation Learning for Astronomical Images

Author(s): Hayat, MA; Stein, G; Harrington, P; Lukic, Z; Mustafa, M | Abstract: Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data. We apply a contrastive learning framework on multiband galaxy photometry from the Sloan Digital Sky Survey (SDSS), to learn image representations. We then use them for galaxy morphology classification and fine-tune them for photometric redshift estimation, using labels from the Galaxy Zoo 2 data set and SDSS spectroscopy. In both downstream tasks, using the same learned representations, we outperform the supervised state-of-the-art results, and we show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training. The codes, trained models, and data can be found at https://portal.nersc.gov/project/dasrepo/self-supervised-learning-sdss.
Belief polarization in a complex world: A learning theory perspective

We present two models of how people form beliefs that are based on machine learning theory. We illustrate how these models give insight into observed human phenomena by showing how polarized beliefs can arise even when people are exposed to almost identical sources of information. In our first model, people form beliefs that are deterministic functions that best fit their past data (training sets). In that model, their inability to form probabilistic beliefs can lead people to have opposing views even if their data are drawn from distributions that only slightly disagree. In the second model, people pay a cost that is increasing in the complexity of the function that represents their beliefs. In this second model, even with large training sets drawn from exactly the same distribution, agents can disagree substantially because they simplify the world along different dimensions. We discuss what these models of belief formation suggest for improving people’s accuracy and agreement.
Facilitating the Search for Partners on Matching Platforms

Two-sided matching platforms can control and optimize over many aspects of the search for partners. To understand how matching platforms should be designed, we introduce a dynamic two-sided search ...
String Stability of Connected Vehicle Platoons Under Lossy V2V Communication

Recent advances in vehicle connectivity have allowed formation of autonomous vehicle platoons for improved mobility and traffic throughput. In order to avoid a pile-up in such platoons, it is important to ensure platoon (string) stability, which is the focus of this work. As per conventional definition of string stability, the power (2-norm) of the spacing error signals should not amplify downstream in a platoon. But in practice, it is the infinity-norm of the spacing error signal that dictates whether a collision occurs. We address this discrepancy in the first part of our work, where we reconsider string stability from a safety perspective and develop an upper limit on the maximum spacing error in a homogeneous platoon as a function of the acceleration maneuver of the lead vehicle. In the second part of this paper, we extend our previous results by providing the minimum achievable time headway for platoons with two-predecessor lookup schemes experiencing burst-noise packet losses. Finally, we utilize throttle and brake maps to develop a longitudinal vehicle model and validate it against a Lincoln MKZ which is then used for numerical corroboration of the proposed time headway selection algorithms.
Predicting Localized Primordial Star Formation with Deep Convolutional Neural Networks

We investigate applying 3D deep convolutional neural networks as fast surrogate models of the formation and feedback effects of primordial stars in hydrodynamic cosmological simulations of the first galaxies. Here, we present the surrogate model to predict localized primordial star formation; the feedback model will be presented in a subsequent paper. The star formation prediction model consists of two sub-models: the first is a 3D volume classifier that predicts which (10 comoving kpc)$^3$ volumes will host star formation, followed by a 3D Inception-based U-net voxel segmentation model that predicts which voxels will form primordial stars. We find that the combined model predicts primordial star forming volumes with high skill, with $F_1 >0.995$ and true skill score $>0.994$. The star formation is localized within the volume to $\lesssim5^3$~voxels ($\sim1.6$~comoving kpc$^3$) with $F_1>0.399$ and true skill score $>0.857$. Applied to simulations with low spatial resolution, the model predicts star forming regions in the same locations and at similar redshifts as sites in resolved full-physics simulations that explicitly model primordial star formation and feedback. When applied to simulations with lower mass resolution, we find that the model predicts star forming regions at later redshift due to delayed structure formation resulting from lower mass resolution. Our model predicts primordial star formation without halo finding, so will be useful in spatially under-resolved simulations that cannot resolve primordial star forming halos. To our knowledge, this is the first model that can predict primordial star forming regions that match highly-resolved cosmological simulations.
Parametric Testing of Chevrons on Single Flow Hot Jets

A parametric family of chevron nozzles have been studied, looking for relationships between chevron geometric parameters, flow characteristics, and far-field noise. Both cold and hot conditions have been run at acoustic Mach number 0.9. Ten models have been tested, varying chevron count, penetration, length, and chevron symmetry. Four comparative studies were defined from these datasets which show: that chevron length is not a major impact on either flow or sound; that chevron penetration increases noise at high frequency and lowers it at low frequency, especially for low chevron counts; that chevron count is a strong player with good low frequency reductions being achieved with high chevron count without strong high frequency penalty; and that chevron asymmetry slightly reduces the impact of the chevron. Finally, it is shown that although the hot jets differ systematically from the cold one, the overall trends with chevron parameters is the same.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Levenberg-Marquardt Algorithm

Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
Multiple-Shooting Adjoint Method for Whole-Brain Dynamic Causal Modeling

Dynamic causal modeling (DCM) is a Bayesian framework to infer directed connections between compartments, and has been used to describe the interactions between underlying neural populations based on functional neuroimaging data. DCM is typically analyzed with the expectation-maximization (EM) algorithm. However, because the inversion of a large-scale continuous system is difficult when noisy observations are present, DCM by EM is typically limited to a small number of compartments (<10). Another drawback with the current method is its complexity; when the forward model changes, the posterior mean changes, and we need to re-derive the algorithm for optimization. In this project, we propose the Multiple-Shooting Adjoint (MSA) method to address these limitations. MSA uses the multiple-shooting method for parameter estimation in ordinary differential equations (ODEs) under noisy observations, and is suitable for large-scale systems such as whole-brain analysis in functional MRI (fMRI). Furthermore, MSA uses the adjoint method for accurate gradient estimation in the ODE; since the adjoint method is generic, MSA is a generic method for both linear and non-linear systems, and does not require re-derivation of the algorithm as in EM. We validate MSA in extensive experiments: 1) in toy examples with both linear and non-linear models, we show that MSA achieves better accuracy in parameter value estimation than EM; furthermore, MSA can be successfully applied to large systems with up to 100 compartments; and 2) using real fMRI data, we apply MSA to the estimation of the whole-brain effective connectome and show improved classification of autism spectrum disorder (ASD) vs. control compared to using the functional connectome. The package is provided https://jzkay12.github.io/TorchDiffEqPack.
ZeRO-Offload: Democratizing Billion-Scale Model Training

Large-scale model training has been a playing ground for a limited few requiring complex model refactoring and access to prohibitively expensive GPU clusters. ZeRO-Offload changes the large model training landscape by making large model training accessible to nearly everyone. It can train models with over 13 billion parameters on a single GPU, a 10x increase in size compared to popular framework such as PyTorch, and it does so without requiring any model change from the data scientists or sacrificing computational efficiency. ZeRO-Offload enables large model training by offloading data and compute to CPU. To preserve compute efficiency, it is designed to minimize the data movement to/from GPU, and reduce CPU compute time while maximizing memory savings on GPU. As a result, ZeRO-Offload can achieve 40 TFlops/GPU on a single NVIDIA V100 GPU for 10B parameter model compared to 30TF using PyTorch alone for a 1.4B parameter model, the largest that can be trained without running out of memory. ZeRO-Offload is also designed to scale on multiple-GPUs when available, offering near linear speedup on up to 128 GPUs. Additionally, it can work together with model parallelism to train models with over 70 billion parameters on a single DGX-2 box, a 4.5x increase in model size compared to using model parallelism alone. By combining compute and memory efficiency with ease-of-use, ZeRO-Offload democratizes large-scale model training making it accessible to even data scientists with access to just a single GPU.
Dynamically grown generative adversarial networks

Recent work introduced progressive network growing as a promising way to ease the training for large GANs, but the model design and architecture-growing strategy still remain under-explored and needs manual design for different image data. In this paper, we propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator. It enjoys the benefits of both eased training because of progressive growing and improved performance because of broader architecture design space. Experimental results demonstrate new state-of-the-art of image generation. Observations in the search procedure also provide constructive insights into the GAN model design such as generator-discriminator balance and convolutional layer choices.
Reconfigurable model execution in the OpenMDAO framework

NASA's OpenMDAO framework facilitates constructing complex models and computing their derivatives for multidisciplinary design optimization. Decomposing a model into components that follow a prescribed interface enables OpenMDAO to assemble multidisciplinary derivatives from the component derivatives using what amounts to the adjoint method, direct method, chain rule, global sensitivity equations, or any combination thereof, using the MAUD architecture. OpenMDAO also handles the distribution of processors among the disciplines by hierarchically grouping the components, and it automates the data transfer between components that are on different processors. These features have made OpenMDAO useful for applications in aircraft design, satellite design, wind turbine design, and aircraft engine design, among others. This paper presents new algorithms for OpenMDAO that enable reconfigurable model execution. This concept refers to dynamically changing, during execution, one or more of: the variable sizes, solution algorithm, parallel load balancing, or set of variables-i.e., adding and removing components, perhaps to switch to a higher-fidelity sub-model. Any component can reconfigure at any point, even when running in parallel with other components, and the reconfiguration algorithm presented here performs the synchronized updates to all other components that are affected. A reconfigurable software framework for multidisciplinary design optimization enables new adaptive solvers, adaptive parallelization, and new applications such as gradient-based optimization with overset flow solvers and adaptive mesh refinement. Benchmarking results demonstrate the time savings for reconfiguration compared to setting up the model again from scratch, which can be significant in large-scale problems. Additionally, the new reconfigurability feature is applied to a mission profile optimization problem for commercial aircraft where both the parametrization of the mission profile and the time discretization are adaptively refined, resulting in computational savings of roughly 10% and the elimination of oscillations in the optimized altitude profile.
Adaptive Weighted Discriminator for Training Generative Adversarial Networks

Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN’s discriminators and they all have a common structure: a sum of real and fake losses that only depends on the actual and generated data respectively. One challenge associated with an equally weighted sum of two losses is that the training may benefit one loss but harm the other, which we show causes instability and mode collapse. In this paper, we introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions or aw-loss functions. Using the gradients of the real and fake parts of the loss, we can adaptively choose weights to train a discriminator in the direction that benefits the GAN’s stability. Our method can be potentially applied to any discriminator model with a loss that is a sum of the real and fake parts. Experiments validated the effectiveness of our loss functions on unconditional and conditional image generation tasks, improving the baseline results by a significant margin on CIFAR-10, STL-10, and CIFAR-100 datasets in Inception Scores (IS) and Frechet Inception Distance (FID) metrics.
An Inexpensive, High-Precision, Modular Spherical Treadmill Setup Optimized for Drosophila Experiments

To pursue a more mechanistic understanding of the neural control of behavior, many neuroethologists study animal behavior in controlled laboratory environments. One popular approach is to measure the movements of restrained animals while presenting controlled sensory stimulation. This approach is especially powerful when applied to genetic model organisms, such as Drosophila melanogaster, where modern genetic tools enable unprecedented access to the nervous system for activity monitoring or targeted manipulation. While there is a long history of measuring the behavior of body- and head-fixed insects walking on an air-supported ball, the methods typically require complex setups with many custom components. Here we present a compact, simplified setup for these experiments that achieves high-performance at low cost. The simplified setup integrates existing hardware and software solutions with new component designs. We replaced expensive optomechanical and custom machined components with off-the-shelf and 3D-printed parts, and built the system around a low-cost camera that achieves 180 Hz imaging and an inexpensive tablet computer to present view-angle-corrected stimuli updated through a local network. We quantify the performance of the integrated system and characterize the visually guided behavior of flies in response to a range of visual stimuli. In this paper, we thoroughly document the improved system; the accompanying repository incorporates CAD files, parts lists, source code, and detailed instructions. We detail a complete ~$300 system, including a cold-anesthesia tethering stage, that is ideal for hands-on teaching laboratories. This represents a nearly 50-fold cost reduction as compared to a typical system used in research laboratories, yet is fully featured and yields excellent performance. We report the current state of this system, which started with a 1-day teaching lab for which we built seven parallel setups and continues toward a setup in our lab for larger-scale analysis of visual-motor behavior in flies. Because of the simplicity, compactness, and low cost of this system, we believe that high-performance measurements of tethered insect behavior should now be widely accessible and suitable for integration into many systems. This access enables broad opportunities for comparative work across labs, species, and behavioral paradigms.
Trying and Failing: Biases in Donor Aversion to Rejection

Non-profit organizations (NPOs) play a critical role in advancing the UN Sustainable Development Goals, directing resources from donors to aid recipients. To achieve this, they often rely on donations of physical goods, which can not only further the mission of the NPO, but also give new life to used goods destined for the landfill. However, not all donations are wanted or needed - the NPO may be space constrained or the donation may be of poor quality. Accepting unwanted donations imposes non-trivial operational costs on the NPO or its downstream partners. Nonetheless, NPOs often hesitate to reject unwanted items, fearing negative repercussions for future donations. To better understand these repercussions, we study how donors respond to rejection using a controlled experiment. Subjects repeatedly choose whether to complete a real-effort task that generates a donation, which is rejected with a fixed probability whose value is unknown to them. We measure subjects' donation decisions and beliefs about the probability that their donations are accepted. We compare these measures against a for-profit experimental condition wherein the subject, not an NPO, receives the payment generated by the real-effort task. Our results identify a mechanism by which rejections affect donations: via a reduction in donors' beliefs about future success. Moreover, we identify a novel instance of self-serving bias: when high effort is required to make a donation, subjects' beliefs respond significantly more negatively to rejection in the donation condition than in the for-profit condition. We propose ways in which NPOs can alleviate this bias.
Scattering amplitudes for monopoles: pairwise little group and pairwise helicity

Author(s): Csaki, Csaba; Hong, Sungwoo; Shirman, Yuri; Telem, Ofri; Terning, John; Waterbury, Michael | Abstract: On-shell methods are particularly suited for exploring the scattering of electrically and magnetically charged objects, for which there is no local and Lorentz invariant Lagrangian description. In this paper we show how to construct a Lorentz-invariant S-matrix for the scattering of electrically and magnetically charged particles, without ever having to refer to a Dirac string. A key ingredient is a revision of our fundamental understanding of multi-particle representations of the Poincar #x27;e group. Surprisingly, the asymptotic states for electric-magnetic scattering transform with an additional little group phase, associated with pairs of electrically and magnetically charged particles. The corresponding "pairwise helicity" is identified with the quantized "cross product" of charges, $e_1 g_2 - e_2 g_1$, for every charge-monopole pair, and represents the extra angular momentum stored in the asymptotic electromagnetic field. We define a new kind of pairwise spinor-helicity variable, which serves as an additional building block for electric-magnetic scattering amplitudes. We then construct the most general 3-point S-matrix elements, as well as the full partial wave decomposition for the $2\to 2$ fermion-monopole S-matrix. In particular, we derive the famous helicity flip in the lowest partial wave as a simple consequence of a generalized spin-helicity selection rule, as well as the full angular dependence for the higher partial waves. Our construction provides a significant new achievement for the on-shell program, succeeding where the Lagrangian description has so far failed.
Hoplite: efficient and fault-tolerant collective communication for task-based distributed systems

Task-based distributed frameworks (e.g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving. As more data-intensive applications move to run on top of task-based systems, collective communication efficiency has become an important problem. Unfortunately, traditional collective communication libraries (e.g., MPI, Horovod, NCCL) are an ill fit, because they require the communication schedule to be known before runtime and they do not provide fault tolerance. We design and implement Hoplite, an efficient and fault-tolerant collective communication layer for task-based distributed systems. Our key technique is to compute data transfer schedules on the fly and execute the schedules efficiently through fine-grained pipelining. At the same time, when a task fails, the data transfer schedule adapts quickly to allow other tasks to keep making progress. We apply Hoplite to a popular task-based distributed framework, Ray. We show that Hoplite speeds up asynchronous stochastic gradient descent, reinforcement learning, and serving an ensemble of machine learning models that are difficult to execute efficiently with traditional collective communication by up to 7.8x, 3.9x, and 3.3x, respectively.
Potato crop stress identification in aerial images using deep learning-based object detection

Recent research on the application of remote sensing and deep learning-based analysis in precision agriculture demonstrated a potential for improved crop management and reduced environmental impacts of agricultural production. Despite the promising results, the practical relevance of these technologies for actual field deployment requires novel algorithms that are customized for analysis of agricultural images and robust to implementation on natural field imagery. The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Specifically, we examine premature plant senescence resulting in drought stress on Russet Burbank potato plants. The proposed deep learning model, named Retina-UNet-Ag, is a variant of Retina-UNet (Jaeger et al., 2018) and includes connections from low-level semantic dense representation maps to the feature pyramid network. The paper also introduces a dataset of field images acquired with a Parrot Sequoia camera carried by a Solo unmanned aerial vehicle. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice score coefficient of 0.74. A comparison to related state-of-the-art deep learning models for object detection revealed that the presented approach is effective for the task at hand. The method applied here is conducive toward the assessment and recognition of potato crop stress (early plant senescence resulting from drought stress in this case) in natural aerial field images collected under real conditions.
Charge Density and Hydrophobicity-Dominated Regimes in the Phase Behavior of Complex Coacervates

The role of hydrophobicity, and particularly nonionic hydrophobic comonomers, on the phase behavior of polyelectrolyte complex coacervates is not well-understood. Here, we address this problem by synthesizing a library of polymers with a wide range of charge densities and nonionic hydrophobic side chain lengths, and characterizing their phase behavior by optical turbidity. The polymers were prepared by post-polymerization modification of poly(N-acryloxy succinimide), targeting charge densities between 40 and 100% and nonionic aliphatic sidechains with lengths from 0 to 12 carbons long. Turbidity measurements on pairs of polycations and polyanions with matched charge densities and nonionic sidechain lengths revealed a complex salt response with distinct charge density-dominated and hydrophobicity-dominated regimes. The polymer solubilities were not directly correlated with the phase behavior of the coacervates, indicating the difficulty of understanding the coacervate phase behavior in terms of the polymer-water interaction parameter. This result suggests that there is significant room for further work to understand the mechanisms by which specific molecular-scale interactions moderate the phase behavior of complex coacervates.
Thouless–Anderson–Palmer Equations for the Ghatak–Sherrington Mean Field Spin Glass Model

We derive the Thouless–Anderson–Palmer (TAP) equations for the Ghatak and Sherrington model (J Phys C 10(16):3149–3156, 1977). Our derivation, based on the cavity method, holds at high temperature and at all values of the crystal field. It confirms the prediction of Yokota (J Phys Condens Matter 4(10):2615–2622, 1992).
Critical Window Variable Selection for Mixtures: Estimating the Impact of Multiple Air Pollutants on Stillbirth

BACKGROUND AND AIM: Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnera...
A Hypothesis Testing Approach to Nonstationary Source Separation

The extraction of nonstationary signals from blind and semi-blind multivariate observations is a recurrent problem. Numerous algorithms have been developed for this problem, which are based on the exact or approximate joint diagonalization of second or higher order cumulant matrices/tensors of multichannel data. While a great body of research has been dedicated to joint diagonalization algorithms, the selection of the diagonalized matrix/tensor set remains highly problem-specific. Herein, various methods for nonstationarity identification are reviewed and a new general frame-work based on hypothesis testing is proposed, which results in a classification/clustering perspective to semi-blind source separation of nonstationary components. The proposed method is applied to noninvasive fetal ECG extraction, as case study.
Mucosal immunization with DTaP confers protection against Bordetella pertussis infection and cough in Sprague-Dawley rats

Pertussis is a respiratory disease caused by the Gram-negative pathogen, Bordetella pertussis. The transition from a whole-cell pertussis vaccine (wP and DTP) to an acellular pertussis vaccine (aP, DTaP, and Tdap) correlates with an increase in pertussis cases, despite widespread vaccine implementation and coverage, and it is now appreciated that the protection provided by aP rapidly wanes. To recapitulate the localized immunity observed from natural infection, mucosal vaccination with aP was explored using the coughing rat model of pertussis. Overall, our goal was to evaluate the route of vaccination in the coughing rat model of pertussis. Immunity induced by both oral gavage and intranasal vaccination of aP in B. pertussis challenged rats over a 9-day infection was compared to intramuscular wP (IM-wP)- and IM-aP-immunized rats that were used as positive controls. Our data demonstrate that mucosal immunization of aP resulted in the production of anti-B. pertussis IgG antibody titers similar to IM-wP- and IM-aP-vaccinated controls postchallenge. IN-aP also induced anti-B. pertussis IgA antibodies in the nasal cavity. Immunization with IM-wP, IM-aP, IN-aP, and OG-aP immunization protected against B. pertussis-induced cough, whereas OG-aP immunization did not protect against respiratory distress. Mucosal immunization by both intranasal and oral gavage administration protected against acute inflammation and decreased bacterial burden in the lung compared to mock-vaccinated challenge rats. The data presented in this study suggest that mucosal vaccination with aP can induce a mucosal immune response and provide protection against B. pertussis challenge. This study highlights the potential benefits and uses of the coughing rat model of pertussis; however, further questions regarding waning immunity still require additional investigation.
The Impact of Payment Frequency on Consumer Spending and Subjective Wealth Perceptions

A growing trend is for consumers to get paid more often, resulting in more frequent, yet smaller paychecks. However, surprisingly little is known about whether and how payment frequency impacts consumer behavior. The current work addresses this gap. An analysis of income and expenditure data of over 30,000 consumers from a financial services provider demonstrates a naturally occurring relationship between higher payment frequencies and increased spending. This effect is replicated across five controlled lab studies, providing evidence for a causal relationship between payment frequency and spending. This effect is explained by changes in consumers’ subjective wealth perceptions. Higher payment frequencies increase consumers’ subjective wealth perceptions, which increase spending. Moreover, the effect of payment frequency is not simply a function of objective wealth differences, as those paid more frequently can feel greater subjective wealth perceptions, even when objective wealth is greater for those paid less frequently. The effect of payment frequency on subjective wealth perceptions is attenuated in contexts in which consumers have less uncertainty in predicting their resource sufficiency throughout a period. This work suggests that consumers’ perceptions and their resulting behaviors are based not just on how much resources they receive, but also on when they receive their resources.
ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot.

One-stage long-tailed recognition methods improve the overall performance in a "seesaw" manner, i.e., either sacrifice the head's accuracy for better tail classification or elevate the head's accuracy even higher but ignore the tail. Existing algorithms bypass such trade-off by a multi-stage training process: pre-training on imbalanced set and fine-tuning on balanced set. Though achieving promising performance, not only are they sensitive to the generalizability of the pre-trained model, but also not easily integrated into other computer vision tasks like detection and segmentation, where pre-training of classifiers solely is not applicable. In this paper, we propose a one-stage long-tailed recognition scheme, ally complementary experts (ACE), where the expert is the most knowledgeable specialist in a sub-set that dominates its training, and is complementary to other experts in the less-seen categories without being disturbed by what it has never seen. We design a distribution-adaptive optimizer to adjust the learning pace of each expert to avoid over-fitting. Without special bells and whistles, the vanilla ACE outperforms the current one-stage SOTA method by 3-10% on CIFAR10-LT, CIFAR100-LT, ImageNet-LT and iNaturalist datasets. It is also shown to be the first one to break the "seesaw" trade-off by improving the accuracy of the majority and minority categories simultaneously in only one stage. Code and trained models are at this https URL.
HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training With Crafted Input Noise

Low-latency deep spiking neural networks (SNNs) have become a promising alternative to conventional artificial neural networks (ANNs) because of their potential for increased energy efficiency on event-driven neuromorphic hardware. Neural networks, including SNNs, however, are subject to various adversarial attacks and must be trained to remain resilient against such attacks for many applications. Nevertheless, due to prohibitively high training costs associated with SNNs, analysis, and optimization of deep SNNs under various adversarial attacks have been largely overlooked. In this paper, we first present a detailed analysis of the inherent robustness of low-latency SNNs against popular gradient-based attacks, namely fast gradient sign method (FGSM) and projected gradient descent (PGD). Motivated by this analysis, to harness the model robustness against these attacks we present an SNN training algorithm that uses crafted input noise and incurs no additional training time. To evaluate the merits of our algorithm, we conducted extensive experiments with variants of VGG and ResNet on both CIFAR-10 and CIFAR-100 datasets. Compared to standard trained direct input SNNs, our trained models yield improved classification accuracy of up to 13.7% and 10.1% on FGSM and PGD attack-generated images, respectively, with negligible loss in clean image accuracy. Our models also outperform inherently robust SNNs trained on rate-coded inputs with improved or similar classification performance on attack-generated images while having up to 25x and 4.6x lower latency and computation energy, respectively.
dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design

Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG'o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor's ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor).
Solar sail with superconducting circular current-carrying wire

Abstract A solar sail presents a large sheet of low areal density membrane and is an elegant propellant-less propulsion system for future exploration of the Solar System and beyond. To date, the study of sail membrane deployment strategies has attracted considerable attention. In this work we present a novel means for deploying and stretching the circular solar sail. We consider the superconducting current loop attached to the thin membrane and predict that a superconducting current loop can deploy and stretch the circular solar sail membrane. In the framework of a strict mathematical approach based on classical electrodynamics and the theory of elasticity the magnetic field induced by the superconducting current loop and elastic properties of a circular solar sail membrane and wire loop are analyzed. The formulas for the wire and sail membrane stresses and strains caused by the current in the superconducting wire are derived. The obtained analytical expressions can be applied to a wide range of solar sail sizes. Numerical calculations for the sail of radii 5 m to 150 m made of CP1 membrane of thickness of 3.5 μ m attached to Bi - 2212 superconducting wire with the cross-section radii of 0.5 mm to 10 mm are presented. Calculations are performed for the engineering current densities of 100 A/mm2 to 1000 A/mm2. Our calculations demonstrate the feasibility of solar sail deployment for future exploration of deep space by means of the light pressure propellant.
Olfactory landmarks and path integration converge to form a cognitive spatial map.

Summary The convergence of internal path integration and external sensory landmarks generates a cognitive spatial map in the hippocampus. We studied how localized odor cues are recognized as landmarks by recording the activity of neurons in CA1 during a virtual navigation task. We found that odor cues enriched place cell representations, dramatically improving navigation. Presentation of the same odor at different locations generated distinct place cell representations. An odor cue at a proximal location enhanced the local place cell density and also led to the formation of place cells beyond the cue. This resulted in the recognition of a second, more distal odor cue as a distinct landmark, suggesting an iterative mechanism for extending spatial representations into unknown territory. Our results establish that odors can serve as landmarks, motivating a model in which path integration and odor landmarks interact sequentially and iteratively to generate cognitive spatial maps over long distances.
Precision Autism: Genomic Stratification of Disorders Making Up the Broad Spectrum May Demystify Its "Epidemic Rates".

In the last decade, Autism has broadened and often shifted its diagnostics criteria, allowing several neuropsychiatric and neurological disorders of known etiology. This has resulted in a highly heterogeneous spectrum with apparent exponential rates in prevalence. I ask if it is possible to leverage existing genetic information about those disorders making up Autism today and use it to stratify this spectrum. To that end, I combine genes linked to Autism in the SFARI database and genomic information from the DisGeNET portal on 25 diseases, inclusive of non-neurological ones. I use the GTEx data on genes’ expression on 54 human tissues and ask if there are overlapping genes across those associated to these diseases and those from SFARI-Autism. I find a compact set of genes across all brain-disorders which express highly in tissues fundamental for somatic-sensory-motor function, self-regulation, memory, and cognition. Then, I offer a new stratification that provides a distance-based orderly clustering into possible Autism subtypes, amenable to design personalized targeted therapies within the framework of Precision Medicine. I conclude that viewing Autism through this physiological (Precision) lens, rather than viewing it exclusively from a psychological behavioral construct, may make it a more manageable condition and dispel the Autism epidemic myth.
Differentially Private n-gram Extraction

We revisit the problem of $n$-gram extraction in the differential privacy setting. In this problem, given a corpus of private text data, the goal is to release as many $n$-grams as possible while preserving user level privacy. Extracting $n$-grams is a fundamental subroutine in many NLP applications such as sentence completion, response generation for emails etc. The problem also arises in other applications such as sequence mining, and is a generalization of recently studied differentially private set union (DPSU). In this paper, we develop a new differentially private algorithm for this problem which, in our experiments, significantly outperforms the state-of-the-art. Our improvements stem from combining recent advances in DPSU, privacy accounting, and new heuristics for pruning in the tree-based approach initiated by Chen et al. (2012).
CPG-Actor: Reinforcement Learning for Central Pattern Generators

Central Pattern Generators (CPGs) have several properties desirable for locomotion: they generate smooth trajectories, are robust to perturbations and are simple to implement. However, they are notoriously difficult to tune and commonly operate in an open-loop manner. This paper proposes a new methodology that allows tuning CPG controllers through gradient-based optimisation in a Reinforcement Learning (RL) setting. In particular, we show how CPGs can directly be integrated as the Actor in an Actor-Critic formulation. Additionally, we demonstrate how this change permits us to integrate highly non-linear feedback directly from sensory perception to reshape the oscillators’ dynamics. Our results on a locomotion task using a single-leg hopper demonstrate that explicitly using the CPG as the Actor rather than as part of the environment results in a significant increase in the reward gained over time (20\(\times \) more) compared with previous approaches. Finally, we demonstrate how our closed-loop CPG progressively improves the hopping behaviour for longer training epochs relying only on basic reward functions.
SGL: Spectral Graph Learning from Measurements

This work introduces a highly-scalable spectral graph densification framework for learning resistor networks with linear measurements, such as node voltages and currents. We prove that given O(logN) pairs of voltage and current measurements, it is possible to recover ultra-sparse N-node resistor networks which can well preserve the effective resistance distances on the graph. In addition, the learned graphs also preserve the structural (spectral) properties of the original graph, which can potentially be leveraged in many circuit design and optimization tasks. We show that the proposed graph learning approach is equivalent to solving the classical graphical Lasso problems with Laplacian-like precision matrices. Through extensive experiments for a variety of real-world test cases, we show that the proposed approach is highly scalable for learning ultrasparse resistor networks without sacrificing solution quality.
A machine learning model for nowcasting epidemic incidence.

Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data. This process is known in economics as nowcasting. We describe in this paper a simple random forest statistical model for nowcasting the COVID - 19 daily new infection counts based on historic data along with a set of simple covariates, such as the currently reported infection counts, day of the week, and time since first reporting. We apply the model to adjust the daily infection counts in Ohio, and show that the predictions from this simple data-driven method compare favorably both in quality and computational burden to those obtained from the state-of-the-art hierarchical Bayesian model employing a complex statistical algorithm. The interactive notebook for performing nowcasting is available online at https://tinyurl.com/simpleMLnowcasting.