cssi/SciSciGPT-SciSciNet huggingface dataset:
Applicability and Challenges of Deep Reinforcement Learning for Satellite Frequency Plan Design

The study and benchmarking of Deep Reinforcement Learning (DRL) models has become a trend in many industries, including aerospace engineering and communications. Recent studies in these fields propose these kinds of models to address certain complex real-time decision-making problems in which classic approaches do not meet time requirements or fail to obtain optimal solutions. While the good performance of DRL models has been proved for specific use cases or scenarios, most studies do not discuss the compromises and generalizability of such models during real operations. In this paper we explore the tradeoffs of different elements of DRL models and how they might impact the final performance. To that end, we choose the Frequency Plan Design (FPD) problem in the context of multibeam satellite constellations as our use case and propose a DRL model to address it. We identify six different core elements that have a major effect in its performance: the policy, the policy optimizer, the state, action, and reward representations, and the training environment. We analyze different alternatives for each of these elements and characterize their effect. We also use multiple environments to account for different scenarios in which we vary the dimensionality or make the environment non-stationary. Our findings show that DRL is a potential method to address the FPD problem in real operations, especially because of its speed in decision-making. However, no single DRL model is able to outperform the rest in all scenarios, and the best approach for each of the six core elements depends on the features of the operation environment. While we agree on the potential of DRL to solve future complex problems in the aerospace industry, we also reflect on the importance of designing appropriate models and training procedures, understanding the applicability of such models, and reporting the main performance tradeoffs.
On Robust Mean Estimation under Coordinate-level Corruption

We study the problem of robust mean estimation and introduce a novel Hamming distance-based measure of distribution shift for coordinate-level corruptions. We show that this measure yields adversary models that capture more realistic corruptions than those used in prior works, and present an information-theoretic analysis of robust mean estimation in these settings. We show that for structured distributions, methods that leverage the structure yield information theoretically more accurate mean estimation. We also focus on practical algorithms for robust mean estimation and study when data cleaning-inspired approaches that first fix corruptions in the input data and then perform robust mean estimation can match the information theoretic bounds of our analysis. We finally demonstrate experimentally that this two-step approach outperforms structure-agnostic robust estimation and provides accurate mean estimation even for high-magnitude corruption.
Multimodal Emergent Fake News Detection via Meta Neural Process Networks

Fake news travels at unprecedented speeds, reaches global audiences and puts users and communities at great risk via social media platforms. Deep learning based models show good performance when trained on large amounts of labeled data on events of interest, whereas the performance of models tends to degrade on other events due to domain shift. Therefore, significant challenges are posed for existing detection approaches to detect fake news on emergent events, where large-scale labeled datasets are difficult to obtain. Moreover, adding the knowledge from newly emergent events requires to build a new model from scratch or continue to fine-tune the model, which can be challenging, expensive, and unrealistic for real-world settings. In order to address those challenges, we propose an end-to-end fake news detection framework named MetaFEND, which is able to learn quickly to detect fake news on emergent events with a few verified posts. Specifically, the proposed model integrates meta-learning and neural process methods together to enjoy the benefits of these approaches. In particular, a label embedding module and a hard attention mechanism are proposed to enhance the effectiveness by handling categorical information and trimming irrelevant posts. Extensive experiments are conducted on multimedia datasets collected from Twitter and Weibo. The experimental results show our proposed MetaFEND model can detect fake news on never-seen events effectively and outperform the state-of-the-art methods.
On the use of surface porosity to reduce unsteady lift

An innovative application of existing technology is proposed for attenuating the effects of transient phenomena, such as rotor-stator and rotor-strut interactions, linked to noise and fatigue failure in turbomachinery environments. A computational study was designed to assess the potential of passive porosity technology as a mechanism for alleviating interaction effects by reducing the unsteady lift developed on a stator airfoil subject to wake impingement. The study involved a typical high bypass fan Stator airfoil (solid baseline and several porous configurations), immersed in a free field and exposed to the effects of a transversely moving wake. It was found that, for the airfoil under consideration, the magnitude of the unsteady lift could be reduced more than 18% without incurring significant performance losses.
Prefix-Tuning: Optimizing Continuous Prompts for Generation

Fine-tuning is the de facto way of leveraging large pretrained language models for downstream tasks. However, fine-tuning modifies all the language model parameters and therefore necessitates storing a full copy for each task. In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural language generation tasks, which keeps language model parameters frozen and instead optimizes a sequence of continuous task-specific vectors, which we call the prefix. Prefix-tuning draws inspiration from prompting for language models, allowing subsequent tokens to attend to this prefix as if it were “virtual tokens”. We apply prefix-tuning to GPT-2 for table-to-text generation and to BART for summarization. We show that by learning only 0.1% of the parameters, prefix-tuning obtains comparable performance in the full data setting, outperforms fine-tuning in low-data settings, and extrapolates better to examples with topics that are unseen during training.
The Moral Irrelevance of Constitutive Luck

One’s constitution—whether one is generous or miserly, temperate or intemperate, kind or mean, etc.—is beyond one’s control in significant respects. Yet one’s constitution affects how one acts. And how one acts affects one’s moral standing. The counterintuitive inference—the so-called problem of constitutive moral luck—is that one’s moral standing is, to some significant extent, beyond one’s control. This article grants the premises but resists the inference. It argues that one’s constitution should have no net impact on one’s moral standing. While a bad constitution lowers the chance that one will act morally, it offers significant gains to moral standing should that chance materialize. A good constitution increases one’s chance of performing good acts but for correspondingly more modest gains. This effect should smooth out, and possibly eliminate, the expected impact of constitution on moral standing.
Development of Minimum-Correction Wind Tunnels

Flow distortions due to wind tunnel wall interference may be accounted for if the model to tunnel ratio is small, but the theory becomes less reliable as the model becomes larger. This paper presents theoretical analysis and experimental evidence which supports a new concept of wind tunnel. The method employs active control of flow through the walls so that the model is in approximately free air conditions during the test. Practical considerations in the design of such a tunnel are presented. Results indicate that a minimum correction wind tunnel may be achieved with active walls of relatively low porosity.
Maize Inbred Line B96 Is the Source of Large-Effect Loci for Resistance to Generalist but Not Specialist Spider Mites.

Maize (Zea mays subsp. mays) yield loss from arthropod herbivory is substantial. While the basis of resistance to major insect herbivores has been comparatively well-studied in maize, less is known about resistance to spider mite herbivores, which are distantly related to insects and feed by a different mechanism. Two spider mites, the generalist Tetranychus urticae, and the grass-specialist Oligonychus pratensis, are notable pests of maize, especially during drought conditions. We assessed resistance (antibiosis) to both mites of 38 highly diverse maize lines, including several previously reported to be resistant to one or the other mite species. We found that line B96, as well as its derivatives B49 and B75, were highly resistant to T. urticae. In contrast, neither these three lines, nor any others included in our study, were notably resistant to the specialist O. pratensis. Quantitative trait locus (QTL) mapping with replicate populations from crosses of B49, B75, and B96 to susceptible B73 identified a QTL in the same genomic interval on chromosome 6 for T. urticae resistance in each of the three resistant lines, and an additional resistance QTL on chromosome 1 was unique to B96. Single-locus genotyping with a marker coincident with the chromosome 6 QTL in crosses of both B49 and B75 to B73 revealed that the respective QTL was large-effect; it explained ~70% of the variance in resistance, and resistance alleles from B49 and B75 acted recessively as compared to B73. Finally, a genome-wide haplotype analysis using genome sequence data generated for B49, B75, and B96 identified an identical haplotype, likely of initial origin from B96, as the source of T. urticae resistance on chromosome 6 in each of the B49, B75, and B96 lines. Our findings uncover the relationship between intraspecific variation in maize defenses and resistance to its major generalist and specialist spider mite herbivores, and we identified loci for use in breeding programs and for genetic studies of resistance to T. urticae, the most widespread spider mite pest of maize.
A 20-year perspective on the Ottawa Ankle Rules: Are we still on solid footing?

ABSTRACT The Ottawa Ankle Rules, finalized in 1995, are a clinical decision directive for the use of radiographs in patients with traumatic ankle and foot injuries. The researchers who developed the rules defined clinically insignificant ankle fractures as distal fibular or malleolar chip fractures of 3 mm or less. They concluded that at least one-third of all ankle and foot radiographs could be eliminated by using their rules, which have since been adopted as the standard of care in EDs. This article explores whether, after more than 20 years in use, the Ottawa Ankle Rules are still valid, and whether they should continue to be used or be replaced by a superior protocol.
Artificial Illusions: Deepfakes as Speech

Deepfakes, a new type of artificial media created by sophisticated machine learning algorithms, present a fundamental epistemological problem to society: How can we know the truth when seeing and hearing are not believing? This paper discusses how deepfakes fit into the category of illusory speech, what they do in society, and how to deal with them. Illusions present an alternate reality, much like a lie, but they also contain evidence for that reality. Some illusions, like games of tag and magic tricks, are harmless and fun. Others, like counterfeit coins and deepfakes, harm others and are deeply convincing. For example, the most common use for deepfake technology is to produce pornographic videos of women who never consented. After strangers attacked them in this way, women reported feeling violated and living in a state of constant “visceral fear.” Pornographic deepfakes — most often deployed against women — abridge their targets’ sexual agency and privacy, contributing to inequality and enabling intimate partner abuse, workplace sexual harassment, and other discrimination and hate. Deepfakes also pose a threat in politics and society more generally. In addition to allowing malicious actors to produce convincing, illusory disinformation, their increased use may lead to a general inability to discern the truth. In the face of the deep and distressing harms that deepfakers cause to women and the danger that they present to democracy, this paper argues for new civil and criminal penalties for deepfakers as well as new regulations and liabilities for internet platforms that host their work.
Efficient Competitions and Online Learning with Strategic Forecasters

Winner-take-all competitions in forecasting and machine-learning suffer from distorted incentives. [23] identified this problem and proposed ELF, a truthful mechanism to select a winner. We show that, from a pool of n forecasters, ELF requires Θ(nlog n) events or test data points to select a near-optimal forecaster with high probability. We then show that standard online learning algorithms select an e-optimal forecaster using only O(log(n) / e2) events, by way of a strong approximate-truthfulness guarantee. This bound matches the best possible even in the nonstrategic setting. We then apply these mechanisms to obtain the first no-regret guarantee for non-myopic strategic experts.
scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

A system-level understanding of the regulation and coordination mechanisms of gene expression is essential to studying the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell-type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The scLink R package is available at https://github.com/Vivianstats/scLink.
Metamorphic Detection of Repackaged Malware

Machine learning-based malware detection systems are often vulnerable to evasion attacks, in which a malware developer manipulates their malicious software such that it is misclassified as benign. Such software hides some properties of the real class or adopts some properties of a different class by applying small perturbations. A special case of evasive malware hides by repackaging a bonafide benign mobile app to contain malware in addition to the original functionality of the app, thus retaining most of the benign properties of the original app. We present a novel malware detection system based on metamorphic testing principles that can detect such benign-seeming malware apps. We apply metamorphic testing to the feature representation of the mobile app, rather than to the app itself. That is, the source input is the original feature vector for the app and the derived input is that vector with selected features removed. If the app was originally classified benign, and is indeed benign, the output for the source and derived inputs should be the same class, i.e., benign, but if they differ, then the app is exposed as (likely) malware. Malware apps originally classified as malware should retain that classification, since only features prevalent in benign apps are removed. This approach enables the machine learning model to classify repackaged malware with reasonably few false negatives and false positives. Our training pipeline is simpler than many existing ML-based malware detection methods, as the network is trained end-to-end to jointly learn appropriate features and to perform classification. We pre-trained our classifier model on 3 million apps collected from the widely-used AndroZoo dataset.1 We perform an extensive study on other publicly available datasets to show our approach’s effectiveness in detecting repackaged malware with more than 94% accuracy, 0.98 precision, 0.95 recall, and 0.96 F1 score.
A Survey on Security and Privacy Issues in Modern Healthcare Systems: Attacks and Defenses

Recent advancements in computing systems and wireless communications have made healthcare systems more efficient than before. Modern healthcare devices can monitor and manage different health conditions of patients automatically without any manual intervention from medical professionals. Additionally, the use of implantable medical devices, body area networks, and Internet of Things technologies in healthcare systems improve the overall patient monitoring and treatment process. However, these systems are complex in software and hardware, and optimizing between security, privacy, and treatment is crucial for healthcare systems because any security or privacy violation can lead to severe effects on patients’ treatments and overall health conditions. Indeed, the healthcare domain is increasingly facing security challenges and threats due to numerous design flaws and the lack of proper security measures in healthcare devices and applications. In this article, we explore various security and privacy threats to healthcare systems and discuss the consequences of these threats. We present a detailed survey of different potential attacks and discuss their impacts. Furthermore, we review the existing security measures proposed for healthcare systems and discuss their limitations. Finally, we conclude the article with future research directions toward securing healthcare systems against common vulnerabilities.
The lock-on effect in electron-beam-controlled gallium arsenide switches

The authors analyze the lock-on effect, which is the inability of photoconductive or electron-beam-controlled semiconductor switches to recover to their initial hold-off voltages following the application of the laser or electron-beam pulse, if the applied voltage exceeds a certain value. For GaAs this threshold voltage corresponds to average electric fields in the range from 4 to 12 kV/cm. Experimental results on semi-insulating GaAs switches indicate that the corresponding lock-on current after e-beam irradiation is identical with the steady-state dark current. The highly resistive state of the switch before e-beam irradiation is shown to be a transient phase towards the much lower steady-state dark resistance, with a duration which depends on the impurity content of the switch material and the applied voltage. The irradiation of the GaAs samples with electrons or photons causes an acceleration of this temporal evolution; at sufficiently high laser or e-beam intensities, lock-on of the dark current after termination of the driving ionization source is observed. Based on the experimental results, a model is developed which describes the lock-on effect in terms of double injection and carrier trapping in deep intraband levels. The model explains the major characteristics of the lock-up effects and is supported by the qualitative agreement of the calculated current-voltage curves with the experimental data. >
Australian Federalism in James Buchanan’s Early Work on Fiscal Equity

James Buchanan, one of the founders of public choice theory and constitutional economics, began his career studying fiscal federalism in the doctoral program at the University of Chicago. This pape...
If you Cheat, I Cheat: Cheating on a Collaborative Task with a Social Robot

Robots may soon play a role in higher education by augmenting learning environments and managing interactions between instructors and learners. Little, however, is known about how the presence of robots in the learning environment will influence academic integrity. This study therefore investigates if and how college students cheat while engaged in a collaborative sorting task with a robot. We employed a 2x2 factorial design to examine the effects of cheating exposure (exposure to cheating or no exposure) and task clarity (clear or vague rules) on college student cheating behaviors while interacting with a robot. Our study finds that prior exposure to cheating on the task significantly increases the likelihood of cheating. Yet, the tendency to cheat was not impacted by the clarity of the task rules. These results suggest that normative behavior by classmates may strongly influence the decision to cheat while engaged in an instructional experience with a robot.
Chance-constrained controller state and reference governor

Abstract The controller state and reference governor (CSRG) is an add-on scheme for nominal closed-loop systems with dynamic controllers which supervises the controller internal state and the reference input to the closed-loop system to enforce pointwise-in-time constraints. By admitting both controller state and reference modifications, the CSRG can achieve an enlarged constrained domain of attraction compared to conventional reference governor schemes where only reference modification is permitted. This paper studies the CSRG for systems subject to stochastic disturbances and chance constraints. We describe the CSRG algorithm in such a stochastic setting and analyze its theoretical properties, including chance-constraint enforcement, finite-time reference convergence, and closed-loop stability. We also present examples illustrating the application of CSRG to constrained aircraft flight control.
Interaction between decision-making and interoceptive representations of bodily arousal in frontal cortex.

Decision-making and representations of arousal are intimately linked. Behavioral investigations have classically shown that either too little or too much bodily arousal is detrimental to decision-making, indicating that there is an inverted “U” relationship between bodily arousal and performance. How these processes interact at the level of single neurons as well as the neural circuits involved are unclear. Here we recorded neural activity from orbitofrontal cortex (OFC) and dorsal anterior cingulate cortex (dACC) of macaque monkeys while they made reward-guided decisions. Heart rate (HR) was also recorded and used as a proxy for bodily arousal. Recordings were made both before and after subjects received excitotoxic lesions of the bilateral amygdala. In intact monkeys, higher HR facilitated reaction times (RTs). Concurrently, a set of neurons in OFC and dACC selectively encoded trial-by-trial variations in HR independent of reward value. After amygdala lesions, HR increased, and the relationship between HR and RTs was altered. Concurrent with this change, there was an increase in the proportion of dACC neurons encoding HR. Applying a population-coding analysis, we show that after bilateral amygdala lesions, the balance of encoding in dACC is skewed away from signaling either reward value or choice direction toward HR coding around the time that choices are made. Taken together, the present results provide insight into how bodily arousal and decision-making are signaled in frontal cortex.
Product Processing for Tapered Sparse Arrays

The product processor output has recently been introduced as a spatial power spectral density estimate, unifying product arrays such as coprime arrays, nested arrays, and standard uniform line arrays. The expected value and covariance function of this estimate for a white Gaussian process was derived in previous work over these various array configurations. However, this prior work used a uniform taper in all cases. In this paper, we show that when product arrays are windowed with non-uniform tapers, the expected value of the product processor output is the convolution of the true spatial power spectral density with the spatial Fourier transform of the difference coarray. This expected value makes a Fourier transform pair with a spatial autocorrelation estimate obtained by windowing the true autocorrelation function. We also derive the covariance function of the product processor output with non-uniform tapers, and compare these derived statistics for the aforementioned array geometries. Also, in prior work, the moments were provided only for linear arrays; this paper extends the estimation results to multidimensional arrays.
Uniform asymptotic expansions for Lommel, Anger-Weber, and Struve functions

Using a differential equation approach asymptotic expansions are rigorously obtained for Lommel, Weber, Anger-Weber and Struve functions, as well as Neumann polynomials, each of which is a solution of an inhomogeneous Bessel equation. The approximations involve Airy and Scorer functions, and are uniformly valid for large real order $\nu$ and unbounded complex argument $z$. An interesting complication is the identification of the Lommel functions with the new asymptotic solutions, and in order to do so it is necessary to consider certain sectors of the complex plane, as well as introduce new forms of Lommel and Struve functions.
Lower Bounds on Information Requirements for Causal Network Inference

Recovery of the causal structure of dynamic networks from noisy measurements has long been a problem of intense interest across many areas of science and engineering. Many algorithms have been proposed, but there is no work that compares the performance of the algorithms to converse bounds in a non-asymptotic setting. As a step to address this problem, this paper gives lower bounds on the error probability for causal network support recovery in a linear Gaussian setting. The bounds are based on the use of the Bhattacharyya coefficient for binary hypothesis testing problems with mixture probability distributions. Comparison of the bounds and the performance achieved by two representative recovery algorithms are given for sparse random networks based on the Erdős-Renyi model.
Does filling-dependent band renormalization aid pairing in twisted bilayer graphene?

Magic-angle twisted bilayer graphene (MATBG) exhibits a panoply of many-body phenomena that are intimately tied to the appearance of narrow and well-isolated electronic bands. The microscopic ingredients that are responsible for the complex experimental phenomenology include electron–electron (phonon) interactions and nontrivial Bloch wavefunctions associated with the narrow bands. Inspired by recent experiments, we focus on two independent quantities that are considerably modified by Coulomb interaction-driven band renormalization, namely the density of states and the minimal spatial extent associated with the Wannier functions. First, we show that a filling-dependent enhancement of the density of states, caused by band flattening, in combination with phonon-mediated attraction due to electron-phonon umklapp processes, increases the tendency towards superconducting pairing in a range of angles around magic-angle. Second, we demonstrate that the minimal spatial extent associated with the Wannier functions, which contributes towards increasing the superconducting phase stiffness, also develops a nontrivial enhancement due to the interaction-induced renormalization of the Bloch wavefunctions. While our modeling of superconductivity (SC) assumes a weak electron-phonon coupling and does not consider many of the likely relevant correlation effects, it explains simply the experimentally observed robustness of SC in the wide range of angles that occurs in the relevant range of fillings.
What Happens When a Retailer Drops a Product Category? Investigating the Consequences of Ending Tobacco Sales

The paper measures the cross-category spillover effects of a retailer changing its assortment at the extensive margin (by dropping an entire category from its portfolio) on the outcomes for its riv...
Neural cell injury pathology due to high-rate mechanical loading

Abstract Successful detection and prevention of brain injuries relies on the quantitative identification of cellular injury thresholds associated with the underlying pathology. Here, by combining a recently developed inertial microcavitation rheology technique with a 3D in vitro neural tissue model, we quantify and resolve the structural pathology and critical injury strain thresholds of neural cells occurring at high loading rates such as encountered in blast, cavitation or directed energy exposures. We find that neuronal dendritic spines characterized by MAP2 displayed the lowest physical failure strain at 7.3%, whereas microtubules and filamentous actin were able to tolerate appreciably higher strains (14%) prior to injury. Interestingly, while these critical injury thresholds were similar to previous literature values reported for moderate and lower strain rates ( 100 1/s), the pathology of primary injury reported here was distinctly different by being purely physical in nature as compared to biochemical activation during apoptosis or necrosis. Statement of Significance Mitigation and prevention of cellular injury is challenging in part due to the lack of quantitative correlation between mechanical insult and cellular pathology, especially at high deformation rates (>104 s−1) that occur in blast and directed energy related brain injury, or laser and sonic-based medical procedures. By utilizing a recently developed inertial microcavitation rheology technique for generating high-rate deformations in a 3D in vitro neural tissue model, we quantitatively correlate critical stretch, strain and stress-based injury criteria to observed cell pathology. These quantitative experimental measurements provide unprecedented new detail into the cellular pathology of neural tissues affected by high-rate injury including the first quantitative high-rate injury threshold metrics.
Nucleosynthesis signatures of neutrino-driven winds from proto-neutron stars: a perspective from chemical evolution models

We test the hypothesis that the observed first-peak (Sr, Y, Zr) and second-peak (Ba) s-process elemental abundances in low metallicity Milky Way stars ($\text{[Fe/H]} \lesssim -0.5$), and the abundances of the intervening elements Mo and Ru, can be explained by a pervasive r-process contribution that originates in neutrino-driven winds from highly-magnetic and rapidly rotating proto-neutron stars (proto-NSs). To this end, we construct chemical evolution models that incorporate recent calculations of proto-NS yields in addition to contributions from AGB stars, Type Ia supernovae, and two alternative sets of yields for massive star winds and core collapse supernovae. For non-rotating massive star yields from either set, models without proto-NS winds underpredict the observed s-process peak abundances by $0.3$-$1\,\text{dex}$ at low metallicity, and they severely underpredict Mo and Ru at all metallicities. Models that include the additional wind yields predicted for proto-NSs with spin periods $P \sim 2$-$5\,\text{ms}$ fit the observed trends for all these elements well. Alternatively, models that omit proto-NS winds but adopt yields of rapidly rotating massive stars, with $v_{\rm rot}$ between $150$ and $300\,\text{km}\,\text{s}^{-1}$, can explain the observed abundance levels reasonably well for $\text{[Fe/H]}<-2$. These models overpredict [Sr/Fe] and [Mo/Fe] at higher metallicities, but with a tuned dependence of $v_{\rm rot}$ on stellar metallicity they might achieve an acceptable fit at all [Fe/H]. If many proto-NSs are born with strong magnetic fields and short spin periods, then their neutrino-driven winds provide a natural source for Sr, Y, Zr, Mo, Ru, and Ba in low metallicity stellar populations. Spherical winds from unmagnetized proto-NSs, on the other hand, overproduce the observed Sr, Y, and Zr abundances by a large factor.
Learning the Next Best View for 3D Point Clouds via Topological Features

In this paper, we introduce a reinforcement learning approach utilizing a novel topology-based information gain metric for directing the next best view of a noisy 3D sensor. The metric combines the disjoint sections of an observed surface to focus on high-detail features such as holes and concave sections. Experimental results show that our approach can aid in establishing the placement of a robotic sensor to optimize the information provided by its streaming point cloud data. Furthermore, a labeled dataset of 3D objects, a CAD design for a custom robotic manipulator, and software for the transformation, union, and registration of point clouds has been publicly released to the research community.
Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach.

This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree-structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data-driven leaf groups with distinct vectors of class probabilities, and (3) individual-level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.
Hierarchical Bayesian models of reinforcement learning: Introduction and comparison to alternative methods

Abstract Reinforcement learning models have been used extensively to capture learning and decision-making processes in humans and other organisms. One essential goal of these computational models is the generalization to new sets of observations. Extracting parameters that can reliably predict out-of-sample data can be difficult, however. The use of prior distributions to regularize parameter estimates has been shown to help remedy this issue. While previous research has suggested that empirical priors estimated from a separate dataset improve predictive accuracy, this paper outlines an alternate method for the derivation of empirical priors: hierarchical Bayesian modeling. We provide a detailed introduction to this method, and show that using hierarchical models to simultaneously extract and impose empirical priors leads to better out-of-sample prediction while being more data efficient.
Customized Handling of Unintended Interface Operation In Assistive Robots

We present an assistance system that reasons about a human’s intended actions during robot teleoperation in order to provide appropriate modifications on unintended behavior. Existing methods typically treat the human and control interface as a black box and assume the measured user input is noise-free, and use this signal to infer task-level human intent. We recognize that the signal measured through the interface is masked by the physical limitations of the user and the interface they are required to use. With this key insight, we model the human’s physical interaction with a control interface during robot teleoperation, and distinguish between interface-level intended and measured physical actions explicitly. By reasoning over the unobserved intentions using model-based inference techniques, our assistive system provides customized modifications on a user’s issued commands. We validate our algorithm both in simulation and with a 10-person human subject study in which we evaluate the performance of the proposed assistance paradigms. Our results show that the assistance paradigms helped to significantly reduce task completion time, number of mode switches, cognitive workload, and user frustration, and improve overall user satisfaction.
Learning-based synthetic dual energy CT imaging from single energy CT for stopping power ratio calculation in proton radiation therapy.

Objective:Dual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of phot...
Neural responses in retrosplenial cortex associated with environmental alterations.

Summary The retrosplenial cortex (RSC) is an area interconnected with regions of the brain that display spatial correlates. Neurons in connected regions may encode an animal's position in the environment and location or proximity to objects or boundaries. RSC has also been shown to be important for spatial memory, such as tracking distance from and between landmarks, contextual information, and orientation within an environment. For these reasons, it is important to determine how neurons in RSC represent cues such as objects or boundaries and their relationship to the environment. In the current work, we performed electrophysiological recordings in RSC, whereas rats foraged in arenas that could contain an object or in which the environment was altered. We report RSC neurons display changes in mean firing rate responding to alterations of the environment. These alterations include the arena rotating, changing size or shape, or an object being introduced into the arena.
The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention

A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behavior and activities. While these algorithms work well in a controlled environment, naturalistic driving conditions add new challenges such as illumination variations, occlusions, and extreme head poses. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks to effectively monitor driver actions and behaviors. Toward building the required infrastructure, this paper presents the multimodal driver monitoring (MDM) dataset, which was collected with 59 subjects that were recorded performing various tasks. We use the Fi-Cap device that continuously tracks the head movement of the driver using fiducial markers, providing frame-based annotations to train head pose algorithms in naturalistic driving conditions. We ask the driver to look at predetermined gaze locations to obtain accurate correlation between the driver's facial image and visual attention. We also collect data when the driver performs common secondary activities such as navigation using a smart phone and operating the in-car infotainment system. All of the driver's activities are recorded with high definition RGB cameras and a time-of-flight depth camera. We also record the controller area network-bus (CAN-Bus), extracting important information. These high quality recordings serve as the ideal resource to train various efficient algorithms for monitoring the driver, providing further advancements in the field of in-vehicle safety systems.
Deep learning-based fast solver of the shallow water equations

Fast and reliable prediction of river flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for this purpose. However, traditional numerical solvers of the SWEs are computationally expensive and require high-resolution riverbed profile measurement (bathymetry). In this work, we propose a two-stage process in which, first, using the principal component geostatistical approach (PCGA) we estimate the probability density function of the bathymetry from flow velocity measurements, and then use machine learning (ML) algorithms to obtain a fast solver for the SWEs. The fast solver uses realizations from the posterior bathymetry distribution and takes as input the prescribed range of BCs. The first stage allows us to predict flow velocities without direct measurement of the bathymetry. Furthermore, we augment the bathymetry posterior distribution to a more general class of distributions before providing them as inputs to ML algorithm in the second stage. This allows the solver to incorporate future direct bathymetry measurements into the flow velocity prediction for improved accuracy, even if the bathymetry changes over time compared to its original indirect estimation. We propose and benchmark three different solvers, referred to as PCA-DNN (principal component analysis-deep neural network), SE (supervised encoder), and SVE (supervised variational encoder), and validate them on the Savannah river, Augusta, GA. Our results show that the fast solvers are capable of predicting flow velocities for different bathymetry and BCs with good accuracy, at a computational cost that is significantly lower than the cost of solving the full boundary value problem with traditional methods.
Neural Stem Cells in the Adult Brain

It is now widely accepted that new neurons are added continuously to some regions of the adult mammalian brain. More than 30 yr of reports describing neurogenesis in the adult brains of fish, frogs, reptiles, birds, and rodents (1, 2, 3, 4, 5, 6) have recently culminated in studies demonstrating the birth of new central nervous system neurons in both primates (7) and humans (8,9). Hence, the century-old, dogmatic proposition of a fixed, ended, immutable adult brain has been refuted, spurring new investigations into the regenerative capacity of the central nervous system.
The VB5 interface

The VB5 interface is an ATM-based broadband interface at the ‘V’ reference point, the interface between a user access and a service node (SN), also known as the SNI (service node interface) within the ITU-T. The B-ISDN architectural model allows for a number of different user accesses, such as straightforward digital sections and simple multiplexers, but it is anticipated that the most frequent implementation, particularly for mass deployment, will be via a broadband access network (AN) with VB5 interfaces. The VB5 interface is specified in a similar way to its narrowband cousin V5, in two variants – VB5.1, which supports allocation of resources via the management system, and VB5.2, which adds a mechanism for allocating AN resources under the control of the SN, enabling call-by-call concentration in the AN. This paper describes the background to the development of VB5 and explains the two variants VB5.1 and VB5.2.
What's love got to do with it? Parental involvement and spouse choice in urban India

“Arranged" marriages, characterized by strong parental control over mate choice, are the norm in India, although there is a steady transition towards autonomous “love” marriages, especially within the urban middle class. I construct a novel dataset by surveying 6,030 parents and adult children in Mumbai, India, to study selection into arranged marriage and its effects on spouse choice. I consider the choice between an arranged and a love marriage as the outcome of bargaining between parents and children, when agents have different preferences for spouse attributes. I find that stronger financial and kinship ties between parents and sons increase the likelihood of an arranged marriage. Furthermore, when parents are involved in mate choice, sons are significantly less likely to marry college-educated women and women engaged in the labor force, after controlling for individual and family characteristics. I show that these effects are driven, at least in part, by parental preferences and cannot be entirely attributed to correlation between arranged marriages and unobserved characteristics or preferences. These results suggest that lowering the incentive for parental control in mate choice may improve investments in women’s human capital in India.
Theories of Star Formation Confronted by IRAS Data

The importance to star formation theories of IRAS observations of the dust distribution around young stars, the luminosities of protostars and embedded clusters, and the size distribution of dust particles is discussed. The important role of the smallest grains in determining the ionization fraction and magnetic diffusion rate in dense molecular clouds is illustrated by new solutions to the equations of charge equilibrum.
Professional Competencies for Student Affairs Practice

The purpose of this chapter is to explore the integration of the ACPA/NASPA Professional Competency Areas for Student Affairs Practitioners (ACPA/NASPA, 2010) on community college campuses. The competencies provide specific skill sets for a broad range of student affairs practice areas that should be met by professionals throughout their careers. These competencies provide a tool to help assess and support staff development, and create clear performance expectations.
Tutorial III: Evolving neural networks

Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: The state can be disambiguated through recurrency, and novel situations handled through pattern matching. In this tutorial, I will review (1) neuroevolution methods that evolve fixedtopology networks, network topologies, and network construction processes, (2) ways of combining traditional neural network learning algorithms with evolutionary methods, and (3) applications of neuroevolution to creating intelligent agents in games.
Histologic Identification of Prominent Intrapulmonary Anastomotic Vessels in Severe Congenital Diaphragmatic Hernia

Objective To determine whether prominent intrapulmonary anastomotic vessels (IPAVs) or bronchopulmonary “shunt” vessels can be identified in lungs from infants with fatal congenital diaphragmatic hernia (CDH). Study design We performed histology with immunostaining for CD31 (endothelium) and D2-40 (lymphatics), along with high-precision 3-dimensional (3D) reconstruction on lung tissue from 9 patients who died with CDH. Results Each patient with CDH required mechanical ventilation, cardiotonic support, and pulmonary hypertension (PH)-targeted drug therapy. All patients were diagnosed with severe PH by echocardiography, and 5 received extracorporeal membrane oxygenation therapy. Death occurred at a median age of 24 days (range, 10-150 days) from refractory hypoxemia with severe PH, pneumonia, or tension pneumothorax. Histology showed decreased alveolarization with pulmonary vascular disease. In each patient, prominent IPAVs were identified as engorged, thin-walled vessels that connected pulmonary veins with microvessels surrounding pulmonary arteries and airways in lungs ipsilateral and contralateral to the CDH. Prominent anastomoses between pulmonary arteries and bronchial arteries were noted as well. The 3D reconstruction studies demonstrated that IPAVs connect pulmonary vasculature to systemic (bronchial) vessels both at the arterial and venous side. Conclusion Histology and 3D reconstruction identified prominent bronchopulmonary vascular anastamoses in the lungs of infants who died with severe CDH. We speculate that IPAVs connecting pulmonary and bronchial arteries contribute to refractory hypoxemia in severe CDH.