cssi/SciSciGPT-SciSciNet huggingface dataset:
Internet Distance Learning: The Problems, the Pitfalls, and the Future

The Georgia Institute of Technology in Atlanta is in a unique position of having a campus located in the heart of the Lorraine region of France in the city of Metz. Since the number of faculty at GTL is limited, some courses must be offered in non-traditional formats. Due to the problems involved in shipping video tapes across the Atlantic, the Internet was considered as an alternative delivery vehicle. Initially driven by a very real and pressing need to deliver courses in a timely and effective manner across the Atlantic, Internet course delivery is now finding its way into many other programs at Georgia Tech. In this paper, we describe some of our experiences with Internet course development and delivery, and present a set of tools that we have developed for placing courses on the web easily. We will also present some of the problems and limitations with on-line course production and delivery, and speculate on the future of distance education.
Socio-Economics: A Proposal for a New Interdisciplinary Field

Psychologists (working with sociologists and political scientists) seem ready to go beyond piecemeal studies of economic behavior, or studies of economic behavior as merely incidents of general psychological theorems. They can formulate a new paradigm for the study of economic behavior. The unsatisfactory nature of the prevailing neo-classical paradigm has been well established. Economic behavior requires a distinct paradigm because rational choice behavior is a distinct subject. The new paradigm, the base for a new interdisciplinary field, referred to here as socioeconomics, must initially be highly parsimonious, without being excessively "underdetermined." It must answer at least four key questions: How do people actually make choices when they aspire to act rationally? What is the role of emotions and values, next to intra-cognitive factors, in such decisions? What is the role of groups in setting individual choices? And, what is the role of society? The answers may require collaboration of psychologists with other social scientists as well as the institutionalization of a new interstitial discipline.
COVID-19 neuropathology at Columbia University Irving Medical Center/New York Presbyterian Hospital.

Many patients with SARS-CoV-2 infection develop neurological signs and symptoms, though, to date, little evidence exists that primary infection of the brain is a significant contributing factor. We present the clinical, neuropathological, and molecular findings of 41 consecutive patients with SARS-CoV-2 infections who died and underwent autopsy in our medical center. The mean age was 74 years (38-97 years), 27 patients (66%) were male and 34 (83%) were of Hispanic/Latinx ethnicity. Twenty-four patients (59%) were admitted to the intensive care unit (ICU). Hospital-associated complications were common, including 8 (20%) with deep vein thrombosis/pulmonary embolism (DVT/PE), 7 (17%) patients with acute kidney injury requiring dialysis, and 10 (24%) with positive blood cultures during admission. Eight (20%) patients died within 24 hours of hospital admission, while 11 (27%) died more than 4 weeks after hospital admission. Neuropathological examination of 20-30 areas from each brain revealed hypoxic/ischemic changes in all brains, both global and focal; large and small infarcts, many of which appeared hemorrhagic; and microglial activation with microglial nodules accompanied by neuronophagia, most prominently in the brainstem. We observed sparse T lymphocyte accumulation in either perivascular regions or in the brain parenchyma. Many brains contained atherosclerosis of large arteries and arteriolosclerosis, though none had evidence of vasculitis. Eighteen (44%) contained pathologies of neurodegenerative diseases, not unexpected given the age range of our patients. We examined multiple fresh frozen and fixed tissues from 28 brains for the presence of viral RNA and protein, using quantitative reverse-transcriptase PCR (qRT-PCR), RNAscope, and immunocytochemistry with primers, probes, and antibodies directed against the spike and nucleocapsid regions. qRT-PCR revealed low to very low, but detectable, viral RNA levels in the majority of brains, although they were far lower than those in nasal epithelia. RNAscope and immunocytochemistry failed to detect viral RNA or protein in brains. Our findings indicate that the levels of detectable virus in COVID-19 brains are very low and do not correlate with the histopathological alterations. These findings suggest that microglial activation, microglial nodules and neuronophagia, observed in the majority of brains, do not result from direct viral infection of brain parenchyma, but rather likely from systemic inflammation, perhaps with synergistic contribution from hypoxia/ischemia. Further studies are needed to define whether these pathologies, if present in patients who survive COVID-19, might contribute to chronic neurological problems.
Software professionals, malpractice law, and codes of ethics

We all know what a professional is—or do we? For years, ACM has proclaimed that its members are part of a computing profession. But is it really a profession? Many people describe themselves as “professionals” in the colloquial sense of being paid to perform some specialized skill. Yet, only a few occupations are regarded as professions in the legal sense. Courts do not consider athletes or chefs to be professionals the way doctors and lawyers are. Likewise, courts have consistently excluded software developers from that select group. To understand why U.S. law does not recognize computing as a profession—and whether that classification could be changed—calls for a fresh look at the law of professions. Why does the law distinguish professionals from nonprofessionals such as mechanics or pilots? What would happen if courts treated software developers like doctors or lawyers? What are professionals’ legal duties of care and how do they differ from ethical codes of conduct? Can one bootstrap the other? Much of the computing community has assumed that a more robust commitment to ethics is a prerequisite for legal recognition as a profession. That assumption is exactly backward. Professional malpractice law is needed to catalyze a robust code of ethics. The lesson is this: the best way for ACM’s Code of Ethics to make a meaningful difference in changing software development practices is for courts to recognize software as a profession.
Meta Learning in the Continuous Time Limit

In this paper, we establish the ordinary differential equation (ODE) that underlies the training dynamics of Model-Agnostic Meta-Learning (MAML). Our continuous-time limit view of the process eliminates the influence of the manually chosen step size of gradient descent and includes the existing gradient descent training algorithm as a special case that results from a specific discretization. We show that the MAML ODE enjoys a linear convergence rate to an approximate stationary point of the MAML loss function for strongly convex task losses, even when the corresponding MAML loss is non-convex. Moreover, through the analysis of the MAML ODE, we propose a new BI-MAML training algorithm that significantly reduces the computational burden associated with existing MAML training methods. To complement our theoretical findings, we perform empirical experiments to showcase the superiority of our proposed methods with respect to the existing work.
Quadratic differentials and circle patterns on complex projective tori

Given a triangulation of a closed surface, we consider a cross-ratio system that assigns a complex number to every edge satisfying certain polynomial equations per vertex. Every cross-ratio system induces a complex projective structure together with a circle pattern. In particular, there is an associated conformal structure. We show that for any triangulated torus, the projection from the space of cross-ratio systems with prescribed Delaunay angles to the Teichmuller space of the closed torus is a covering map with at most one branch point. Our approach is based on a notion of discrete holomorphic quadratic differentials.
Machine Learning–enabled Scalable Performance Prediction of Scientific Codes

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.
On the escape rate of geodesic loops in an open manifold with nonnegative Ricci curvature

A consequence of the Cheeger-Gromoll splitting theorem states that for any open manifold $(M,x)$ of nonnegative Ricci curvature, if all the minimal geodesic loops at $x$ that represent elements of $\pi_1(M,x)$ are contained in a bounded ball, then $\pi_1(M,x)$ is virtually abelian. We generalize the above result: if these minimal representing geodesic loops of $\pi_1(M,x)$ escape from any bounded metric balls at a sublinear rate with respect to their lengths, then $\pi_1(M,x)$ is virtually abelian.
Synthetic control method with convex hull restrictions: a Bayesian maximum a posteriori approach

Synthetic control methods have gained popularity among causal studies with observational data, particularly when estimating the impacts of the interventions that are implemented to a small number of large units. Implementing the synthetic control methods faces two major challenges: a) estimating weights for each control unit to create a synthetic control and b) providing statistical inferences. To overcome these challenges, we propose a Bayesian framework that implements the synthetic control method with the parallelly shiftable convex hull and provides a useful Bayesian inference, which is drawn from the duality between a penalized least squares method and a Bayesian Maximum A Posteriori (MAP) approach. Simulation results indicate that the proposed method leads to smaller biases compared to alternatives. We apply our Bayesian method to the real data example of Abadie and Gardeazabal (2003) and find that the treatment effects are statistically significant during the subset of the post-treatment period.
A Case Study of Onboarding in Software Teams: Tasks and Strategies

Developers frequently move into new teams or environments across software companies. Their onboarding experience is correlated with productivity, job satisfaction, and other short-term and long-term outcomes. The majority of the onboarding process comprises engineering tasks such as fixing bugs or implementing small features. Nevertheless, we do not have a systematic view of how tasks influence onboarding. In this paper, we present a case study of Microsoft, where we interviewed 32 developers moving into a new team and 15 engineering managers onboarding a new developer into their team – to understand and characterize developers' onboarding experience and expectations in relation to the tasks performed by them while onboarding. We present how tasks interact with new developers through three representative themes: learning, confidence building, and socialization. We also discuss three onboarding strategies as inferred from the interviews that managers commonly use unknowingly, and discuss their pros and cons and offer situational recommendations. Furthermore, we triangulate our interview findings with a developer survey (N = 189) and a manager survey (N = 37) and find that survey results suggest that our findings are representative and our recommendations are actionable. Practitioners could use our findings to improve their onboarding processes, while researchers could find new research directions from this study to advance the understanding of developer onboarding. Our research instruments and anonymous data are available at https://zenodo.org/record/4455937#.YCOQCs 0lFd.
Post-translational flavinylation is associated with diverse extracytosolic redox functionalities throughout bacterial life

Disparate redox activities that take place beyond the bounds of the prokaryotic cell cytosol must connect to membrane or cytosolic electron pools. Proteins post-translationally flavinylated by the enzyme ApbE mediate electron transfer in several characterized extracytosolic redox systems but the breadth of functions of this modification remains unknown. Here, we present a comprehensive bioinformatic analysis of 31,910 prokaryotic genomes that provides evidence of extracytosolic ApbEs within ~50% of bacteria and the involvement of flavinylation in numerous uncharacterized biochemical processes. By mining flavinylation-associated gene clusters, we identify five protein classes responsible for transmembrane electron transfer and two domains of unknown function (DUF2271 and DUF3570) that are flavinylated by ApbE. We observe flavinylation/iron transporter gene colocalization patterns that implicate functions in iron reduction and assimilation. We find associations with characterized and uncharacterized respiratory oxidoreductases that highlight roles of flavinylation in respiratory electron transport chains. Finally, we identify interspecies gene cluster variability consistent with flavinylation/cytochrome functional redundancies and discover a class of 'multi-flavinylated proteins' that may resemble multi-heme cytochromes in facilitating longer distance electron transfer. These findings provide mechanistic insight into an important facet of bacterial physiology and establish flavinylation as a functionally diverse mediator of extracytosolic electron transfer.
Role of meteorological factors in the transmission of SARS-CoV-2 in the United States.

Improved understanding of the effects of meteorological conditions on the transmission of SARS-CoV-2, the causative agent for COVID-19 disease, is needed. Here, we estimate the relationship between air temperature, specific humidity, and ultraviolet radiation and SARS-CoV-2 transmission in 2669 U.S. counties with abundant reported cases from March 15 to December 31, 2020. Specifically, we quantify the associations of daily mean temperature, specific humidity, and ultraviolet radiation with daily estimates of the SARS-CoV-2 reproduction number (Rt) and calculate the fraction of Rt attributable to these meteorological conditions. Lower air temperature (within the 20-40 °C range), lower specific humidity, and lower ultraviolet radiation were significantly associated with increased Rt. The fraction of Rt attributable to temperature, specific humidity, and ultraviolet radiation were 3.73% (95% empirical confidence interval [eCI]: 3.66-3.76%), 9.35% (95% eCI: 9.27-9.39%), and 4.44% (95% eCI: 4.38-4.47%), respectively. In total, 17.5% of Rt was attributable to meteorological factors. The fractions attributable to meteorological factors generally were higher in northern counties than in southern counties. Our findings indicate that cold and dry weather and low levels of ultraviolet radiation are moderately associated with increased SARS-CoV-2 transmissibility, with humidity playing the largest role.
On the Genus and Area of Constant Mean Curvature Surfaces with Bounded Index

Using the local picture of the degeneration of sequences of minimal surfaces developed by Chodosh et al. (Invent Math 209(3):617–664, [1]) we show that in any closed Riemannian 3-manifold (M, g), the genus of an embedded CMC surface can be bounded only in terms of its index and area, independently of the value of its mean curvature. We also show that if M has finite fundamental group, the genus and area of any non-minimal embedded CMC surface can be bounded in term of its index and a lower bound for its mean curvature.
Preliminary Exploration of Adaptive State Predictor Based Human Operator Modeling

Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to quantify the effects of changing aircraft dynamics on an operator s ability to track a signal in order to eventually model a pilot adapting to changing aircraft dynamics. A gradient descent estimator and a least squares estimator with exponential forgetting used these data to predict pilot stick input. The results indicate that individual pilot characteristics and vehicle dynamics did not affect the accuracy of either estimator method to estimate pilot stick input. These methods also were able to predict pilot stick input during changing aircraft dynamics and they may have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot.
Measuring the 'I don't know' Problem through the Lens of Gricean Quantity.

We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice’s Maxims of Conversation (1975). Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the ‘I don’t know’ problem, in which a dialog system produces generic responses. The linguistically motivated RUQ diagnostic compares the model score of a generic response to that of the reference response. We find that for reasonable baseline models, ‘I don’t know’ is preferred over the reference the majority of the time, but this can be reduced to less than 5% with hyperparameter tuning. RUQ allows for the direct analysis of the ‘I don’t know’ problem, which has been addressed but not analyzed by prior work.
Creation and validation of 3D-printed head molds for stereotaxic injections of neonatal mouse brains

Abstract Background An increasing number of rodent model systems use injection of DNA or viral constructs in the neonatal brain. However, approaches for reliable positioning and stereotaxic injection at this developmental stage are limited, typically relying on handheld positioning or molds that must be re-aligned for use in a given laboratory. New method A complete protocol and open-source software pipeline for generating 3D-printed head molds derived from a CT scan of a neonatal mouse head cast, together with a universal adapter that can be placed on a standard stereotaxic stage. Results A series of test injections with adenovirus encoding red fluorescent protein, or Fluorogold, were conducted using original clay molds and newly generated 3D printed molds. Several metrics were used to compare spread and localization of targeted injections. Comparison with existing methods The new method of head mold generation gave comparable results to the field standard, but also allowed the rapid generation of additional copies of each head mold with standardized positioning of the head each time. Conclusions This 3D printing pipeline can be used to efficiently develop a series of head molds with standardized injection coordinates across multiple laboratories. More broadly, this pipeline can easily be adapted to other perinatal ages or species.
Provable Lipschitz Certification for Generative Models

We present a scalable technique for upper bounding the Lipschitz constant of generative models. We relate this quantity to the maximal norm over the set of attainable vector-Jacobian products of a given generative model. We approximate this set by layerwise convex approximations using zonotopes. Our approach generalizes and improves upon prior work using zonotope transformers and we extend to Lipschitz estimation of neural networks with large output dimension. This provides efficient and tight bounds on small networks and can scale to generative models on VAE and DCGAN architectures.
Detection of Morphed Face Images Using Discriminative Wavelet Sub-bands

This work investigates the well-known problem of morphing attacks, which has drawn considerable attention in the biometrics community. Morphed images have exposed face recognition systems’ susceptibility to false acceptance, resulting in dire consequences, especially for national security applications. To detect morphing attacks, we propose a method which is based on a discriminative 2D Discrete Wavelet Transform (2D-DWT). A discriminative wavelet sub-band can highlight inconsistencies between a real and a morphed image. We observe that there is a salient discrepancy between the entropy of a given subband in a bona fide image, and the same sub-band’s entropy in a morphed sample. Considering this dissimilarity between these two entropy values, we find the Kullback-Leibler divergence between the two distributions, namely the entropy of the bona fide and the corresponding morphed images. The most discriminative wavelet sub-bands are those with the highest corresponding KL-divergence values. Accordingly, 22 sub-bands are selected as the most discriminative ones in terms of morph detection. We show that a Deep Neural Network (DNN) trained on the 22 discriminative sub-bands can detect morphed samples precisely. Most importantly, the effectiveness of our algorithm is validated through experiments on three datasets: VISAPP17, LMA, and MorGAN. We also performed an ablation study on the sub-band selection.
Project-Level Encoding for Neural Source Code Summarization of Subroutines

Source code summarization of a subroutine is the task of writing a short, natural language description of that subroutine. The description usually serves in documentation aimed at programmers, where even brief phrase (e.g. "compresses data to a zip file") can help readers rapidly comprehend what a subroutine does without resorting to reading the code itself. Techniques based on neural networks (and encoder-decoder model designs in particular) have established themselves as the state-of-the-art. Yet a problem widely recognized with these models is that they assume the information needed to create a summary is present within the code being summarized itself – an assumption which is at odds with program comprehension literature. Thus a current research frontier lies in the question of encoding source code context into neural models of summarization. In this paper, we present a project-level encoder to improve models of code summarization. By project-level, we mean that we create a vectorized representation of selected code files in a software project, and use that representation to augment the encoder of state-of-the-art neural code summarization techniques. We demonstrate how our encoder improves several existing models, and provide guidelines for maximizing improvement while controlling time and resource costs in model size.
A New Perspective on Learning Context-Specific Independence.

Local structure such as context-specific independence (CSI) has received much attention in the probabilistic graphical model (PGM) literature, as it facilitates the modeling of large complex systems, as well as for reasoning with them. In this paper, we provide a new perspective on how to learn CSIs from data. We propose to first learn a functional and parameterized representation of a conditional probability table (CPT), such as a neural network. Next, we quantize this continuous function, into an arithmetic circuit representation that facilitates efficient inference. In the first step, we can leverage the many powerful tools that have been developed in the machine learning literature. In the second step, we exploit more recently-developed analytic tools from explainable AI, for the purposes of learning CSIs. Finally, we contrast our approach, empirically and conceptually, with more traditional variable-splitting approaches, that search for CSIs more explicitly.
A unified convergence rate analysis of the accelerated smoothed gap reduction algorithm

In this paper, we develop a unified convergence analysis framework for the Accelerated Smoothed GAp ReDuction algorithm (ASGARD) introduced in Tran-Dinh et al. (SIAM J Optim 28(1):96–134, 2018). Unlike Tran-Dinh et al. (SIAM J Optim 28(1):96–134, 2018), the new analysis covers three settings in a single algorithm: general convexity, strong convexity, and strong convexity and smoothness. Moreover, we establish the convergence guarantees on three criteria: (i) gap function, (ii) primal objective residual, and (iii) dual objective residual. Our convergence rates are optimal (up to a constant factor) in all cases. While the convergence rate on the primal objective residual for the general convex case has been established in Tran-Dinh et al. (SIAM J Optim 28(1):96–134, 2018), we prove additional convergence rates on the gap function and the dual objective residual. The analysis for the last two cases is completely new. Our results provide a complete picture on the convergence guarantees of ASGARD. Finally, we present four different numerical experiments on a representative optimization model to verify our algorithm and compare it with the well-known Nesterov’s smoothing algorithm.
Measurement and Analysis of Terminal Shock Oscillation and Buffet Forcing Functions on a Launch Vehicle Payload Fairing

The buffet loads on a launch vehicle payload shroud can be impacted by the unsteadiness associated with a terminal shock at high subsonic speeds. At these conditions, flow accelerates to supersonic speeds on the nose of the payload fairing and is terminated by a normal shock on the cylindrical section downstream of the nose cone/cylinder shoulder. The location of the terminal shock and associated separated boundary layer is affected by the freestream Mach number, Reynolds number, and the pitch/yaw of the launch vehicle. Furthermore, even when the freestream conditions and vehicle attitude are constant, this terminal shock oscillates on the surface of the vehicle. The time-varying surface pressure associated with the terminal shock results in unsteady aerodynamic loads that may interact with vehicle structural dynamic modes and the guidance and control of the vehicle. Buffet testing of a 3-percent scale rigid buffet model of a launch vehicle cargo configuration with a tangent-ogive payload shroud was conducted in 2012 and in 2016. Initial buffet forcing functions (BFFs) utilized a coarse pressure sensor distribution on the vehicle surface in which a single longitudinal station with eight sensors observed the terminal shock environment at Mach 0.90. An examination of these circumferential pressures reveal large impulse-like pressure fluctuations and an asymmetry in pressure when the vehicle is at a nonzeroangle of attack that result in high BFFs. Revisions to the shock integration region were made based on computational fluid dynamics and shadowgraph video of shock motion to better represent the BFFs and reduce the high loads resulting from this environment. To more clearly understand this terminal shock environment, a second wind tunnel test was conducted with a dense distribution of 256 sensors at the terminal shock location. These sensor arrays presents a unique opportunity to observe the unsteady terminal shock environment and to characterize the impact of various integration schemes on the BFFs. This paper presents a summary of the development of BFFs for this terminal shock and a detailed analyses of shock region pressure coefficients, coherence, BFFs, shock location time histories, and power spectral density to help guide development of BFFs for other launch vehicle test and analysis programs.
Differential equation based method for accurate approximations in optimization

The key to this new method is to interpret sensitivity equations as differential equations that may be solved explicitly for closed-form approximations. Approximations were developed for vibration frequencies, mode shapes, and static displacements
Refraction of space-time wave packets: I. Theoretical principles

Space-time (ST) wave packets are pulsed optical beams endowed with precise spatio-temporal structure by virtue of which they exhibit unique and useful characteristics such as propagation invariance and tunable group velocity. We study in detail here, and in two accompanying papers, the refraction of ST wave packets at planar interfaces between non-dispersive, homogeneous, and isotropic dielectrics. We formulate a law of refraction that determines the change in the ST wave-packet group velocity across such an interface as a consequence of a newly identified optical refractive invariant that we call the “spectral curvature”. Because the spectral curvature vanishes in conventional optical fields where the spatial and temporal degrees of freedom are separable, these phenomena have not been observed to date. We derive the laws of refraction for baseband, X wave, and sideband ST wave packets that reveal fascinating refractive phenomena, especially for the former class of wave packets. We predict theoretically, and confirm experimentally in the accompanying papers, refractive phenomena such as group-velocity invariance (ST wave packets whose group velocity does not change across the interface), anomalous refraction (group-velocity increase in higher-index media), group-velocity inversion (change in the sign of the group velocity upon refraction but not its magnitude), and the dependence of the group velocity of the refracted ST wave packet on the angle of incidence.
Entanglement-enhanced estimation of a parameter embedded in multiple phases

Quantum-enhanced sensing promises to improve the performance of sensing tasks using nonclassical probes and measurements that require far fewer scene-modulated photons than the best classical schemes, thereby granting previously inaccessible information about a wide range of physical systems. We propose a generalized distributed sensing framework that uses an entangled quantum probe to estimate a scene-parameter encoded within an array of phases, with a functional dependence on that parameter determined by the physics of the actual system. The receiver uses a laser light source enhanced by quantum-entangled multipartite squeezed-vacuum light to probe the phases and thereby estimate the desired scene-parameter. The entanglement suppresses the collective quantum vacuum noise across the phase array. We report simple analytical expressions for the Cram\'er-Rao bound that depend only on the optical probes and the physical model of the measured system, and we show that our structured receiver asymptotically saturates the quantum Cram\'er-Rao bound in the lossless case. Our approach enables Heisenberg limited precision in estimating a scene-parameter with respect to total probe energy, as well as with respect to the number of modulated phases. Furthermore, we study the impact of uniform loss in our system and examine the behavior of both the quantum and the classical Cram\'er-Rao bounds. We apply our framework to examples as diverse as radio-frequency phased-array directional radar, beam-displacement tracking for atomic-force microscopy, and fiber-based temperature gradiometry.
Discovery and engineering of low work function perovskite materials

Materials with low work functions are critical for an array of applications requiring the facile removal or efficient transport of electrons through a device. Perovskite oxides are a promising class of materials for finding low work functions, and here we target applications in thermionic and field electron emission. Perovskites have highly malleable compositions which enable tunable work function values over a wide range, robust stability at high temperatures, and high electronic conductivities. In this work, we screened over 2900 perovskite oxides in search of stable, conductive, low-work-function materials using Density Functional Theory (DFT) methods. Our work provides insight into the materials chemistry governing the work function value of a perovskite, where materials with barely filled d bands possess the lowest work functions. Our screening has resulted in a total of seven promising compounds, such as BaMoO3 and SrNb0.75Co0.25O3 with work functions of 1.1 eV and 1.5 eV, respectively. These promising materials and others presented in this study may find use as low work function electron emitters in high power vacuum electronic and thermionic energy conversion devices. Moreover, the database of calculated work functions and materials chemistry trends governing the value of the work function may aid in the engineering of perovskite heterojunction devices.
Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection

Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods such as LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: (a) disease-to-normal sample classification; (b) multiclass classification of different disease samples; and (c) disease subtypes detection. Considering the classification of ROC-AUC, false-positive, and false-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.
Invertibility of multi-energy X-ray transform.

PURPOSE The goal is to provide a sufficient condition for the invertibility of a multi-energy (ME) X-ray transform. The energy-dependent X-ray attenuation profiles can be represented by a set of coefficients using the Alvarez-Macovski (AM) method. An ME X-ray transform is a mapping from N AM coefficients to N noise-free energy-weighted measurements, where N≥2 . METHODS We apply a general invertibility theorem to prove the equivalence of global and local invertibility for an ME X-ray transform. We explore the global invertibility through testing whether the Jacobian of the mapping J(A) has zero values over the support of the mapping. The Jacobian of an arbitrary ME X-ray transform is an integration over all spectral measurements. A sufficient condition for J(A)≠0 for all A is that the integrand of J(A) is ≥0 (or ≤0 ) everywhere. Note that the trivial case of the integrand equals 0 everywhere is ignored. Using symmetry, we simplified the integrand of the Jacobian to three factors that are determined by the total attenuation, the basis functions, and the energy-weighting functions, respectively. The factor related to the total attenuation is always positive; hence, the invertibility of the X-ray transform can be determined by testing the signs of the other two factors. Furthermore, we use the Cramer-Rao lower bound (CRLB) to characterize the noise-induced estimation uncertainty and provide a maximum-likelihood (ML) estimator. RESULTS The factor related to the basis functions is always negative when the photoelectric/Compton/Rayleigh basis functions are used and K-edge materials are not considered. The sign of the energy-weighting factor depends on the system source spectra and the detector response functions. For four special types of X-ray detectors, the sign of this factor stays the same over the integration range. Therefore, when these four types of detectors are used for imaging non-K-edge materials, the ME X-ray transform is globally invertible. The same framework can be used to study an arbitrary ME X-ray imaging system, for example, when K-edge materials are present. Furthermore, the ML estimator we presented is an unbiased, efficient estimator and can be used for a wide range of scenes. CONCLUSIONS We have provided a framework to study the invertibility of an arbitrary ME X-ray transform and proved the global invertibility for four types of systems.
Counting to Explore and Generalize in Text-based Games

We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments. We show promising results on a set of generated text-based games of varying difficulty where the goal is to collect a coin located at the end of a chain of rooms. In contrast to previous text-based RL approaches, we observe that our agent learns policies that generalize to unseen games of greater difficulty.
The Critical Role of Charge Balance on the Memory Characteristics of Ferroelectric Field-Effect Transistors

Ferroelectric field-effect transistors (Fe-FETs) with ferroelectric hafnium oxide (FE HfO2) as the gate insulator are being extensively explored as a promising device candidate for non-volatile memory application. FE HfO2 exhibits long retention over ten years, high endurance over 1012 cycles, high speed with subnanosecond polarization switching, and high remnant polarization of 10– $30~\mu \text{C}$ /cm2. However, the performance of Fe-FETs is known to be much worse than FE HfO2 capacitors, which is not completely understood. In this work, we developed a comprehensive Fe-FET model based on a charge balance framework. The role of charge balance and the impact of leakage-assist-switching mechanism on the memory characteristics of Fe-FETs with Metal/Ferroelectric/Dielectric/Semiconductor (M/FE/DE/S) gate-stack is studied. It is found that the ferroelectric/dielectric (FE/DE) interface and DE layer instead of FE layer is critical to determine the memory characteristics of Fe-FETs, and experimental Fe-FETs can be well explained by this model, where the discrepancy between FE capacitors and Fe-FETs are successfully understood.
Tangled Physics: Knots as a challenge for physical scene understanding

Author(s): Croom, Sholei; Firestone, Chaz | Abstract: Humans have a remarkable capacity to make intuitive predictions about physical scenes. Recent studies suggest that this capacity recruits a general-purpose “physics engine” that reliably simulates how scenes will unfold. Here, we complicate this picture by introducing knots to the study of intuitive physics. Three experiments reveal that even basic judgments about knots strain human physical reasoning. Experiment 1-2 presented photographs of simple knots and asked participants to judge each knot’s relative strength. Strikingly, observers reliably ranked weaker knots as strong and stronger knots as weak. Experiment 3 presented photographs of tangled strings and asked participants whether the tangle forms a knot when pulled. When shown a tangle that would not form a knot when pulled taut, subjects were at or near chance discriminating knots from non-knots. These failures challenge the domain-generality of physical reasoning mechanisms, and perhaps suggest that soft-body phenomena recruit different cognitive processes than rigid-body physics.
Tools for efficient analysis of neurons in a 3D reference atlas of whole mouse spinal cord.

Summary To fill the prevailing gap in methodology for whole spinal cord (SC) analysis, we have (1) designed scaffolds (SpineRacks) that facilitate efficient and ordered cryo-sectioning of the entire SC in a single block, (2) constructed a 3D reference atlas of adult mouse SC, and (3) developed software (SpinalJ) to register images of sections and for standardized analysis of cells and projections in atlas space. We have verified mapping accuracies for known neurons and demonstrated the usefulness of this platform to reveal unknown neuronal distributions. Together, these tools provide high-throughput analyses of whole mouse SC and enable direct comparison of 3D spatial information between animals and studies.
Isoform Transcriptome of Developing Human Brain Provides New Insights into Autism Risk Variants

Alternative splicing plays important role in brain development, however its global contribution to human neurodevelopmental diseases (NDD) has not been fully investigated. Here, we examined the relationships between splicing isoforms expression in the brain and de novo loss-of-function mutations identified in the patients with NDDs. We constructed isoform transcriptome of the developing human brain, and observed differentially expressed isoforms and isoform co-expression modules undetectable by the gene-level analyses. These isoforms were enriched in loss-of-function mutations and microexons, co-expressed with a unique set of partners, and had higher prenatal expression. We experimentally tested the impact of splice site mutations in five NDD risk genes, including SCN2A, DYRK1A and BTRC, and demonstrated exon skipping. Furthermore, our results suggest that the splice site mutation in BTRC reduces translational efficiency, likely impacting Wnt signaling through impaired degradation of β-catenin. We propose that functional effect of mutations associated with human diseases should be investigated at isoform- rather than gene-level resolution.
Compositionally-Driven Formation Mechanism of Hierarchical Morphologies in Co-Deposited Immiscible Alloy Thin Films

Co-deposited, immiscible alloy systems form hierarchical microstructures under specific deposition conditions that accentuate the difference in constituent element mobility. The mechanism leading to the formation of these unique hierarchical morphologies during the deposition process is difficult to identify, since the characterization of these microstructures is typically carried out post-deposition. We employ phase-field modeling to study the evolution of microstructures during deposition combined with microscopy characterization of experimentally deposited thin films to reveal the origin of the formation mechanism of hierarchical morphologies in co-deposited, immiscible alloy thin films. Our results trace this back to the significant influence of a local compositional driving force that occurs near the surface of the growing thin film. We show that local variations in the concentration of the vapor phase near the surface, resulting in nuclei (i.e., a cluster of atoms) on the film’s surface with an inhomogeneous composition, can trigger the simultaneous evolution of multiple concentration modulations across multiple length scales, leading to hierarchical morphologies. We show that locally, the concentration must be above a certain threshold value in order to generate distinct hierarchical morphologies in a single domain.
From Coupling to Spectral Independence and Blackbox Comparison with the Down-Up Walk

We show that the existence of a "good" coupling w.r.t. Hamming distance for any local Markov chain on a discrete product space implies rapid mixing of the Glauber dynamics in a blackbox fashion. More specifically, we only require the expected distance between successive iterates under the coupling to be summable, as opposed to being one-step contractive in the worst case. Combined with recent local-to-global arguments [Chen et al., 2021], we establish asymptotically optimal lower bounds on the standard and modified log-Sobolev constants for the Glauber dynamics for sampling from spin systems on bounded-degree graphs when a curvature condition [Ollivier, 2009] is satisfied. To achieve this, we use Stein’s method for Markov chains [Bresler and Nagaraj, 2019; Reinert and Ross, 2019] to show that a "good" coupling for a local Markov chain yields strong bounds on the spectral independence of the distribution in the sense of [Anari et al., 2020]. Our primary application is to sampling proper list-colorings on bounded-degree graphs. In particular, combining the coupling for the flip dynamics given by [Vigoda, 2000; Chen et al., 2019] with our techniques, we show optimal O(nlog n) mixing for the Glauber dynamics for sampling proper list-colorings on any bounded-degree graph with maximum degree Δ whenever the size of the color lists are at least ({11/6 - e}) Δ, where e ≈ 10^{-5} is small constant. While O(n²) mixing was already known before, our approach additionally yields Chernoff-type concentration bounds for Hamming Lipschitz functions in this regime, which was not known before. Our approach is markedly different from prior works establishing spectral independence for spin systems using spatial mixing [Anari et al., 2020; Z. {Chen} et al., 2020; Chen et al., 2021; Feng et al., 2021], which crucially is still open in this regime for proper list-colorings.
A Scavenger Hunt for Service Robots

Creating robots that can perform general-purpose service tasks in a human-populated environment has been a longstanding grand challenge for AI and Robotics research. One particularly valuable skill that is relevant to a wide variety of tasks is the ability to locate and retrieve objects upon request. This paper models this skill as a Scavenger Hunt (SH) game, which we formulate as a variation of the NP-hard stochastic traveling purchaser problem. In this problem, the goal is to find a set of objects as quickly as possible, given probability distributions of where they may be found. We investigate the performance of several solution algorithms for the SH problem, both in simulation and on a real mobile robot. We use Reinforcement Learning (RL) to train an agent to plan a minimal cost path, and show that the RL agent can outperform a range of heuristic algorithms, achieving near optimal performance. In order to stimulate research on this problem, we introduce a publicly available software stack and associated website that enable users to upload scavenger hunts which robots can download, perform, and learn from to continually improve their performance on future hunts.
Generative Design of NU’s Husky Carbon, A Morpho-Functional, Legged Robot

We report the design of a morpho-functional robot called Husky Carbon. Our goal is to integrate two forms of mobility, aerial and quadrupedal legged locomotion, within a single platform. There are prohibitive design restrictions such as tight power budget and payload, which can particularly become important in aerial flights. To address these challenges, we pose a problem called the Mobility Value of Added Mass (MVAM) problem. In the MVAM problem, we attempt to allocate mass in our designs such that the energetic performance is affected the least. To solve the MVAM problem, we adopted a generative design approach using Grasshopper’s evolutionary solver to synthesize a parametric design space for Husky. Then, this space was searched for the morphologies that could yield a minimized Total Cost Of Transport (TCOT) and payload. This approach revealed that a front heavy quadrupedal robot can achieve a lower TCOT while retaining larger margins on allowable added mass to its design. Based on this framework Husky was built and tested as a front heavy robot.