cssi/SciSciGPT-SciSciNet huggingface dataset:
An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis

Alcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex neurobehavioral mechanisms driving AUD. We analyzed causal pathways to AUD severity using Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). We applied exploratory factor analysis to parse the wide HCP phenotypic space (100 measures) into 18 underlying domains, and we assessed functional connectivity within 12 resting-state brain networks. We then employed data-driven CDA to generate a causal model relating phenotypic factors, fMRI network connectivity, and AUD symptom severity, which highlighted a limited set of causes of AUD. The model proposed a hierarchy with causal influence propagating from brain connectivity to cognition (fluid/crystalized cognition, language/math ability, & working memory) to social (agreeableness/social support) to affective/psychiatric function (negative affect, low conscientiousness/attention, externalizing symptoms) and ultimately AUD severity. Our data-driven model confirmed hypothesized influences of cognitive and affective factors on AUD, while underscoring that addiction models need to be expanded to highlight the importance of social factors, amongst others. Rawls and colleagues use an advanced statistical approach to identify causal neurobehavioral mechanisms underlying Alcohol Use Disorder. Their findings support current multifactorial models of addiction, but also highlight the importance of social factors in addiction maintenance.
Effect of Foraging and Nest Defense tradeoffs on the Reproductive Success of Wood Storks (Mycteria americana)

In many species of birds, parental care is provided by both parents to maximize offspring survival, and there may be important trade-offs between maximizing food gathering and nest protection during the nesting period. The role of parental care in determining reproductive success was investigated in Wood Storks (Mycteria americana), and specifically how the trade-off between frequency and duration of foraging trips and nest protection contributed to the nesting outcome. Parental behavior of 85 pairs of Wood Storks was monitored throughout the nesting season in two breeding colonies in Palm Beach County, Florida, USA. Wood Storks gradually increased the frequency, but not the duration, of foraging trips as chicks developed. The ratio of hatchlings to fledglings was positively associated with the frequency of foraging trips during late chick development. Intra-specific aggressions resulting in nest takeovers affected 32% of the nests. Occurrence of nest takeovers were higher for later-breeding pairs, and happened primarily in the first few weeks of incubation, but was not affected by the degree of joint nest attendance of both parents. These results establish a functional link between parental effort and reproductive outcome in Wood Storks, and highlight the importance of frequent foraging trips, but not nest attendance, by parents.
Shape matters: a Brownian microswimmer in a channel

We consider the active Brownian particle model for a two-dimensional microswimmer with fixed speed, whose direction of swimming changes according to a Brownian process. The probability density for the swimmer obeys a Fokker–Planck equation defined on the configuration space, whose structure depends on the swimmer's shape, centre of rotation and domain of swimming. We enforce zero probability flux at the boundaries of configuration space. At first neglecting hydrodynamic interactions, we derive a reduced equation for a swimmer in an infinite channel, in the limit of small rotational diffusivity, and find that the invariant density depends strongly on the swimmer's precise shape and centre of rotation. We also give a formula for the mean reversal time: the expected time taken for a swimmer to completely reverse direction in the channel. Using homogenization theory, we find an expression for the effective longitudinal diffusivity of a swimmer in the channel, and show that it is bounded by the mean reversal time. Finally, we include hydrodynamic interactions with walls, and examine the role of shape.
Estimating incidence of infection from diverse data sources: Zika virus in Puerto Rico, 2016.

Emerging epidemics are challenging to track. Only a subset of cases is recognized and reported, as seen with the Zika virus (ZIKV) epidemic where large proportions of infection were asymptomatic. However, multiple imperfect indicators of infection provide an opportunity to estimate the underlying incidence of infection. We developed a modeling approach that integrates a generic Time-series Susceptible-Infected-Recovered epidemic model with assumptions about reporting biases in a Bayesian framework and applied it to the 2016 Zika epidemic in Puerto Rico using three indicators: suspected arboviral cases, suspected Zika-associated Guillain-Barre Syndrome cases, and blood bank data. Using this combination of surveillance data, we estimated the peak of the epidemic occurred during the week of August 15, 2016 (the 33rd week of year), and 120 to 140 (50% credible interval [CrI], 95% CrI: 97 to 170) weekly infections per 10,000 population occurred at the peak. By the end of 2016, we estimated that approximately 890,000 (95% CrI: 660,000 to 1,100,000) individuals were infected in 2016 (26%, 95% CrI: 19% to 33%, of the population infected). Utilizing multiple indicators offers the opportunity for real-time and retrospective situational awareness to support epidemic preparedness and response.
Borel and Volume Classes for Dense Representations of Discrete Groups

We show that the bounded Borel class of any dense representation $\rho: G\to \PSL_n\bC$ is non-zero in degree three bounded cohomology and has maximal semi-norm, for any discrete group $G$. When $n=2$, the Borel class is equal to the $3$-dimensional hyperbolic volume class. Using tools from the theory of Kleinian groups, we show that the volume class of a dense representation $\rho: G\to \PSL_2\bC$ is uniformly separated in semi-norm from any other representation $\rho': G\to \G$ for which there is a subgroup $H\le G$ on which $\rho$ is still dense but $\rho'$ is discrete or indiscrete but stabilizes a point, line, or plane in $\bH^3\cup \partial \bH^3$. We exhibit a family of dense representations of a non-abelian free group on two letters and a family of discontinuous dense representations of $\PSL_2\bR$, whose volume classes are linearly independent and satisfy some additional properties; the cardinality of these families is that of the continuum. We explain how the strategy employed may be used to produce non-trivial volume classes in higher dimensions, contingent on the existence of a family of hyperbolic manifolds with certain topological and geometric properties.
Characterization and Suppression of Background Light Shifts in an Optical Lattice Clock

Experiments involving optical traps often require careful control of the ac Stark shifts induced by strong confining light fields. By carefully balancing light shifts between two atomic states of interest, optical traps at the magic wavelength have been especially effective at suppressing deleterious effects stemming from such shifts. Highlighting the power of this technique, optical clocks today exploit Lamb-Dicke confinement in magic-wavelength optical traps, in some cases realizing shift cancelation at the ten parts per billion level. Theory and empirical measurements can be used at varying levels of precision to determine the magic wavelength where shift cancelation occurs. However, lasers exhibit background spectra from amplified spontaneous emission or other lasing modes that can easily contaminate measurement of the magic wavelength and its reproducibility in other experiments or conditions. Indeed, residual light shifts from laser background have plagued optical lattice clock measurements for years. In this work, we develop a simple theoretical model allowing prediction of light shifts from measured background spectra. We demonstrate good agreement between this model and measurements of the background light shift from an amplified diode laser in a Yb optical lattice clock. Additionally, we model and experimentally characterize the filtering effect of a volume Bragg grating bandpass filter, demonstrating that application of the filter can reduce background light shifts from amplified spontaneous emission well below the ${10}^{\ensuremath{-}18}$ fractional clock frequency level. This demonstration is corroborated by direct clock comparisons between a filtered amplified diode laser and a filtered titanium:sapphire laser.
Global software engineering in the age of GitHub and zoom

Much has changed since the inaugural ICGSE conference in 2006. Tools have improved, awareness of cultural differences is widespread, and developments such as the foregrounding of open source have all enhanced our ability to work across geographic divides. But the pervasive and profound impact of software in the world -- especially for societal scale systems such as social media -- forces new and deeply challenging responsibilities on both developers and academics. We must find better ways of incorporating ethics into our development practices and pay far more attention to harmful unintended consequences as deployed systems interact with and often disrupt crucial social systems.
Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Heterogeneous systems are becoming increasingly prevalent. In order to exploit the rich compute resources of such systems, robust programming models are needed for application developers to seamlessly migrate legacy code from today’s systems to tomorrow’s. Over the past decade and more, directives have been established as one of the promising paths to tackle programmatic challenges on emerging systems. This work focuses on applying and demonstrating OpenMP offloading directives on five proxy applications. We observe that the performance varies widely from one compiler to the other; a crucial aspect of our work is reporting best practices to application developers who use OpenMP offloading compilers. While some issues can be worked around by the developer, there are other issues that must be reported to the compiler vendors. By restructuring OpenMP offloading directives, we gain an 18x speedup for the su3 proxy application on NERSC’s Cori system when using the Clang compiler, and a 15.7x speedup by switching max reductions to add reductions in the laplace mini-app when using the Cray-llvm compiler on Cori.
Analyzing the Performance of Smart Industry 4.0 Applications on Cloud Computing Systems

Cloud-based Deep Neural Network (DNN) applications that make latency-sensitive inference are becoming an indispensable part of Industry 4.0. Due to the multi-tenancy and resource heterogeneity, both inherent to the cloud computing environments, the inference time of DNN-based applications are stochastic. Such stochasticity, if not captured, can potentially lead to low Quality of Service (QoS) or even a disaster in critical sectors, such as Oil and Gas industry. To make Industry 4.0 robust, solution architects and researchers need to understand the behavior of DNN-based applications and capture the stochasticity exists in their inference times. Accordingly, in this study, we provide a descriptive analysis of the inference time from two perspectives. First, we perform an application-centric analysis and statistically model the execution time of four categorically different DNN applications on both Amazon and Chameleon clouds. Second, we take a resource-centric approach and analyze a rate-based metric in form of Million Instruction Per Second (MIPS) for heterogeneous machines in the cloud. This non-parametric modeling, achieved via Jackknife and Bootstrap re-sampling methods, provides the confidence interval of MIPS for heterogeneous cloud machines. The findings of this research can be helpful for researchers and cloud solution architects to develop solutions that are robust against the stochastic nature of the inference time of DNN applications in the cloud and can offer a higher QoS to their users and avoid unintended outcomes.
Generation of Modular and Measurable Validation Scenarios for Autonomous Vehicles Using Accident Data

Autonomous vehicle (AV) technology is positioned to have a significant impact on various industries. Hence, artificial intelligence powered AVs and modern vehicles with advanced driver-assistance systems have been operated in street networks for real-life testing. As these tests become more frequent, accidents have been inevitable and there have been reported crashes. The data from these accidents are invaluable for generating edge case test scenarios and understanding accident-time behavior. In this paper, we use the existing AV accident data and identify the atomic blocks within each accident, which are modular and measurable scenario units. Our approach formulates each accident scenario using these atomic blocks and defines them in the Measurable Scenario Description Language (M-SDL). This approach produces modular scenario units with coverage analysis, provides a method to assist in the measurable analysis of accident-time AV behavior, identifies edge scenarios using AV assessment metrics.
Nonparametric Iterated-Logarithm Extensions of the Sequential Generalized Likelihood Ratio Test

We develop a nonparametric extension of the sequential generalized likelihood ratio (GLR) test and corresponding time-uniform confidence sequences for the mean of a univariate distribution. By utilizing a geometric interpretation of the GLR statistic, we derive a simple analytic upper bound on the probability that it exceeds any prespecified boundary; these are intractable to approximate via simulations due to infinite horizon of the tests and the composite nonparametric nulls under consideration. Using time-uniform boundary-crossing inequalities, we carry out a unified nonasymptotic analysis of expected sample sizes of one-sided and open-ended tests over nonparametric classes of distributions (including sub-Gaussian, sub-exponential, sub-gamma, and exponential families). Finally, we present a flexible and practical method to construct time-uniform confidence sequences that are easily tunable to be uniformly close to the pointwise Chernoff bound over any target time interval.
Pandemics and firms: Drawing lessons from history

Abstract The COVID-19 pandemic caused an unprecedented and synchronized contraction in the global economy. To assess the likely evolution of firm performance going forward, this paper investigates empirically the impact of past pandemics using firm-level data on more than 537,000 nonfinancial companies from 14 developing countries during the period 1998?2018. The analysis indicates that the prevalence of infectious diseases has an economically and statistically significant negative effect on nonfinancial corporate performance. This adverse impact is particularly pronounced on smaller and younger firms, compared to larger and more established corporations. We also find that a higher number of infectious-disease cases in the population increases the probability of failure among nonfinancial firms, particularly for small and young firms. In the case of COVID-19, the magnitude of these effects will be much greater, given the unprecedented scale of the outbreak and strict policy responses to contain its spread.
Bayesian Massive MIMO Channel Estimation with Parameter Estimation Using Low-Resolution ADCs

In order to reduce hardware complexity and power consumption, massive multiple-input multiple-output (MIMO) systems employ low-resolution analog-to-digital converters (ADCs) to acquire quantized measurements y. This poses new challenges to the channel estimation problem, and the sparse prior on the channel coefficient vector x in the angle domain is often used to compensate for the information lost during quantization. By interpreting the sparse prior from a Bayesian perspective, we can assume x follows certain sparse prior distribution and recover it using approximate message passing (AMP). However, the distribution parameters are unknown in practice and need to be estimated. Due to the increased computational complexity in the quantization noise model, previous works either use an approximated noise model or manually tune the noise distribution parameters. In this paper, we treat both signals and parameters as random variables and recover them jointly within the AMP framework. The proposed approach leads to a much simpler parameter estimation method, allowing us to work with the quantization noise model directly. Experimental results show that the proposed approach achieves state-of-the-art performance under various noise levels and does not require parameter tuning, making it a practical and maintenance-free approach for channel estimation.
Electrical tuning of tin-vacancy centers in diamond

Many applications in quantum information processing require multiple, identical quantum emitters. Among various color centers in diamond, tin-vacancy centers draw attention because of their high quantum efficiency and long spin coherence. The authors study the shifts of optical-transition frequencies of these centers in an applied electric field, and importantly find that both the linear and quadratic coefficients are orders of magnitude smaller than those of non-inversion-symmetric defects such as nitrogen-vacancy centers. This low susceptibility allows the integration of tin-vacancy centers into photonic nanostructures, which is essential for efficient spin-photon interfaces.
A Hybrid Wired/Wireless Deterministic Network for Smart Grid

With the rapid growth of time-critical applications in smart grid, robotics, autonomous vehicles, and industrial automation, demand for high reliability, low latency and strictly bounded jitter is sharply increasing. High-precision time synchronization communications, such as Time Triggered Ethernet (TTE), have been successfully developed for wired networks. However, the high cost of deploying additional equipment and extra wiring limits the scalability of these networks. Therefore, in this article, a hybrid wired/wireless high-precision time synchronization network based on a combination of high-speed TTE and 5G Ultra-Reliable and Low-Latency Communications (URLLC) is proposed. The main motivation is to comply with the low latency, low jitter, and high reliability requirements of time critical applications, such as smart grid synchrophasor communications. Therefore, in the proposed hybrid network architecture, a high-speed TTE is considered as the main bus (i.e., backbone network), whereas a Precision Time Protocol (PTP) aided 5G-URLLC-based wireless access is used as a sub-network. The main challenge is to achieve interoperability between the PTP aided URLLC and the TTE, while ensuring high precision timing and synchronization. The simulation results demonstrate the impact of the PTP-aided URLLC in maintaining network reliability, latency, and jitter in full coordination with the TTE-network.
DASZL: Dynamic Action Signatures for Zero-shot Learning.

There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large. This makes end-to-end supervised training of a recognition system impractical as no training set is practically able to encompass the entire label set. In this paper, we present an approach to fine-grained recognition that models activities as compositions of dynamic action signatures. This compositional approach allows us to reframe fine-grained recognition as zero-shot activity recognition, where a detector is composed "on the fly" from simple first-principles state machines supported by deep-learned components. We evaluate our method on the Olympic Sports and UCF101 datasets, where our model establishes a new state of the art under multiple experimental paradigms. We also extend this method to form a unique framework for zero-shot joint segmentation and classification of activities in video and demonstrate the first results in zero- shot decoding of complex action sequences on a widely-used surgical dataset. Lastly, we show that we can use off-the-shelf object detectors to recognize activities in completely de-novo settings with no additional training.
Past, present and future stars that can see Earth as a transiting exoplanet

In the search for life in the cosmos, transiting exoplanets are currently our best targets. With thousands already detected, our search is entering a new era of discovery with upcoming large telescopes that will look for signs of ‘life’ in the atmospheres of transiting worlds. Previous work has explored the zone from which Earth would be visible while transiting the Sun1–4. However, these studies considered only the current position of stars, and did not include their changing vantage point over time. Here we report that 1,715 stars within 100 parsecs from the Sun are in the right position to have spotted life on a transiting Earth since early human civilization (about 5,000 years ago), with an additional 319 stars entering this special vantage point in the next 5,000 years. Among these stars are seven known exoplanet hosts, including Ross-128, which saw Earth transit the Sun in the past, and Teegarden’s Star and Trappist-1, which will start to see it in 29 and 1,642 years, respectively. We found that human-made radio waves have already swept over 75 of the closest stars on our list. The Gaia database is used to identify stars from which astronomers on orbiting planets could see Earth transiting the Sun in the past, present and future.
Adaptive Variational Quantum Dynamics Simulations

A time-dependent adaptive variational method is developed to enable practical and highly accurate quantum dynamics simulations on NISQ devices.
Farey recursive functions

We introduce Farey recursive functions and investigate their basic properties. Farey recursive functions are a special type of recursive function from the rationals to a commutative ring. The recursion of these functions is organized by the Farey graph. They arise naturally in the study of 2-bridge knots and links.
The Ethics of DNA Testing at the Border.

From 2018 to 2020, the U.S. government dramatically expanded DNA surveillance of immigrants. The most recent expansion, finalized in March 2020, effectively requires the collection of DNA from all immigration detainees and storage of their genetic information in the Federal Bureau of Investigation’s (“FBI”) Combined DNA Index System (“CODIS”) database for criminal forensic investigation. This new policy is ethically troubling because it fails to address the potential privacy harms it creates; shifts the application of DNA analysis for criminal investigation from retrospective to prospective assessment of criminality; and disparately impacts racial and ethnic minorities. In this time of extreme immigration policymaking, the expansion of DNA surveillance of immigrants should be recognized as an element of a larger project of dehumanizing immigrants and criminalizing immigration. Although the policy applies only to immigrants, it is contributing to a transformation of norms around collection and retention of genetic information by the U.S. government generally, which can be expected to spill over to U.S. citizens. Advocates, policymakers, and scholars with expertise in genetic privacy, bioethics, or immigration must speak up to ensure that regulation of DNA collection and analysis in the immigration context is guided by the principles of necessity and proportionality.
Defect chemistry and doping of BiCuSeO

While p-type BiCuSeO is a well-known mid-temperature oxide thermoelectric (TE) material, computations predict that superior TE performance can be realized through n-type doping. In this study, we use first-principles defect calculations to show that Cu vacancies are responsible for the native p-type self doping; yet, we find that BiCuSeO is n-type dopable under Cu-rich growth conditions, where the formation of Cu vacancies is suppressed. We computationally survey a broad suite of 23 dopants and find that only Cl and Br are effective n-type dopants. Therefore, we recommend that future experimental doping efforts utilize phase boundary mapping to optimize the electron concentration and resolve the anomalous p–n–p transitions observed in halogen-doped BiCuSeO. The prospect of n-type doping, as revealed by our defect calculations, paves the path for rational design of BiCuSeO chemical analogues with similar doping behavior and even better TE performance.
Disruption of hippocampal rhythms via optogenetic stimulation during the critical period for memory development impairs spatial cognition

Objective. Investigate whether the disruption of normal hippocampal rhythms via optogenetic stimulation of the medial septum (MS) during late postnatal development has enduring effects on the allocentric memory of rat pups. Methods. Hippocampal theta oscillations were non-endogenously regulated in rat pups using blue light (465-nm) pan-neuronal optogenetic activation of the MS from postnatal day (P)21–25 using random sinewave frequencies ranging from 0.5 to 110 Hz. Non-stimulated and yellow light (590-nm) stimulated rats were used as controls, and local field potentials from the hippocampus and MS were recorded unilaterally during stimulations. In early adulthood, spatial cognition was assessed using the active avoidance task. Rats were perfused and tissue containing the MS and hippocampus was harvested and sectioned to assess cell density. Power spectrum density of both the MS and hippocampus; coherence, and voltage correlation analysis between both structures were used to compare baseline to the disruptive stimulation. Results. During late postnatal development, non-selective optogenetic activation of the MS tightly regulated hippocampal oscillations. Disruptive stimulation increased power but reduced coherence and voltage correlations; and resulted in impaired spatial learning compared with controls. Impairment of spatial cognition was not attributed to MS or hippocampal cell loss. Conclusions. These results demonstrate that hippocampal oscillations can be precisely regulated with non-selective optogenetic stimulation of the MS in weanling rats. A disruptive hippocampal stimulation protocol during the critical period of memory development results in long-standing spatial cognitive deficits that is not attributed to cell loss. Significance. Disruptive activity during postnatal development, such as occurs during early life seizures, has complex effects on developing neural circuits relevant to cognition. By mimicking disruption of MS-hippocampal entrainment in an isolated fashion, it may be possible to pinpoint mechanisms through which ELS may affect cognition.
Regular Sequential Serializability and Regular Sequential Consistency

Strictly serializable (linearizable) services appear to execute transactions (operations) sequentially, in an order consistent with real time. This restricts a transaction's (operation's) possible return values and in turn, simplifies application programming. In exchange, strictly serializable (linearizable) services perform worse than those with weaker consistency. But switching to such services can break applications. This work introduces two new consistency models to ease this trade-off: regular sequential serializability (RSS) and regular sequential consistency (RSC). They are just as strong for applications: we prove any application invariant that holds when using a strictly serializable (linearizable) service also holds when using an RSS (RSC) service. Yet they relax the constraints on services---they allow new, better-performing designs. To demonstrate this, we design, implement, and evaluate variants of two systems, Spanner and Gryff, relaxing their consistency to RSS and RSC, respectively. The new variants achieve better read-only transaction and read tail latency than their counterparts.
Modular development enables rapid design of media for alternative hosts

Developing media to sustain cell growth and production is an essential and ongoing activity in bioprocess development. Modifications to media can often address host or product-specific challenges, such as low productivity or poor product quality. For other applications, systematic design of new media can facilitate the adoption of new industrially relevant alternative hosts. Despite manifold existing methods, common approaches for optimization often remain time and labor-intensive. We present here a novel approach to conventional media blending that leverages stable, simple, concentrated stock solutions to enable rapid improvement of measurable phenotypes of interest. We applied this modular methodology to generate high-performing media for two phenotypes of interest: biomass accumulation and heterologous protein production, using high-throughput, milliliter-scale batch fermentations of Pichia pastoris as a model system. In addition to these examples, we also created a flexible open-source package for modular blending automation on a low-cost liquid handling system to facilitate wide use of this method. Our modular blending method enables rapid, flexible media development, requiring minimal labor investment and prior knowledge of the host organism, and should enable developing improved media for other hosts and phenotypes of interest.
Component decompositions and adynkra libraries for supermultiplets in lower dimensional superspaces

We present Adynkra Libraries that can be used to explore the embedding of multiplets of component field (whether on-shell or partial on-shell) within Salam-Strathdee superfields for theories in dimension nine through four.
Two data-driven approaches to identifying the spectrum of problematic opioid use: A pilot study within a chronic pain cohort.

Abstract Background Although electronic health records (EHR) have significant potential for the study of opioid use disorders (OUD), detecting OUD in clinical data is challenging. Models using EHR data to predict OUD often rely on case/control classifications focused on extreme opioid use. There is a need to expand this work to characterize the spectrum of problematic opioid use. Methods Using a large academic medical center database, we developed 2 data-driven methods of OUD detection: (1) a Comorbidity Score developed from a Phenome-Wide Association Study of phenotypes associated with OUD and (2) a Text-based Score using natural language processing to identify OUD-related concepts in clinical notes. We evaluated the performance of both scores against a manual review with correlation coefficients, Wilcoxon rank sum tests, and area-under the receiver operating characteristic curves. Records with the highest Comorbidity and Text-based scores were re-evaluated by manual review to explore discrepancies. Results Both the Comorbidity and Text-based OUD risk scores were significantly elevated in the patients judged as High Evidence for OUD in the manual review compared to those with No Evidence (p = 1.3E−5 and 1.3E−6, respectively). The risk scores were positively correlated with each other (rho = 0.52, p Conclusion Risk scores comprising comorbidities and text offer differing but synergistic insights into characterizing problematic opioid use. This pilot project establishes a foundation for more robust work in the future.
Learning Human Objectives from Sequences of Physical Corrections

When personal, assistive, and interactive robots make mistakes, humans naturally and intuitively correct those mistakes through physical interaction. In simple situations, one correction is sufficient to convey what the human wants. But when humans are working with multiple robots or the robot is performing an intricate task often the human must make several corrections to fix the robot’s behavior. Prior research assumes each of these physical corrections are independent events, and learns from them one-at-a-time. However, this misses out on crucial information: each of these interactions are interconnected, and may only make sense if viewed together. Alternatively, other work reasons over the final trajectory produced by all of the human’s corrections. But this method must wait until the end of the task to learn from corrections, as opposed to inferring from the corrections in an online fashion. In this paper we formalize an approach for learning from sequences of physical corrections during the current task. To do this we introduce an auxiliary reward that captures the human’s trade-off between making corrections which improve the robot’s immediate reward and long-term performance. We evaluate the resulting algorithm in remote and in-person human-robot experiments, and compare to both independent and final baselines. Our results indicate that users are best able to convey their objective when the robot reasons over their sequence of corrections.
Naive human B cells engage the receptor binding domain of SARS-CoV-2, variants of concern, and related sarbecoviruses.

Initial exposure to a pathogen elicits an adaptive immune response to control and eradicate the threat. Interrogating the abundance and specificity of the naive B cell repertoire drives understandi...
MudrockNet: Semantic segmentation of mudrock SEM images through deep learning

Abstract Segmentation and analysis of individual pores and grains of mudrocks from scanning electron microscope images is non-trivial because of imaging artifacts, variation in pixel grayscale values across images, and overlaps in grayscale values among different physical features such as silt grains, clay grains and pores, which make identifications difficult. Moreover, because grains and pores often have overlapping grayscale values, direct application of threshold-based segmentation techniques is not sufficient. Recent advances in the field of computer vision have made it easier and faster to segment images and identify multiple occurrences of such features in an image, provided that ground-truth data for training the algorithm are available. Here we propose a deep learning SEM image segmentation model, MudrockNet based on Google's DeepLab-v3+ architecture implemented with the TensorFlow library. The ground-truth data were obtained from an image-processing workflow applied to scanning electron microscope images of uncemented muds from the Kumano Basin offshore Japan at depths 90%, and predictions for the test data obtained a mean intersection over union (IoU) of 0.6663 for silt grains, 0.7797 for clay grains and 0.6751 for pores. We also compared our model with the random forest classifier using trainable Weka segmentation in ImageJ, and it was observed that MudrockNet gave better predictions for silt grains, clay grains and pores in most cases. The size, concentration, and spatial arrangement of the silt and clay grains can affect the petrophysical properties of a mudrock, and an automated method to accurately identify the different grains and pores in mudrocks can help improve reservoir and seal characterization for petroleum exploration and anthropogenic waste sequestration.
A Robotic System for Implant Modification in Single-stage Cranioplasty

Craniomaxillofacial reconstruction with patientspecific customized craniofacial implants (CCIs) is most commonly performed for large-sized skeletal defects. Because the exact size of skull resection may not be known prior to the surgery, in single-stage cranioplasty, an oversized CCI is prefabricated and resized intraoperatively with a manual-cutting process provided by a surgeon. The manual resizing, however, may be inaccurate and significantly add to the operating time. This paper introduces a fast and non-contact approach for intraoperatively determining the exact contour of the skull resection and automatically resizing the implant to fit the resection area. Our approach includes four steps: First, we acquire a patient’s defect information using a handheld 3D scanner. Second, the scanned defect is aligned to the CCI by registering the scanned defect to the preoperative CT model. Third, a cutting toolpath is generated from the scanned defect model by extracting the resection contour. Lastly, a cutting robot resizes the oversized CCI to fit the resection area. To evaluate the resizing performance of our method, we generated six different resection shapes for the cutting experiments. We compared the performance of our method to the performance of surgeon’s manual resizing and an existing technique that collects the defect contour with an optical tracking system. The results show that our proposed method improves the resizing accuracy by 56% compared to the surgeon’s manual modification and 42% compared to the optical tracking method.
Society for Maternal-Fetal Medicine Consult Series #59: The use of analgesia and anesthesia for maternal-fetal procedures.

Abstract Pain is a complex phenomenon that involves more than a simple physical response to external stimuli. In maternal-fetal surgical procedures, fetal analgesia is used primarily to blunt fetal autonomic responses and minimize fetal movement. The purpose of this Consult is to review the literature on what is known about the potential for fetal awareness of pain and to discuss the indications for and the risk-benefit calculus involved in the use of fetal anesthesia and analgesia. The recommendations by the Society for Maternal-Fetal Medicine are as follows: (1) we suggest that fetal paralytic agents be considered in the setting of intrauterine transfusion, if needed, for the purpose of decreasing fetal movement (GRADE 2C); (2) although the fetus is unable to experience pain at the gestational age when procedures are typically performed, we suggest that opioid analgesia should be administered to the fetus during invasive fetal surgical procedures to attenuate acute autonomic responses that may be deleterious, avoid long-term consequences of nociception and physiological stress on the fetus, and decrease fetal movement to enable the safe execution of procedures (GRADE 2C); and (3) due to maternal risk and a lack of evidence supporting benefit to the fetus, we recommend against the administration of fetal analgesia at the time of pregnancy termination (GRADE 1C).
ELLA: Exploration through Learned Language Abstraction

Building agents capable of understanding language instructions is critical to effective and robust human-AI collaboration. Recent work focuses on training these instruction following agents via reinforcement learning in environments with synthetic language; however, these instructions often define long-horizon, sparse-reward tasks, and learning policies requires many episodes of experience. To this end, we introduce ELLA: Exploration through Learned Language Abstraction, a reward shaping approach that correlates high-level instructions with simpler low-level instructions to enrich the sparse rewards afforded by the environment. ELLA has two key elements: 1) A termination classifier that identifies when agents complete low-level instructions, and 2) A relevance classifier that correlates low-level instructions with success on high-level tasks. We learn the termination classifier offline from pairs of instructions and terminal states. Notably, in departure from prior work in language and abstraction, we learn the relevance classifier online, without relying on an explicit decomposition of high-level instructions to low-level instructions. On a suite of complex grid world environments with varying instruction complexities and reward sparsity, ELLA shows a significant gain in sample efficiency across several environments compared to competitive language-based reward shaping and no-shaping methods.
Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess

This paper introduces deep synoptic Monte Carlo planning (DSMCP) for large imperfect information games. The algorithm constructs a belief state with an unweighted particle filter and plans via playouts that start at samples drawn from the belief state. The algorithm accounts for uncertainty by performing inference on "synopses," a novel stochastic abstraction of information states. DSMCP is the basis of the program Penumbra, which won the official 2020 reconnaissance blind chess competition versus 33 other programs. This paper also evaluates algorithm variants that incorporate caution, paranoia, and a novel bandit algorithm. Furthermore, it audits the synopsis features used in Penumbra with per-bit saliency statistics.
Platelet transcriptome yields progressive markers in chronic myeloproliferative neoplasms and identifies putative targets of therapy

Abstract Predicting disease progression remains a particularly challenging endeavor in chronic degenerative disorders and cancer, thus limiting early detection, risk stratification, and preventive interventions. Here, profiling the spectrum of chronic myeloproliferative neoplasms (MPNs) as a model, we identify the blood platelet transcriptome as a proxy for highly sensitive progression biomarkers that also enables prediction of advanced disease via machine learning algorithms. Using RNA sequencing (RNA-seq), we derive disease-relevant gene expression in purified platelets from 120 peripheral blood samples constituting two time-separated cohorts of patients diagnosed with one of three MPN subtypes at sample acquisition – essential thrombocythemia, ET (n=24), polycythemia vera, PV (n=33), and primary or post ET/PV secondary myelofibrosis, MF (n=42), and healthy donors (n=21). The MPN platelet transcriptome reveals an incremental molecular reprogramming that is independent of patient driver mutation status or therapy and discriminates each clinical phenotype. Leveraging this dataset that shows a characteristic progressive expression gradient across MPN, we develop a machine learning model (Lasso-penalized regression) and predict advanced subtype MF at high accuracy and under two conditions of external validation: i) temporal: our two Stanford cohorts, AUC-ROC of 0.96; and ii) geographical: independently published data of an additional n=25 MF and n=46 healthy donors, AUC-ROC of 0.97). Lasso-derived signatures offer a robust core set of Highlights Leveraging two independent and mutually validating MPN patient cohorts, we identify progressive transcriptomic markers that also enable externally validated prediction in MPNs. Our platelet RNA-Seq data identifies impaired protein homeostasis as prominent in MPN progression and offers putative targets of therapy. VISUAL ABSTRACT
DARSI: An Ontology for Facilitating the Development of Data Sharing and Use Agreements

The advantages of data sharing across organizations and disciplines are indisputable; although, sensitive and restricted data cannot be easily shared due to policies and legal matters. The research presented in this paper takes a step toward systematizing the sharing of sensitive and restricted research data by developing an ontology to frame and guide DSUA (Data Sharing and Usage Agreement) development. The paper provides background context, describes the ontology creation process, and introduces the Data Sharing Agreements for Restricted and Sensitive Information (DARSI) ontology. DARSI contains four top level classes, 20 sub-classes, 33 sub-categories and 17 simple properties for categories applicable at various levels. The discussion provides further insight into the work accomplished, and the conclusion identifies next steps.
Extractor: Extracting Attack Behavior from Threat Reports

The knowledge on attacks contained in Cyber Threat Intelligence (CTI) reports is very important to effectively identify and quickly respond to cyber threats. However, this knowledge is often embedded in large amounts of text, and therefore difficult to use effectively. To address this challenge, we propose a novel approach and tool called Extractor that allows precise automatic extraction of concise attack behaviors from CTI reports. Extractor makes no strong assumptions about the text and is capable of extracting attack behaviors as provenance graphs from unstructured text. We evaluate Extractor using real-world incident reports from various sources as well as reports of DARPA adversarial engagements that involve several attack campaigns on various OS platforms of Windows, Linux, and FreeBSD. Our evaluation results show that Extractor can extract concise provenance graphs from CTI reports and show that these graphs can successfully be used by cyber-analytics tools in threat-hunting.
Enhanced performance in GaAs TUNNETT diode oscillators above 100 GHz through diamond heat sinking and power combining

Single-drift GaAs TUNNETT diodes on integral heat sinks exhibit no sign of saturation in RF output power nor efficiency and are thought to be thermally limited. Therefore, diodes from the same MBE material were fabricated using a different selective etching technology and mounted on diamond heat sinks for improved heat dissipation. RF output power levels of over 80 mW with corresponding DC to RF conversion efficiencies of 5.2% were measured between 104 GHz and 107 GHz. To further increase the available RF output power, two TUNNETT diodes were power combined using two radial line full height waveguide cavities. The combined RF output power was 46 mW at 102.3 GHz from two diodes on integral heat sinks and 96.5 mW at 105.9 GHz from two diodes on diamond heat sinks with corresponding combining efficiencies of 80% and more. This is the first successful demonstration of power combining with TUNNETT diodes and the power levels and efficiencies from either single or combined devices are the highest reported to date from any devices made of III-V materials (e.g., GaAs and InP). >
Quantum error correction and large $N$

In recent years quantum error correction(QEC) has become an important part of AdS/CFT. Unfortunately, there are no field-theoretic arguments about why QEC holds in known holographic systems. The purpose of this paper is to fill this gap by studying the error correcting properties of the fermionic sector of various large $N$ theories. Specifically we examine $SU(N)$ matrix quantum mechanics and 3-rank tensor $O(N)^3$ theories. Both of these theories contain large gauge groups. We argue that gauge singlet states indeed form a quantum error correcting code. Our considerations are based purely on large $N$ analysis and do not appeal to a particular form of Hamiltonian or holography.
Staged Skin and Subcutaneous Excision

Lymphedema is a relatively rare problem. Lymphedema results from abnormal and inappropriate collection of lymphatic fluid in the subcutaneous tissues. The etiologies underlying this abnormal collection of this proteinaceous fluid may be from congenital anomalies, functional overload of the superficial draining system, or a combination of both entities. There have been a variety of surgical approaches to manage this condition; however, in our experience staged skin and subcutaneous excision has provided the most durable results to this chronic condition.
The Design of an Adaptive Expert Database Interface

There have been many attempts to increase a users effectiveness at retrieving data from a database. These attempts frequently focus on enhancing the interface through which the user accesses the system. The use of natural language as a medium in which to express a data retrieval task is an example of such an enhancement [1–4]. This paper examines how expert knowledge about relationships between variables in a database can be used to make inferences concerning items to be retrieved. While the principles used to design an expert interface for information retrieval can be generalized to other datasets, the focus here will be on the design of the interface for a particular dataset. The dataset of interest describes makes of automobiles in terms of 39 variables.