cssi/SciSciGPT-SciSciNet huggingface dataset:
Directed information and pearl's causal calculus

Probabilistic graphical models are a fundamental tool in statistics, machine learning, signal processing, and control. When such a model is defined on a directed acyclic graph (DAG), one can assign a partial ordering to the events occurring in the corresponding stochastic system. Based on the work of Judea Pearl and others, these DAG-based “causal factorizations” of joint probability measures have been used for characterization and inference of functional dependencies (causal links). This mostly expository paper focuses on several connections between Pearl's formalism (and in particular his notion of “intervention”) and information-theoretic notions of causality and feedback (such as causal conditioning, directed stochastic kernels, and directed information). As an application, we show how conditional directed information can be used to develop an information-theoretic version of Pearl's “backdoor” criterion for identifiability of causal effects from passive observations. This suggests that the back-door criterion can be thought of as a causal analog of statistical sufficiency.
TRIDENT: A Load-Balancing Clos-Network Packet Switch With Queues Between Input and Central Stages and In-Order Forwarding

We propose a three-stage load balancing packet switch and its configuration scheme. The input- and central-stage switches are bufferless crossbars, and the output-stage switches are buffered crossbars. We call this switch ThRee-stage Clos-network swItch with queues at the middle stage and DEtermiNisTic scheduling (TRIDENT), and the switch is cell based. The proposed configuration scheme uses predetermined and periodic interconnection patterns in the input and central modules to load-balance and route traffic, therefore, it has low configuration complexity. The operation of the switch includes a mechanism applied at input and output modules to forward cells in sequence. TRIDENT achieves 100% throughput under uniform and nonuniform admissible traffic with independent and identical distributions (i.i.d.). The switch achieves this high performance using a low-complexity architecture while performing in-sequence forwarding and no central-stage expansion or memory speedup. We analyze the operations the configuration mechanisms perform on the traffic traversing the switch. We use this analysis to prove that the switch achieves 100% through under i.i.d. traffic. We also show that the switch forward cells in-sequence. We present a simulation analysis as a practical demonstration of the switch performance under uniform and nonuniform i.i.d. traffic.
MTNT: A Testbed for Machine Translation of Noisy Text

Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating noise-robust MT systems. However, as of yet there are no publicly available parallel corpora of with naturally occurring noisy inputs and translations, and thus previous work has resorted to evaluating on synthetically created datasets. In this paper, we propose a benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit (www.reddit.com) and professionally sourced translations. We commissioned translations of English comments into French and Japanese, as well as French and Japanese comments into English, on the order of 7k-37k sentences per language pair. We qualitatively and quantitatively examine the types of noise included in this dataset, then demonstrate that existing MT models fail badly on a number of noise-related phenomena, even after performing adaptation on a small training set of in-domain data. This indicates that this dataset can provide an attractive testbed for methods tailored to handling noisy text in MT.
Towards Deep Learning Models Resistant to Adversarial Attacks.

Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples—inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at this https URL and this https URL.
Stratifying KLR algebras of affine ADE types

Abstract We generalize imaginary Howe duality for KLR algebras of affine ADE types, developed in our previous paper, from balanced to arbitrary convex preorders. Under the assumption that the characteristic of the ground field is greater than some explicit bound, we prove that these KLR algebras are properly stratified.
Similar but Different: Exploiting Users’ Congruity for Recommendation Systems

The pervasive use of social media provides massive data about individuals’ online social activities and their social relations. The building block of most existing recommendation systems is the similarity between users with social relations, i.e., friends. While friendship ensures some homophily, the similarity of a user with her friends can vary as the number of friends increases. Research from sociology suggests that friends are more similar than strangers, but friends can have different interests. Exogenous information such as comments and ratings may help discern different degrees of agreement (i.e., congruity) among similar users. In this paper, we investigate if users’ congruity can be incorporated into recommendation systems to improve it’s performance. Experimental results demonstrate the effectiveness of embedding congruity related information into recommendation systems.
Does the electromotive force (always) represent work

In the literature of Electromagnetism, the electromotive force of a "circuit" is often defined as work done on a unit charge during a complete tour of the latter around the circuit. We explain why this statement cannot be generally regarded as true, although it is indeed true in certain simple cases. Several examples are used to illustrate these points.
Nonlinear parity-time-symmetric transition in finite-size optical couplers

Parity-time-symmetric (PT-symmetric) optical waveguide couplers offer a great potential for future applications in integrated optics, such as ultracompact reconfigurable all-optical signal processing. Here, we predict a nonlinearly triggered transition from a full to a broken PT-symmetric regime in finite-size systems described by smooth permittivity profiles and, in particular, in a conventional discrete waveguide directional coupler configuration with a rectangular profile. For these systems, we show that this phase transition occurs in PT-symmetric couplers, regardless of the details of their geometry, therefore suggesting a practical route for experimental realization of such systems.
The short rate disconnect in a monetary economy

Abstract In modern monetary economies, most payments are made with inside money provided by payment intermediaries. This paper studies interest rate dynamics when payment intermediaries value short bonds as collateral to back inside money. We estimate intermediary Euler equations that relate the short safe rate to other interest rates as well as intermediary leverage and portfolio risk. Towards the end of economic booms, the short rate set by the central bank disconnects from other interest rates: as collateral becomes scarce and spreads widen, payment intermediaries reduce leverage, and increase portfolio risk. We document stable business cycle relationships between spreads, leverage, and the safe portfolio share of payment intermediaries that are consistent with the model. Structural changes, especially in regulation, induce low frequency shifts, such as after the financial crisis.
Short Maturity Forward Start Asian Options in Local Volatility Models

We study the short maturity asymptotics for prices of forward start Asian options under the assumption that the underlying asset follows a local volatility model. We obtain asymptotics for the case...
StrainHub: a phylogenetic tool to construct pathogen transmission networks.

Summary In exploring the epidemiology of infectious diseases, networks have been used to reconstruct contacts among individuals and/or populations. Summarizing networks using pathogen metadata (e.g. host species and place of isolation) and a phylogenetic tree is a nascent, alternative approach. In this paper, we introduce a tool for reconstructing transmission networks in arbitrary space from phylogenetic information and metadata. Our goals are to provide a means of deriving new insights and infection control strategies based on the dynamics of the pathogen lineages derived from networks and centrality metrics. We created a web-based application, called StrainHub, in which a user can input a phylogenetic tree based on genetic or other data along with characters derived from metadata using their preferred tree search method. StrainHub generates a transmission network based on character state changes in metadata, such as place or source of isolation, mapped on the phylogenetic tree. The user has the option to calculate centrality metrics on the nodes including betweenness, closeness, degree and a new metric, the source/hub ratio. The outputs include the network with values for metrics on its nodes and the tree with characters reconstructed. All of these results can be exported for further analysis. Availability and implementation strainhub.io and https://github.com/abschneider/StrainHub.
A Multi-Motor Architecture for Electric Vehicles

This paper proposes an architecture for EVs with three or more electric motors and highlights when adding more motors does not impact the battery state of charge (SOC). The proposed control algorithm optimizes the use of the motors onboard to keep them running in their most efficient regions. Simulation results along with a comparison with other current motors used in EVs is presented in this paper, and further discussion on the results is presented. With this architecture, the powertrain would see a combined efficiency map that incorporates the best operating points of the motors. Therefore, the proposed architecture will allow the EV to operate with a higher range for a given battery capacity.
Tunable Measures for Information Leakage and Applications to Privacy-Utility Tradeoffs

We introduce a tunable measure for information leakage called maximal $\alpha $ -leakage . This measure quantifies the maximal gain of an adversary in inferring any (potentially random) function of a dataset from a release of the data. The inferential capability of the adversary is, in turn, quantified by a class of adversarial loss functions that we introduce as $\alpha $ -loss, $\alpha \in [1,\infty) \cup \{\infty \}$ . The choice of $\alpha $ determines the specific adversarial action and ranges from refining a belief (about any function of the data) for $\alpha =1$ to guessing the most likely value for $\alpha = \infty $ while refining the $\alpha ^{\text {th}}$ moment of the belief for $\alpha $ in between. Maximal $\alpha $ -leakage then quantifies the adversarial gain under $\alpha $ -loss over all possible functions of the data. In particular, for the extremal values of $\alpha =1$ and $\alpha =\infty $ , maximal $\alpha $ -leakage simplifies to mutual information and maximal leakage, respectively. For $\alpha \in (1,\infty)$ this measure is shown to be the Arimoto channel capacity of order $\alpha $ . We show that maximal $\alpha $ -leakage satisfies data processing inequalities and a sub-additivity property thereby allowing for a weak composition result. Building upon these properties, we use maximal $\alpha $ -leakage as the privacy measure and study the problem of data publishing with privacy guarantees, wherein the utility of the released data is ensured via a hard distortion constraint. Unlike average distortion, hard distortion provides a deterministic guarantee of fidelity. We show that under a hard distortion constraint, for $\alpha >1$ the optimal mechanism is independent of $\alpha $ , and therefore, the resulting optimal tradeoff is the same for all values of $\alpha >1$ . Finally, the tunability of maximal $\alpha $ -leakage as a privacy measure is also illustrated for binary data with average Hamming distortion as the utility measure.
{JEDI}: Many-to-Many End-to-End Encryption and Key Delegation for IoT

As the Internet of Things (IoT) emerges over the next decade, developing secure communication for IoT devices is of paramount importance. Achieving end-to-end encryption for large-scale IoT systems, like smart buildings or smart cities, is challenging because multiple principals typically interact indirectly via intermediaries, meaning that the recipient of a message is not known in advance. This paper proposes JEDI (Joining Encryption and Delegation for IoT), a many-to-many end-to-end encryption protocol for IoT. JEDI encrypts and signs messages end-to-end, while conforming to the decoupled communication model typical of IoT systems. JEDI's keys support expiry and fine-grained access to data, common in IoT. Furthermore, JEDI allows principals to delegate their keys, restricted in expiry or scope, to other principals, thereby granting access to data and managing access control in a scalable, distributed way. Through careful protocol design and implementation, JEDI can run across the spectrum of IoT devices, including ultra low-power deeply embedded sensors severely constrained in CPU, memory, and energy consumption. We apply JEDI to an existing IoT messaging system and demonstrate that its overhead is modest.
A Data-Driven CO2 Leakage Detection Using Seismic Data and Spatial-Temporal Densely Connected Convolutional Neural Networks

Abstract In carbon capture and sequestration (also known as carbon capture and storage, or CCS), developing effective monitoring methods is needed to detect and respond to CO2 leakage. CO2 leakage detection methods rely on geophysical observations and monitoring sensor network. However, traditional methods usually require the development of site-specific physical models and expert interpretation, and the effectiveness of these methods can be limited to different application locations, operational scenarios, and conditions. In this paper, we developed a novel data-driven leakage detection method based on densely connected convolutional neural networks. Our method is an end-to-end detection approach, that differs from conventional leakage monitoring methods by directly learning a mapping relationship between seismic data and the CO2 leakage mass. To account for the spatial and temporal characteristics of seismic data, our novel networks architecture combines 1D and 2D convolutional neural networks. To overcome the computational expense of solving optimization problems, we apply a densely-connecting strategy in our network architecture that reduces the number of network parameters. Based on the features generated by our convolutional neural networks, we further incorporate a long short-term memory network to utilize time-sequential information, which further improves the detection accuracy. Finally, we employ our detection method to synthetic seismic datasets generated based on flow simulations of a hypothetical CO2 storage scenario with injection into a partially compartmentalized sandstone storage reservoir. To evaluate method performance, we conducted multiple experiments including a random leakage test, a sequential test, and a robustness test. Numerical results show that our CO2 leakage detection method successfully detects the leakage and accurately predicts the leakage mass, suggesting that it has the potential for application in monitoring of real CO2 storage sites.
MOARD: Modeling Application Resilience to Transient Faults on Data Objects

Understanding application resilience (or error tolerance) in the presence of hardware transient faults on data objects is critical to ensure computing integrity and enable efficient application-level fault tolerance mechanisms. However, we lack a method and a tool to quantify application resilience to transient faults on data objects. The traditional method, random fault injection, cannot help, because of losing data semantics and insufficient information on how and where errors are tolerated. In this paper, we introduce a method and a tool (called "MOARD") to model and quantify application resilience to transient faults on data objects. Our method is based on systematically quantifying error masking events caused by application-inherent semantics and program constructs. We use MOARD to study how and why errors in data objects can be tolerated by the application. We demonstrate tangible benefits of using MOARD to direct a fault tolerance mechanism to protect data objects.
Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission

Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature. Although recent advances in deep learning (DL) enables models to extract interpretable features from unstructured data, they often require a large amount of training data. However, many tasks in medical domains inherently consist of small sample data with lengthy documents; for a kidney transplant as an example, data from only a few thousand of patients are available and each patient's document consists of a couple of millions of words in major hospitals. Thus, complex DL methods cannot be applied to these kind of domains. In this paper, we present a comprehensive ensemble model using vector space modeling and topic modeling. Our proposed model is evaluated on the readmission task of kidney transplant patients, and improves 0.0211 in terms of c-statistics from the previous state-of-the-art approach using structured data, while typical DL methods fails to beat this approach. The proposed architecture provides the interpretable score for each feature from both modalities, structured and unstructured data, which is shown to be meaningful through a physician's evaluation.
Arithmetic-Geometric Mean Robustness for Control from Signal Temporal Logic Specifications

We present a new average-based robustness for Signal Temporal Logic (STL) and a framework for optimal control of a dynamical system under STL constraints. By averaging the scores of different specifications or subformulae at different time points, our definition highlights the frequency of satisfaction as well as how robustly each specification is satisfied. Its usefulness in control synthesis problems is illustrated through case studies.
Experimental Evaluation of Continuum Deformation with a Five Quadrotor Team

This paper experimentally evaluates continuum deformation cooperative control for the first time. Theoretical results are expanded to place a bounding triangle on the leader-follower system such that the team is contained despite nontrivial tracking error. Flight tests were conducted with custom quadrotors running a modified version of ArduPilot on a BeagleBone Blue in M-Air, an outdoor netted flight facility. Motion capture and an onboard inertial measurement unit were used for state estimation. Position error was characterized in single vehicle tests using quintic spline trajectories and different reference velocities. Five-quadrotor leader trajectories were generated, and followers executed the continuum deformation control law in-flight. Flight tests successfully demonstrated continuum deformation; future work in characterizing error propagation from leaders to followers is discussed.
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation.

Conventional automatic speech recognition (ASR) systems trained from frame-level alignments can easily leverage posterior fusion to improve ASR accuracy and build a better single model with knowledge distillation. End-to-end ASR systems trained using the Connectionist Temporal Classification (CTC) loss do not require frame-level alignment and hence simplify model training. However, sparse and arbitrary posterior spike timings from CTC models pose a new set of challenges in posterior fusion from multiple models and knowledge distillation between CTC models. We propose a method to train a CTC model so that its spike timings are guided to align with those of a pre-trained guiding CTC model. As a result, all models that share the same guiding model have aligned spike timings. We show the advantage of our method in various scenarios including posterior fusion of CTC models and knowledge distillation between CTC models with different architectures. With the 300-hour Switchboard training data, the single word CTC model distilled from multiple models improved the word error rates to 13.7%/23.1% from 14.9%/24.1% on the Hub5 2000 Switchboard/CallHome test sets without using any data augmentation, language model, or complex decoder.
Imitation learning for agile autonomous driving

We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost on-board sensors. By imitating a model predictive controller equipped with advanced sen...
Deep Retinal Image Segmentation With Regularization Under Geometric Priors

Vessel segmentation of retinal images is a key diagnostic capability in ophthalmology. This problem faces several challenges including low contrast, variable vessel size and thickness, and presence of interfering pathology such as micro-aneurysms and hemorrhages. Early approaches addressing this problem employed hand-crafted filters to capture vessel structures, accompanied by morphological post-processing. More recently, deep learning techniques have been employed with significantly enhanced segmentation accuracy. We propose a novel domain enriched deep network that consists of two components: 1) a representation network that learns geometric features specific to retinal images, and 2) a custom designed computationally efficient residual task network that utilizes the features obtained from the representation layer to perform pixel-level segmentation. The representation and task networks are jointly learned for any given training set. To obtain physically meaningful and practically effective representation filters, we propose two new constraints that are inspired by expected prior structure on these filters: 1) orientation constraint that promotes geometric diversity of curvilinear features, and 2) a data adaptive noise regularizer that penalizes false positives. Multi-scale extensions are developed to enable accurate detection of thin vessels. Experiments performed on three challenging benchmark databases under a variety of training scenarios show that the proposed prior guided deep network outperforms state of the art alternatives as measured by common evaluation metrics, while being more economical in network size and inference time.
Essential Oils with High Activity against Stationary Phase Bartonella henselae.

Bartonella henselae is a fastidious Gram-negative intracellular bacterium that can cause cat scratch disease, endocarditis in humans and animals, as well as other complications, leading to acute or chronic infections. The current treatment for Bartonella infections is not very effective presumably due to bacterial persistence. To develop better therapies for persistent and chronic Bartonella infections, in this study, with the help of SYBR Green I/PI viability assay, we performed a high-throughput screening of an essential oil library against the stationary phase B. henselae. We successfully identified 32 essential oils that had high activity, including four essential oils extracted from Citrus plants, three from Origanum, three from Cinnamomum, two from Pelargonium, and two from Melaleuca, as well as frankincense, ylang-ylang, fir needle, mountain savory (winter), citronella, spearmint, elemi, vetiver, clove bud, allspice, and cedarwood essential oils. The minimal inhibitory concentration (MIC) determination of these 32 top hits indicated they were not only active against stationary phase non-growing B. henselae but also had good activity against log-phase growing B. henselae. The time-kill assay showed 13 active hits, including essential oils of oregano, cinnamon bark, mountain savory (winter), cinnamon leaf, geranium, clove bud, allspice, geranium bourbon, ylang-ylang, citronella, elemi, and vetiver, could eradicate all stationary phase B. henselae cells within seven days at the concentration of 0.032% (v/v). Two active ingredients, carvacrol and cinnamaldehyde, of oregano and cinnamon bark essential oils, respectively, were shown to be very active against the stationary phase B. henselae such that they were able to eradicate all the bacterial cells even at the concentration ≤ 0.01% (v/v). More studies are needed to identify the active components of some potent essential oils, decode their antimicrobial mechanisms, and evaluate their activity against Bartonella infections in animal models.
Relationships between depressive symptoms and brain responses during emotional movie viewing emerge in adolescence

Affective disorders such as major depression are common but serious illnesses characterized by altered processing of emotional information. Although the frequency and severity of depressive symptoms increase dramatically over the course of childhood and adolescence, much of our understanding of their neurobiological bases comes from work characterizing adults' responses to static emotional information. As a consequence, relationships between depressive brain phenotypes and naturalistic emotional processing, as well as the manner in which these associations emerge over the lifespan, remain poorly understood. Here, we apply static and dynamic inter-subject correlation analyses to examine how brain function is associated with clinical and non-clinical depressive symptom severity in 112 children and adolescents (7-21 years old) who viewed an emotionally evocative clip from the film Despicable Me during functional MRI. Our results reveal that adolescents with greater depressive symptom severity exhibit atypical fMRI responses during movie viewing, and that this effect is stronger during less emotional moments of the movie. Furthermore, adolescents with more similar item-level depressive symptom profiles showed more similar brain responses during movie viewing. In contrast, children's depressive symptom severity and profiles were unrelated to their brain response typicality or similarity. Together, these results indicate a developmental change in the relationships between brain function and depressive symptoms from childhood through adolescence. Our findings suggest that depressive symptoms may shape how the brain responds to complex emotional information in a dynamic manner sensitive to both developmental stage and affective context.
The microbiota regulate neuronal function and fear extinction learning

Multicellular organisms have co-evolved with complex consortia of viruses, bacteria, fungi and parasites, collectively referred to as the microbiota1. In mammals, changes in the composition of the microbiota can influence many physiologic processes (including development, metabolism and immune cell function) and are associated with susceptibility to multiple diseases2. Alterations in the microbiota can also modulate host behaviours—such as social activity, stress, and anxiety-related responses—that are linked to diverse neuropsychiatric disorders3. However, the mechanisms by which the microbiota influence neuronal activity and host behaviour remain poorly defined. Here we show that manipulation of the microbiota in antibiotic-treated or germ-free adult mice results in significant deficits in fear extinction learning. Single-nucleus RNA sequencing of the medial prefrontal cortex of the brain revealed significant alterations in gene expression in excitatory neurons, glia and other cell types. Transcranial two-photon imaging showed that deficits in extinction learning after manipulation of the microbiota in adult mice were associated with defective learning-related remodelling of postsynaptic dendritic spines and reduced activity in cue-encoding neurons in the medial prefrontal cortex. In addition, selective re-establishment of the microbiota revealed a limited neonatal developmental window in which microbiota-derived signals can restore normal extinction learning in adulthood. Finally, unbiased metabolomic analysis identified four metabolites that were significantly downregulated in germ-free mice and have been reported to be related to neuropsychiatric disorders in humans and mouse models, suggesting that microbiota-derived compounds may directly affect brain function and behaviour. Together, these data indicate that fear extinction learning requires microbiota-derived signals both during early postnatal neurodevelopment and in adult mice, with implications for our understanding of how diet, infection, and lifestyle influence brain health and subsequent susceptibility to neuropsychiatric disorders. A diverse intestinal microbiota is required for mice to undergo extinction-related neuronal plasticity and normal fear extinction learning.
Diversity and unity of uncertainty theories

An overview of basic ideas and results emerging from a research program known as Generalized Information Theory (GIT) is presented in the context of the area of intelligent systems. The aim of this research program is to study the concepts of uncertainty and uncertainty-based information in all their manifestations. In GIT, the primary concept is uncertainty and information is defined in terms of reduction of relevant uncertainty.
Template-switching mechanism of a group II intron-encoded reverse transcriptase and its implications for biological function and RNA-Seq.

The reverse transcriptases (RTs) encoded by mobile group II introns and other non-LTR retroelements differ from retroviral RTs in being able to template-switch efficiently from the 5′ end of one template to the 3′ end of another with little or no complementarity between the donor and acceptor templates. Here, to establish a complete kinetic framework for the reaction and to identify conditions that more efficiently capture acceptor RNAs or DNAs, we used a thermostable group II intron RT (TGIRT; GsI–IIC RT) that can template switch directly from synthetic RNA template/DNA primer duplexes having either a blunt end or a 3′-DNA overhang end. We found that the rate and amplitude of template switching are optimal from starter duplexes with a single nucleotide 3′-DNA overhang complementary to the 3′ nucleotide of the acceptor RNA, suggesting a role for nontemplated nucleotide addition of a complementary nucleotide to the 3′ end of cDNAs synthesized from natural templates. Longer 3′-DNA overhangs progressively decreased the template-switching rate, even when complementary to the 3′ end of the acceptor template. The reliance on only a single bp with the 3′ nucleotide of the acceptor together with discrimination against mismatches and the high processivity of group II intron RTs enable synthesis of full-length DNA copies of nucleic acids beginning directly at their 3′ end. We discuss the possible biological functions of the template-switching activity of group II intron- and other non-LTR retroelement–encoded RTs, as well as the optimization of this activity for adapter addition in RNA- and DNA-Seq protocols.
X-ray Emission from O Stars

Young O stars are strong, hard, and variable X-ray sources, properties which strongly afiect their circumstellar and galactic environments. After … 1 Myr, these stars settle down to become steady sources of soft X-rays. I will use high-resolution X-ray spectroscopy and MHD modeling to show that young O stars like µ 1 Ori C are well explained by the magnetically channeled wind shock scenario. After their magnetic flelds dissipate, older O stars produce X-rays via shock heating in their unstable stellar winds. Here too I will use X-ray spectroscopy and numerical modeling to conflrm this scenario. In addition to elucidating the nature and cause of the O star X-ray emission, modeling of the high-resolution X-ray spectra of O supergiants provides strong evidence that mass-loss rates of these O stars have been overestimated.
Outflow Facility Effects of 3 Schlemm's Canal Microinvasive Glaucoma Surgery Devices.

Purpose To study the effect of 3 Schlemm’s canal (SC) microinvasive glaucoma surgery (MIGS) devices on outflow facility. Design Paired comparisons, randomized design, baseline-controlled study. Participants Thirty-six pairs of dissected anterior segments from donated human eye bank eyes without glaucoma were studied. A baseline measurement was collected from each eye to serve as its control. Methods Using a constant pressure perfusion method, outflow facility was measured in paired eyes from human donors. Measurements were made at perfusion pressures of 10 mmHg, 20 mmHg, 30 mmHg, and 40 mmHg. Outflow facility was measured before (baseline control) and after the implantation of an SC glaucoma drainage device or sham procedure. Three sets of experiments were carried out comparing 1 and 2 iStent Trabecular Micro-Bypass Stents and 2 iStent Inject implants with the Hydrus Microstent. Main Outcome Measures Change in outflow facility from baseline or contralateral eye. Results After Hydrus placement, the outflow facility increased from 0.23±0.03 μl/minute per millimeter of mercury at baseline to 0.38±0.03 μl/minute per millimeter of mercury (P Conclusions The longer the MIGS device, and thus the more SC that it dilates, the greater the outflow facility.
Assessing the motivational effects of ethanol in mice using a discrete-trial current-intensity intracranial self-stimulation procedure.

Abstract Background Alcohol (ethanol) produces both rewarding and aversive effects, and sensitivity to these effects is associated with risk for an alcohol use disorder (AUD). Measurement of these motivational effects in animal models is an important but challenging aspect of preclinical research into the neurobiology of AUD. Here, we evaluated whether a discrete-trial current-intensity intracranial self-stimulation (ICSS) procedure can be used to assess both reward-enhancing and aversive responses to ethanol in mice. Methods Male and female C57BL/6J mice were surgically implanted with bipolar stimulating electrodes targeting the medial forebrain bundle and trained on a discrete-trial current-intensity ICSS procedure. Mice were tested for changes in response thresholds after various doses of ethanol (0.5 g/kg-1.75 g/kg; n = 5–7 per dose), using a Latin square design. Results A 1 g/kg dose of ethanol produced a significant reward-enhancement (i.e., lowered response thresholds), whereas a 1.75 g/kg dose produced an aversive effect (elevated response thresholds). Ethanol doses from 1 to 1.75 g/kg increased response latencies as compared to saline treatment. Conclusions The discrete-trial current-intensity ICSS procedure is an effective assay for measuring both reward-enhancing responses to ethanol as well as aversive responses in the same animal. This should prove to be a useful tool for assessing the effects of experimental manipulations on the motivational effects of ethanol in mice.
Multi-ethnic analysis shows genetic risk and environmental predictors interact to influence 25(OH)D concentration and optimal vitamin D intake.

25-Hydroxyvitamin D (25(OH)D) concentration is a complex trait with genetic and environmental predictors that may determine how much vitamin D exposure is required to reach optimal concentration. Interactions between continuous measures of a polygenic score (PGS) and vitamin D intake (PGS*intake) or available ultraviolet (UV) radiation (PGS*UV) were evaluated in individuals of African (n = 1,099) or European (n = 8,569) ancestries. Interaction terms and joint effects (main and interaction terms) were tested using one-degree of freedom (1-DF) and 2-DF models, respectively. Models controlled for age, sex, body mass index, cohort, and dietary intake/available UV. In addition, in participants achieving Institute of Medicine (IOM) vitamin D intake recommendations, 25(OH)D was evaluated by level PGS. The 2-DF PGS*intake, 1-DF PGS*UV, and 2-DF PGS*UV results were statistically significant in participants of European ancestry (p = 3.3 × 10-18 , p = 2.1 × 10-2 , and p = 2.4 × 10-19 , respectively), but not in those of African ancestry. In European-ancestry participants reaching IOM vitamin D intake guidelines, the percent of participants achieving adequate 25(OH)D ( >20 ng/ml) increased as genetic risk decreased (72% vs. 89% in highest vs. lowest risk; p = .018). Available UV radiation and vitamin D intake interact with genetics to influence 25(OH)D. Individuals with higher genetic risk may require more vitamin D exposure to maintain optimal 25(OH)D concentrations.
Techniques for interpretable machine learning

Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. We provide a survey covering existing techniques to increase the interpretability of machine learning models. We also discuss crucial issues that the community should consider in future work such as designing user-friendly explanations and developing comprehensive evaluation metrics to further push forward the area of interpretable machine learning.
The Calabi metric and desingularization of Einstein orbifolds

Consider an Einstein orbifold $(M_0,g_0)$ of real dimension $2n$ having a singularity with orbifold group the cyclic group of order $n$ in ${\rm{SU}}(n)$ which is generated by an $n$th root of unity times the identity. Existence of a Ricci-flat Kahler ALE metric with this group at infinity was shown by Calabi. There is a natural "approximate" Einstein metric on the desingularization of $M_0$ obtained by replacing a small neighborhood of the singular point of the orbifold with a scaled and truncated Calabi metric. In this paper, we identify the first obstruction to perturbing this approximate Einstein metric to an actual Einstein metric. If $M_0$ is compact, we can use this to produce examples of Einstein orbifolds which do not admit any Einstein metric in a neighborhood of the natural approximate Einstein metric on the desingularization. In the case that $(M_0,g_0)$ is asymptotically hyperbolic Einstein and non-degenerate, we show that if the first obstruction vanishes, then there does in fact exist an asymptotically hyperbolic Einstein metric on the desingularization. We also obtain a non-existence result in the asymptotically hyperbolic Einstein case, provided that the obstruction does not vanish. This work extends a construction of Biquard in the case $n =2$, in which case the Calabi metric is also known as the Eguchi-Hanson metric, but there are some key points for which the higher-dimensional case differs.
SYMMETRIC MONOIDAL G-CATEGORIES AND THEIR STRICTIFICATION

We give an operadic definition of a genuine symmetric monoidal G-category, and we prove that its classifying space is a genuine E_\infty G-space. We do this by developing some very general categorical coherence theory. We combine results of Corner and Gurski, Power, and Lack, to develop a strictification theory for pseudoalgebras over operads and monads. It specializes to strictify genuine symmetric monoidal G-categories to genuine permutative G-categories. All of our work takes place in a general internal categorical framework that has many quite different specializations. When G is a finite group, the theory here combines with previous work to generalize equivariant infinite loop space theory from strict space level input to considerably more general category level input. It takes genuine symmetric monoidal G-categories as input to an equivariant infinite loop space machine that gives genuine G-spectra as output.
Impact of social influence on adoption behavior: an online controlled experimental evaluation

It is widely believed that the adoption behavior of a decision-maker in a social network is related to the number of signals it receives from its peers in the social network. It is unclear if these same principles hold when the "pattern" by which they receive these signals vary and when potential decisions have different utilities. To investigate that, we manipulate social signal exposure in an online controlled experiment with human participants. Specifically, we change the number of signals and the pattern through which participants receive them over time. We analyze its effect through a controlled game where each participant makes a decision to select one option when presented with six choices with differing utilities, with one choice having the most utility. We avoided network effects by holding the neighborhood network of the users constant. Over multiple rounds of the game, we observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the six choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar choices, (2) when the quantity of social signals vary over time, the probability that a participant selects the decision similar to the one reflected by the social signals and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals and (3) an early subjugation to higher quantity of peer social signals turned out to be a more effective strategy of social influence when aggregated over the rounds.
Distributed leader following of an active leader for linear heterogeneous multi-agent systems

Abstract This paper considers a leader-following problem for a group of heterogeneous linear time invariant (LTI) followers that are interacting over a directed acyclic graph. Only a subset of the followers has access to the state of the leader in specific sampling times. The dynamics of the leader that generates its sampled states is unknown to the followers. For interaction topologies in which the leader is a global sink in the graph, we propose a distributed algorithm that allows the followers to arrive at the sampled state of the leader by the time the next sample arrives. Our algorithm is a practical solution for a leader-following problem when there is no information available about the state of the leader except its instantaneous value at the sampling times. Our algorithm also allows the followers to track the sampled state of the leader with a locally chosen offset that can be time-varying. When the followers are mobile agents whose state or part of their state is their position vector, the offset mechanism can be used to enable the followers to form a transnational invariant formation about the sampled state of the leader. We prove that the control input of the followers to take them from one sampled state to the next one is minimum energy. We also show in case of the homogeneous followers, after the first sampling epoch the states and inputs of all the followers are synchronized with each other. Numerical examples demonstrate our results.
Stereo Visual Inertial LiDAR Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a fundamental task to mobile and aerial robotics. LiDAR based systems have proven to be superior compared to vision based systems due to its accuracy and robustness. In spite of its superiority, pure LiDAR based systems fail in certain degenerate cases like traveling through a tunnel. We propose Stereo Visual Inertial LiDAR (VIL) SLAM that performs better on these degenerate cases and has comparable performance on all other cases. VIL-SLAM accomplishes this by incorporating tightly-coupled stereo visual inertial odometry (VIO) with LiDAR mapping and LiDAR enhanced visual loop closure. The system generates loop-closure corrected 6-DOF LiDAR poses in real-time and lcm voxel dense maps near real-time. VIL-SLAM demonstrates improved accuracy and robustness compared to state-of-the-art LiDAR methods.
Parallel and nonparallel genomic responses contribute to herbicide resistance in Ipomoea purpurea, a common agricultural weed.

The repeated evolution of herbicide resistance has been cited as an example of genetic parallelism, wherein separate species or genetic lineages utilize the same genetic solution in response to selection. However, most studies that investigate the genetic basis of herbicide resistance examine the potential for changes in the protein targeted by the herbicide rather than considering genome-wide changes. We used a population genomics screen and targeted exome re-sequencing to uncover the potential genetic basis of glyphosate resistance in the common morning glory, Ipomoea purpurea, and to determine if genetic parallelism underlies the repeated evolution of resistance across replicate resistant populations. We found no evidence for changes in 5‐enolpyruvylshikimate‐3‐phosphate synthase (EPSPS), glyphosate’s target protein, that were associated with resistance, and instead identified five genomic regions that showed evidence of selection. Within these regions, genes involved in herbicide detoxification—cytochrome P450s, ABC transporters, and glycosyltransferases—are enriched and exhibit signs of selective sweeps. One region under selection shows parallel changes across all assayed resistant populations whereas other regions exhibit signs of divergence. Thus, while it appears that the physiological mechanism of resistance in this species is likely the same among resistant populations, we find patterns of both similar and divergent selection across separate resistant populations at particular loci.
Strength of species interactions determines biodiversity and stability in microbial communities

Organisms-especially microbes-tend to live together in ecosystems. While some of these ecosystems are very biodiverse, others are not, and while some are very stable over time, others undergo strong temporal fluctuations. Despite a long history of research and a plethora of data, it is not fully understood what determines the biodiversity and stability of ecosystems. Theory and experiments suggest a connection between species interaction, biodiversity and the stability of ecosystems, where an increase in ecosystem stability with biodiversity could be observed in several cases. However, what causes these connections remains unclear. Here, we show in microbial ecosystems in the laboratory that the concentrations of available nutrients can set the strength of interactions between bacteria. High nutrient concentrations allowed the bacteria to strongly alter the chemical environment, causing on average more negative interactions between species. These stronger interactions excluded more species from the community, resulting in a loss of biodiversity. At the same time, the stronger interactions also decreased the stability of the microbial communities, providing a mechanistic link between species interaction, biodiversity and stability in microbial ecosystems.