cssi/SciSciGPT-SciSciNet huggingface dataset:
Selective network discovery via deep reinforcement learning on embedded spaces

Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algorithms optimized for specific downstream learning tasks given resource collection constraints are of great interest. In this paper, we formulate the task-specific network discovery problem as a sequential decision-making problem. Our downstream task is selective harvesting, the optimal collection of vertices with a particular attribute. We propose a framework, called network actor critic (NAC), which learns a policy and notion of future reward in an offline setting via a deep reinforcement learning algorithm. The NAC paradigm utilizes a task-specific network embedding to reduce the state space complexity. A detailed comparative analysis of popular network embeddings is presented with respect to their role in supporting offline planning. Furthermore, a quantitative study is presented on various synthetic and real benchmarks using NAC and several baselines. We show that offline models of reward and network discovery policies lead to significantly improved performance when compared to competitive online discovery algorithms. Finally, we outline learning regimes where planning is critical in addressing sparse and changing reward signals.
User testing of a diagnostic decision support system with machine-assisted chart review to facilitate clinical genomic diagnosis.

Objectives There is a need in clinical genomics for systems that assist in clinical diagnosis, analysis of genomic information and periodic reanalysis of results, and can use information from the electronic health record to do so. Such systems should be built using the concepts of human-centred design, fit within clinical workflows and provide solutions to priority problems. Methods We adapted a commercially available diagnostic decision support system (DDSS) to use extracted findings from a patient record and combine them with genomic variant information in the DDSS interface. Three representative patient cases were created in a simulated clinical environment for user testing. A semistructured interview guide was created to illuminate factors relevant to human factors in CDS design and organisational implementation. Results Six individuals completed the user testing process. Tester responses were positive and noted good fit with real-world clinical genetics workflow. Technical issues related to interface, interaction and design were minor and fixable. Testers suggested solving issues related to terminology and usability through training and infobuttons. Time savings was estimated at 30%–50% and additional uses such as in-house clinical variant analysis were suggested for increase fit with workflow and to further address priority problems. Conclusion This study provides preliminary evidence for usability, workflow fit, acceptability and implementation potential of a modified DDSS that includes machine-assisted chart review. Continued development and testing using principles from human-centred design and implementation science are necessary to improve technical functionality and acceptability for multiple stakeholders and organisational implementation potential to improve the genomic diagnosis process.
Weak Convergence of Topological Measures

Topological measures and deficient topological measures are defined on open and closed subsets of a topological space, generalize regular Borel measures, and correspond to (nonlinear in general) functionals that are linear on singly generated subalgebras or singly generated cones of functions. They lack subadditivity, and many standard techniques of measure theory and functional analysis do not apply to them. Nevertheless, we show that many classical results of probability theory hold for topological and deficient topological measures. In particular, we prove a version of Aleksandrov’s theorem for equivalent definitions of weak convergence of deficient topological measures. We also prove a version of Prokhorov’s theorem which relates the existence of a weakly convergent subsequence in any sequence in a family of topological measures to the characteristics of being a uniformly bounded in variation and uniformly tight family. We define Prokhorov and Kantorovich–Rubenstein metrics and show that convergence in either of them implies weak convergence of (deficient) topological measures on metric spaces. We also generalize many known results about various dense and nowhere dense subsets of deficient topological measures. The present paper constitutes a necessary step to further research in probability theory and its applications in the context of (deficient) topological measures and corresponding nonlinear functionals.
A constrained risk inequality for general losses

We provide a general constrained risk inequality that applies to arbitrary non-decreasing losses, extending a result of Brown and Low [Ann. Stat. 1996]. Given two distributions $P_0$ and $P_1$, we find a lower bound for the risk of estimating a parameter $\theta(P_1)$ under $P_1$ given an upper bound on the risk of estimating the parameter $\theta(P_0)$ under $P_0$. The inequality is a useful pedagogical tool, as its proof relies only on the Cauchy-Schwartz inequality, it applies to general losses, and it transparently gives risk lower bounds on super-efficient and adaptive estimators.
Synthesis of Data Completion Scripts using Finite Tree Automata

In application domains that store data in a tabular format, a common task is to fill the values of some cells using values stored in other cells. For instance, such data completion tasks arise in the context of \emph{missing value imputation} in data science and \emph{derived data} computation in spreadsheets and relational databases. Unfortunately, end-users and data scientists typically struggle with many data completion tasks that require non-trivial programming expertise. This paper presents a synthesis technique for automating data completion tasks using \emph{programming-by-example (PBE)} and a very lightweight sketching approach. Given a \emph{formula sketch} (e.g., {\texttt{AVG}}($\texttt{?}_1$, $\texttt{?}_2$)) and a few input-output examples for each hole, our technique synthesizes a program to automate the desired data completion task. Towards this goal, we propose a domain-specific language (DSL) that combines spatial and relational reasoning over tabular data and a novel synthesis algorithm that can generate DSL programs that are consistent with the input-output examples. The key technical novelty of our approach is a new version space learning algorithm that is based on \emph{finite tree automata} (FTA). The use of FTAs in the learning algorithm leads to a more compact representation that allows more sharing between programs that are consistent with the examples. We have implemented the proposed approach in a tool called \textsc{DACE} and evaluate it on 84 benchmarks taken from online help forums. We also illustrate the advantages of our approach by comparing our technique against two existing synthesizers, namely Prose and Sketch.
Rare event process and entry times distribution for arbitrary null sets on compact manifolds

We establish the general equivalence between rare event process for arbitrary continuous functions whose maximal values are achieved on non-trivial sets, and the entry times distribution for arbitrary measure zero sets. We then use it to show that for differentiable maps on a compact Riemannian manifold that can be modeled by Young’s towers, the rare event process and the limiting entry times distribution both converge to compound Poisson distributions. A similar result is also obtained on Gibbs–Markov systems, for both cylinders and open sets. We also give explicit expressions for the parameters of the limiting distribution, and a simple criterion for the limiting distribution to be Poisson. This can be applied to a large family of continuous observables that achieve their maximum on a non-trivial set with zero measure.
Shannon Entropy Rate of Hidden Markov Processes

Hidden Markov chains are widely applied statistical models of stochastic processes, from fundamental physics and chemistry to finance, health, and artificial intelligence. The hidden Markov processes they generate are notoriously complicated, however, even if the chain is finite state: no finite expression for their Shannon entropy rate exists, as the set of their predictive features is generically infinite. As such, to date one cannot make general statements about how random they are nor how structured. Here, we address the first part of this challenge by showing how to efficiently and accurately calculate their entropy rates. We also show how this method gives the minimal set of infinite predictive features. A sequel addresses the challenge’s second part on structure.
Does Interaction Improve Bayesian Reasoning with Visualization

Interaction enables users to navigate large amounts of data effectively, supports cognitive processing, and increases data representation methods. However, there have been few attempts to empirically demonstrate whether adding interaction to a static visualization improves its function beyond popular beliefs. In this paper, we address this gap. We use a classic Bayesian reasoning task as a testbed for evaluating whether allowing users to interact with a static visualization can improve their reasoning. Through two crowdsourced studies, we show that adding interaction to a static Bayesian reasoning visualization does not improve participants’ accuracy on a Bayesian reasoning task. In some cases, it can significantly detract from it. Moreover, we demonstrate that underlying visualization design modulates performance and that people with high versus low spatial ability respond differently to different interaction techniques and underlying base visualizations. Our work suggests that interaction is not as unambiguously good as we often believe; a well designed static visualization can be as, if not more, effective than an interactive one.
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control

Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications. In this paper, the vulnerabilities of such DRL agents to adversarial attacks is studied. In particular, we consider multiple DRL agents that perform both dynamic channel access and power control in wireless interference channels. For these victim DRL agents, we design a jammer, which is also a DRL agent. We Propose an adversarial jamming attack scheme that utilizes a listening phase and significantly degrades the users’ sum rate. Subsequently, we develop an ensemble policy defense strategy against such a jamming atta.ck.er by reloa.di.ng models (saved during retraining) that have minimum transition correlation.
The energy-spectrum of bicompatible sequences

Genotype-phenotype maps provide a meaningful filtration of sequence space and RNA secondary structures are particular such phenotypes. Compatible sequences, which satisfy the base-pairing constraints of a given RNA structure, play an important role in the context of neutral evolution. Sequences that are simultaneously compatible with two given structures (bicompatible sequences), are beacons in phenotypic transitions, induced by erroneously replicating populations of RNA sequences. RNA riboswitches, which are capable of expressing two distinct secondary structures without changing the underlying sequence, are one example of bicompatible sequences in living organisms. We present a full loop energy model Boltzmann sampler of bicompatible sequences for pairs of structures. The sequence sampler employs a dynamic programming routine whose time complexity is polynomial when assuming the maximum number of exposed vertices, $$\kappa $$ , is a constant. The parameter $$\kappa $$ depends on the two structures and can be very large. We introduce a novel topological framework encapsulating the relations between loops that sheds light on the understanding of $$\kappa $$ . Based on this framework, we give an algorithm to sample sequences with minimum $$\kappa $$ on a particular topologically classified case as well as giving hints to the solution in the other cases. As a result, we utilize our sequence sampler to study some established riboswitches. Our analysis of riboswitch sequences shows that a pair of structures needs to satisfy key properties in order to facilitate phenotypic transitions and that pairs of random structures are unlikely to do so. Our analysis observes a distinct signature of riboswitch sequences, suggesting a new criterion for identifying native sequences and sequences subjected to evolutionary pressure. Our free software is available at: https://github.com/FenixHuang667/Bifold .
Pushforwards of klt pairs under morphisms to abelian varieties

Let f be a morphism from a klt pair $$(X, \Delta )$$ to an abelian variety A, $$m\ge 1$$ a rational number and D a Cartier divisor on X such that $$D\sim _{{{\mathbb {Q}}}}m(K_X+\Delta )$$ . We prove that the sheaf $$f_*\mathcal {O}_X(D)$$ becomes globally generated after pullback by an isogeny and has the Chen–Jiang decomposition, along with some related results. These are applied to some effective results for $$\mathcal {O}_X(D)$$ when X is irregular.
A Tn-seq Screen of Streptococcus pneumoniae Uncovers DNA Repair as the Major Pathway for Desiccation Tolerance and Transmission.

Streptococcus pneumoniae is an opportunistic pathogen that is a common cause of serious invasive diseases such as pneumonia, bacteremia, meningitis, and otitis media. Transmission of this bacterium has classically been thought to occur through inhalation of respiratory droplets and direct contact with nasal secretions. However, the demonstration that S. pneumoniae is desiccation tolerant, and therefore environmentally stable for extended periods of time, opens up the possibility that this pathogen is also transmitted via contaminated surfaces (fomites). To better understand the molecular mechanisms that enable S. pneumoniae to survive periods of desiccation, we performed a high-throughput transposon sequencing (Tn-seq) screen in search of genetic determinants of desiccation tolerance. We identified 42 genes whose disruption reduced desiccation tolerance, and 45 genes that enhanced desiccation tolerance. The nucleotide excision repair pathway was the most enriched category in our Tn-seq results, and we found that additional DNA repair pathways are required for desiccation tolerance, demonstrating the importance of maintaining genome integrity after desiccation. Deletion of the nucleotide excision repair gene uvrA resulted in a delay in transmission between infant mice, indicating a correlation between desiccation tolerance and pneumococcal transmission. Understanding the molecular mechanisms that enable pneumococcal persistence in the environment may enable targeting of these pathways to prevent fomite transmission, thereby preventing the establishment of new colonization and any resulting invasive disease.
Mycobacterium Abscessus HelR Interacts with RNA Polymerase to Confer Intrinsic Rifamycin Resistance

Bacterial RNA polymerase is a target of the potent and broad-spectrum rifamycin group of antibiotics. Mutations within rpoB and inactivation by a diverse group of enzymes constitute the most widespread mechanisms of resistance. Rifampicin (RIF) constitutes the frontline therapy against M. tuberculosis as well as most slow-growing non-tuberculous mycobacteria (NTM) but is completely ineffective against M. abscessus. This high level of RIF resistance is despite the absence of mutations in the rifampicin resistance determining region of Mab_rpoB and is partially attributed to the presence of an ADP-ribosyltransferase (Arr) activity that inactivates RIF. Rifabutin (RBT), a close analogue of RIF, has recently been shown to be effective against M. abscessus in vitro and in a mouse model and comprises a promising therapeutic against M. abscessus infections. Using RNA sequencing we show that exposure of M. abscessus to sublethal doses of RIF and RBT results in ~25-fold upregulation of Mab_ helR in laboratory and clinical isolates; an isogenic deletion mutant of Mab_ helR is hypersensitive to RIF and RBT, and over-expression of Mab_ helR confers RIF tolerance in M. tuberculosis implying that Mab_helR constitutes a significant determinant of inducible RIF and RBT resistance. We demonstrate a preferential association of MabHelR with RNA polymerase in vivo in bacteria exposed to RIF and show that purified MabHelR can rescue transcription inhibition in the presence of RIF in in vitro transcription assays. Furthermore, MabHelR can dissociate RNAP from RIF-stalled initiation complexes in vitro, a species we envisage accumulates upon RIF exposure. Lastly, we show that the tip of the PCh-loop of Mab_ helR, in particular residues E496 and D497 that are in proximity to RIF, is critical for conferring RIF resistance without being required for dissociation of antibiotic-stalled RNAP complexes. This suggests that HelR may be additionally involved in displacing RIF bound to RNAP and function as an RNAP protection protein.
Variations in photoreceptor throughput to mouse visual cortex and the unique effects on tuning.

Visual input to primary visual cortex (V1) depends on highly adaptive filtering in the retina. In turn, isolation of V1 computations requires experimental control of retinal adaptation to infer its spatio-temporal-chromatic output. Here, we measure the balance of input to mouse V1, in the anesthetized setup, from the three main photoreceptor opsins—M-opsin, S-opsin, and rhodopsin—as a function of two stimulus dimensions. The first dimension is the level of light adaptation within the mesopic range, which governs the balance of rod and cone inputs to cortex. The second stimulus dimension is retinotopic position, which governs the balance of S- and M-cone opsin input due to the opsin expression gradient in the retina. The fitted model predicts opsin input under arbitrary lighting environments, which provides a much-needed handle on in-vivo studies of the mouse visual system. We use it here to reveal that V1 is rod-mediated in common laboratory settings yet cone-mediated in natural daylight. Next, we compare functional properties of V1 under rod and cone-mediated inputs. The results show that cone-mediated V1 responds to 2.5-fold higher temporal frequencies than rod-mediated V1. Furthermore, cone-mediated V1 has smaller receptive fields, yet similar spatial frequency tuning. V1 responses in rod-deficient (Gnat1−/−) mice confirm that the effects are due to differences in photoreceptor opsin contribution.
Quantitative phosphoproteomics uncovers dysregulated kinase networks in Alzheimer’s disease

Alzheimer’s disease (AD) is a form of dementia characterized by amyloid-β plaques and tau neurofibrillary tangles that progressively disrupt neural circuits in the brain. The signaling networks underlying AD pathological changes are poorly characterized at the phosphoproteome level. Using mass spectrometry, we analyzed the proteome and tyrosine, serine and threonine phosphoproteomes of temporal cortex tissue from patients with AD and aged-matched controls. We identified cocorrelated peptide clusters that were linked to varying levels of phospho-tau, oligodendrocyte, astrocyte, microglia and neuron pathologies. We found that neuronal synaptic protein abundances were strongly anti-correlated with markers of microglial reactivity. We also observed that phosphorylation sites on kinases targeting tau and other new signaling factors were correlated with these peptide modules. Finally, we used data-driven statistical modeling to identify individual peptides and peptide clusters that were predictive of AD histopathologies. Together, these results build a map of pathology-associated phosphorylation signaling events occurring in AD. Morshed et al. analyzed the proteome and phosphoproteome of brain tissue from patients with Alzheimer’s disease. The analysis of patient heterogeneity links glia pathologies and signaling pathways to stages of disease progression.
Searching for waveforms on spatially-filtered epileptic ECoG

Seizures are one of the defining symptoms in patients with epilepsy, and due to their unannounced occurrence, they can pose a severe risk for the individual that suffers it. New research efforts are showing a promising future for the prediction and preemption of imminent seizures, and with those efforts, a vast and diverse set of features have been proposed for seizure prediction algorithms. However, the data-driven discovery of nonsinusoidal waveforms for seizure prediction is lacking in the literature, which is in stark contrast with recent works that show the close connection between the waveform morphology of neural oscillations and the physiology and pathophysiology of the brain, and especially its use in effectively discriminating between normal and abnormal oscillations in electrocorticographic (ECoG) recordings of epileptic patients. Here, we explore a scalable, energy-guided waveform search strategy on spatially-projected continuous multi-day ECoG data sets. Our work shows that data-driven waveform learning methods have the potential to not only contribute features with predictive power for seizure prediction, but also to facilitate the discovery of oscillatory patterns that could contribute to our understanding of the pathophysiology and etiology of seizures.
Message Latency Characterization of a Distributed Live, Virtual, Constructive Simulation Environment

A distributed test environment incorporating Live, Virtual, Constructive, (LVC) concepts was developed to execute standalone and integrated simulations and flight-tests that support unmanned aircraft research for NASA’s Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) Project. The LVC components form the core infrastructure that supports simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This LVC infrastructure enables efficient testing by leveraging the use of existing distributed assets. The LVC concepts used for the UAS in the NAS project include live aircraft, flight simulators, and virtual air traffic control assets operating at facilities distributed across multiple NASA Centers. With a distributed network, however, there is a concern that message latency could impact the realism of a simulation and its data. The latencies associated with sending data among these distributed facilities were, therefore, measured to ensure that they fall within acceptable parameters. Several live and virtual test assets were integrated into the LVC infrastructure including NASA Armstrong’s Ikhana MQ-9 unmanned aircraft, NASA Glenn’s S3-B manned aircraft, and the B747 flight simulator at NASA Ames. Average latencies from 100 to 150 milliseconds were observed between the LVC System running at NASA Ames and each of the participating NASA Centers under a light-to-moderate (fifty aircraft) traffic sample.
Weather Impact on Airport Arrival Meter Fix Throughput

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.
Novices' Learning Barriers When Using Code Examples in Open-Ended Programming

Open-ended programming increases students' motivation by allowing them to solve authentic problems and connect programming to their own interests. However, such open-ended projects are also challenging, as they often encourage students to explore new programming features and attempt tasks that they have not learned before. Code examples are effective learning materials for students and are well-suited to supporting open-ended programming. However, there is little work to understand how novices learn with examples during open-ended programming, and few real-world deployments of such tools. In this paper, we explore novices' learning barriers when interacting with code examples during open-ended programming. We deployed Example Helper, a tool that offers galleries of code examples to search and use, with 44 novice students in an introductory programming classroom, working on an open-ended project in Snap. We found three high-level barriers that novices encountered when using examples: decision, search, and integration barriers. We discuss how these barriers arise and design opportunities to address them.
A Neural Network for the Identification of Measured Helicopter Noise

The results of a preliminary study of the components of a novel acoustic helicopter identification system are described. The identification system uses the relationship between the amplitudes of the first eight harmonics in the main rotor noise spectrum to distinguish between helicopter types. Two classification algorithms are tested; a statistically optimal Bayes classifier, and a neural network adaptive classifier. The performance of these classifiers is tested using measured noise of three helicopters. The statistical classifier can correctly identify the helicopter an average of 67 percent of the time, while the neural network is correct an average of 65 percent of the time. These results indicate the need for additional study of the envelope of harmonic amplitudes as a component of a helicopter identification system. Issues concerning the implementation of the neural network classifier, such as training time and structure of the network, are discussed.
Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos

A key challenge of learning the geometry of dressed humans lies in the limited availability of the ground truth data (e.g., 3D scanned models), which results in the performance degradation of 3D human reconstruction when applying to real-world imagery. We address this challenge by leveraging a new data resource: a number of social media dance videos that span diverse appearance, clothing styles, performances, and identities. Each video depicts dynamic movements of the body and clothes of a single person while lacking the 3D ground truth geometry. To utilize these videos, we present a new method to use the local transformation that warps the predicted local geometry of the person from an image to that of another image at a different time instant. This allows self-supervision as enforcing a temporal coherence over the predictions. In addition, we jointly learn the depth along with the surface normals that are highly responsive to local texture, wrinkle, and shade by maximizing their geometric consistency. Our method is end-to-end trainable, resulting in high fidelity depth estimation that predicts fine geometry faithful to the input real image. We demonstrate that our method outperforms the state-of-the-art human depth estimation and human shape recovery approaches on both real and rendered images.
De Novo Identification and Visualization of Important Cell Populations for Classic Hodgkin Lymphoma Using Flow Cytometry and Machine Learning.

Objectives Automated classification of flow cytometry data has the potential to reduce errors and accelerate flow cytometry interpretation. We desired a machine learning approach that is accurate, is intuitively easy to understand, and highlights the cells that are most important in the algorithm's prediction for a given case. Methods We developed an ensemble of convolutional neural networks for classification and visualization of impactful cell populations in detecting classic Hodgkin lymphoma using two-dimensional (2D) histograms. Data from 977 and 245 clinical flow cytometry cases were used for training and testing, respectively. Seventy-eight nongated 2D histograms were created per flow cytometry file. Shapley additive explanation (SHAP) values were calculated to determine the most impactful 2D histograms and regions within histograms. SHAP values from all 78 histograms were then projected back to the original cell data for gating and visualization using standard flow cytometry software. Results The algorithm achieved 67.7% recall (sensitivity), 82.4% precision, and 0.92 area under the receiver operating characteristic. Visualization of the important cell populations for individual predictions demonstrated correlations with known biology. Conclusions The method presented enables model explainability while highlighting important cell populations in individual flow cytometry specimens, with potential applications in both diagnosis and discovery of previously overlooked key cell populations.
An incidence loss model for wave rotors with axially aligned passages

A simple mathematical model is described to account for the losses incurred when the flow in the duct (port) of a wave rotor is not aligned with the passages. The model, specifically for wave rotors with axially aligned passages, describes a loss mechanism which is sensitive to incident flow angle and Mach number. Implementation of the model in a one-dimensional CFD based wave rotor simulation is presented. Comparisons with limited experimental results are consistent with the model. Sensitivity studies are presented which highlight the significance of the incidence loss relative to other loss mechanisms in the wave rotor.
Multidimensional Uncertainty-Aware Evidential Neural Networks.

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.
Potential Early Markets for Fusion Energy

We examine potential early markets for fusion energy and their projected cost targets, based on analysis and synthesis of many relevant, recent studies and reports. Seeking to provide guidance to ambitious fusion developers aspiring to enable commercial deployment before 2040, we examine cost requirements for fusion-generated electricity, process heat, and hydrogen production based on today’s market prices but with various adjustments relating to possible scenarios in 2035, such as “business-as-usual,” high renewables penetration, and carbon pricing up to 100 $/ $$\hbox {tCO}_2$$ . Key findings are that fusion developers should consider focusing initially on high-priced global electricity markets and consider including integrated thermal storage, depending on techno-economic factors, in order to maximize revenue and compete in markets with high renewables penetration. Process heat and hydrogen production will be tough early markets for fusion, but may open up to fusion as markets evolve and if fusion’s levelized cost of electricity falls below 50 $/ $$\hbox {MWh}_{\mathrm {e}}$$ . Finally, we discuss potential ways for a fusion plant to increase revenue via cogeneration (e.g., desalination, direct air capture, or district heating) and to lower capital costs (e.g., by minimizing construction times and interest or by retrofitting coal plants).
Naturally light Dirac neutrinos from SU(6)

A known mechanism for obtaining naturally light Dirac neutrinos is implemented in the context of SU(6) → SU(5) × U(1)N.
From Proper Scoring Rules to Max-Min Optimal Forecast Aggregation

This paper forges a strong connection between two seemingly unrelated forecasting problems: incentive-compatible forecast elicitation and forecast aggregation. Proper scoring rules are the well-known solution to the former problem. To each such rule s we associate a corresponding method of aggregation, mapping expert forecasts and expert weights to a "consensus forecast," which we call quasi-arithmetic (QA) pooling with respect to s. We justify this correspondence in several ways: QA pooling with respect to the two most well-studied scoring rules (quadratic and logarithmic) corresponds to the two most well-studied forecast aggregation methods (linear and logarithmic). Given a scoring rule s used for payment, a forecaster agent who sub-contracts several experts, paying them in proportion to their weights, is best off aggregating the experts' reports using QA pooling with respect to s, meaning this strategy maximizes its worst-case profit (over the possible outcomes). The score of an aggregator who uses QA pooling is concave in the experts' weights. As a consequence, online gradient descent can be used to learn appropriate expert weights from repeated experiments with low regret. The class of all QA pooling methods is characterized by a natural set of axioms (generalizing classical work by Kolmogorov on quasi-arithmetic means).
Airline revenue management around sporting mega-events: an application using data from the Super Bowl XLIX

Using air travel ticketing data for the travel dates around the 2015 Super Bowl in Phoenix, AZ, we exploit the exogenous shock created by the National Football Leagure conference championship round results and design a quasi-natural experiment to identify market-specific airfare and booking patterns. Our findings help quantify the impact on the resolution in uncertainty by measuring the changes in airfare and bookings, before and after the conference championship games, in the airline markets of finalist teams and teams that lost in the conference championship round. This empirical evidence provides important insights to airlines.
SCN2A contributes to oligodendroglia excitability and development in the mammalian brain.

Summary Spiking immature oligodendrocytes (OLs), referred to as spiking OLs, express voltage-activated Na+ channels (Nav) and K+ (Kv) channels, endowing a subpopulation of OLs with the ability to generate Nav-driven spikes. In this study, we investigate the molecular profile of spiking OLs, using single-cell transcriptomics paired with whole-cell patch-clamp recordings. SCN2A, which encodes the channel Nav1.2, is specifically expressed in spiking OLs in the brainstem and cerebellum, both in mice and in Olive baboons. Spiking OLs express lineage markers of OL progenitor cells (OPCs) and pre-myelinating OLs, indicating they belong to a transitional stage during differentiation. Deletion of SCN2A reduces the Nav current-expressing OL population and eliminates spiking OLs, indicating that SCN2A is essential for spiking in OLs. Deletion of SCN2A does not impact global OL proliferation but disrupts maturation of a subpopulation of OLs, suggesting that Nav1.2 is involved in heterogeneity in OL lineage cells and their development.
Separability of Hermitian tensors and PSD decompositions

Hermitian tensors are natural generalizations of Hermitian matrices, while possessing rather different properties. A Hermitian tensor is separable if it has a Hermitian decomposition with only posi...
LT-LiMn0.5Ni0.5O2: a unique co-free cathode for high energy Li-ion cells.

A novel LiMn0.5Ni0.5O2 cathode with a predominant, partially-disordered lithiated-spinel structure has been prepared by a ‘low temperature’ (LT) synthesis. Li/LT-LiMn0.5Ni0.5O2 cells operate between 5.0 and 2.5 V with good cycling stability, yielding a capacity of 225 mA h g−1, principally by redox reactions on the nickel ions on distinct voltage plateaus at ∼3.6 V and ∼4.6 V.
Realistic finite-temperature effects in neutron star merger simulations

Binary neutron star mergers provide a unique probe of the dense-matter equation of state (EoS) across a wide range of parameter space, from the zero-temperature EoS during the inspiral to the high-temperature EoS following the merger. In this paper, we implement a new model for calculating parametrized finite-temperature EoS effects into numerical relativity simulations. This ``${M}^{*}$-model'' is based on a two-parameter approximation of the particle effective mass and includes the leading-order effects of degeneracy in the thermal pressure and energy. We test our numerical implementation by performing evolutions of rotating single stars with zero- and nonzero temperature gradients, as well as evolutions of binary neutron star mergers. We find that our new finite-temperature EoS implementation can support stable stars over many dynamical timescales. We also perform a first parameter study to explore the role of the ${M}^{*}$ parameters in binary neutron star merger simulations. All simulations start from identical initial data with identical cold EoSs, and differ only in the thermal part of the EoS. We find that both the thermal profile of the remnant and the postmerger gravitational wave signal depend on the choice of ${M}^{*}$ parameters, but that the total merger ejecta depends only weakly on the finite-temperature part of the EoS across a wide range of parameters. Our simulations provide a first step toward understanding how the finite-temperature properties of dense matter may affect future observations of binary neutron star mergers.
Complexity and randomness in the Heisenberg groups (and beyond)

By studying the commuting graphs of conjugacy classes of the sequence of Heisenberg groups $H_{2n+1}(p)$ and their limit $H_\infty(p)$ we find pseudo-random behavior (and the random graph in the limiting case). This makes a nice case study for transfer of information between finite and infinite objects. Some of this behavior transfers to the problem of understanding what makes understanding the character theory of the uni-upper-triangular group (mod p) “wild.” Our investigations in this paper may be seen as a meditation on the question: is randomness simple or is it complicated?
Suppressing STAT3 activity protects the endothelial barrier from VEGF-mediated vascular permeability.

Vascular permeability triggered by inflammation or ischemia promotes edema, exacerbates disease progression, and impairs tissue recovery. Vascular endothelial growth factor (VEGF) is a potent inducer of vascular permeability. VEGF plays an integral role in regulating vascular barrier function physiologically and in pathologies, including cancer, stroke, cardiovascular disease, retinal conditions, and COVID-19-associated pulmonary edema, sepsis, and acute lung injury. Understanding temporal molecular regulation of VEGF-induced vascular permeability will facilitate developing therapeutics to inhibit vascular permeability, while preserving tissue-restorative angiogenesis. Here, we demonstrate that VEGF signals through signal transducer and activator of transcription 3 (STAT3) to promote vascular permeability. We show that genetic STAT3 ablation reduces vascular permeability in STAT3-deficient endothelium of mice and VEGF-inducible zebrafish crossed with CRISPR/Cas9 generated Stat3 knockout zebrafish. Intercellular adhesion molecule 1 (ICAM-1) expression is transcriptionally regulated by STAT3 and VEGF-dependent STAT3 activation is regulated by JAK2. Pyrimethamine, an FDA-approved anti-microbial agent that inhibits STAT3-dependent transcription, substantially reduces VEGF-induced vascular permeability in zebrafish, mouse, and human endothelium. Collectively, our findings suggest that VEGF/VEGFR-2/JAK2/STAT3 signaling regulates vascular barrier integrity, and inhibition of STAT3-dependent activity reduces VEGF-induced vascular permeability.
Wake up and Join Me! an Energy-Efficient Algorithm for Maximal Matching in Radio Networks

We consider networks of small, autonomous devices that communicate with each other wirelessly. Minimizing energy usage is an important consideration in designing algorithms for such networks, as battery life is a crucial and limited resource. Working in a model where both sending and listening for messages deplete energy, we consider the problem of finding a maximal matching of the nodes in a radio network of arbitrary and unknown topology. We present a distributed randomized algorithm that produces, with high probability, a maximal matching. The maximum energy cost per node is O(log² n), and the time complexity is O(Δ log n). Here n is any upper bound on the number of nodes, and Δ is any upper bound on the maximum degree; n and Δ are parameters of our algorithm that we assume are known a priori to all the processors. We note that there exist families of graphs for which our bounds on energy cost and time complexity are simultaneously optimal up to polylog factors, so any significant improvement would need additional assumptions about the network topology. We also consider the related problem of assigning, for each node in the network, a neighbor to back up its data in case of eventual node failure. Here, a key goal is to minimize the maximum load, defined as the number of nodes assigned to a single node. We present an efficient decentralized low-energy algorithm that finds a neighbor assignment whose maximum load is at most a polylog(n) factor bigger that the optimum.
Symmetries of stochastic colored vertex models

Author(s): Galashin, Pavel | Abstract: We discover a new property of the stochastic colored six-vertex model called flip-invariance. We use it to show that for a given collection of observables of the model, any transformation that preserves the distribution of each individual observable also preserves their joint distribution. This generalizes recent shift-invariance results of Borodin-Gorin-Wheeler. As limiting cases, we obtain similar statements for the Brownian last passage percolation, the Kardar-Parisi-Zhang equation, the Airy sheet, and directed polymers. Our proof relies on an equivalence between the stochastic colored six-vertex model and the Yang-Baxter basis of the Hecke algebra. We conclude by discussing the relationship of the model with Kazhdan-Lusztig polynomials and positroid varieties in the Grassmannian.
Using voltammetry augmented with physics-based modeling and Bayesian hypothesis testing to identify analytes in electrolyte solutions

Abstract Voltammetry is a foundational electrochemical technique that can qualitatively and quantitatively probe electroactive species in solutions and as such has been used in numerous fields of study. Recently, automation has been introduced to extend the capabilities of voltammetric analysis through approaches such as Bayesian parameter estimation and compound identification. However, opportunities exist to enable more versatile methods across a wider range of solution compositions and experimental conditions. Here, we present a protocol that uses experimental voltammetry, physics-driven models, binary hypothesis testing, and Bayesian inference to enable robust labeling of analytes in multicomponent solutions across multiple techniques. We first describe the development of this protocol, and we subsequently validate the methodology in a case study involving five N-functionalized phenothiazine derivatives. In this analysis, the protocol correctly labeled solutions each containing 10H-phenothiazine and 10-methylphenothiazine from both cyclic voltammograms and cyclic square wave voltammograms, demonstrating the ability to identify redox-active constituents of a multicomponent solution. Finally, we identify areas of further improvement—such as achieving greater detection accuracy—and future applications to potentially enhance in situ or operando diagnostic workflows.
Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators

We study how stochastic differential equation (SDE) based ideas can inspire new modifications to existing algorithms for a set of problems in computer vision. Loosely speaking, our formulation is related to both explicit and implicit strategies for data augmentation and group equivariance, but is derived from new results in the SDE literature on estimating infinitesimal generators of a class of stochastic processes. If and when there is nominal agreement between the needs of an application/task and the inherent properties and behavior of the types of processes that we can efficiently handle, we obtain a very simple and efficient plug-in layer that can be incorporated within any existing network architecture, with minimal modification and only a few additional parameters. We show promising experiments on a number of vision tasks including few shot learning, point cloud transformers and deep variational segmentation obtaining efficiency or performance improvements.
Design Paradigms Based on Spring Agonists for Underactuated Robot Hands: Concepts and Application

In this paper, we focus on a rarely used paradigm in the design of underactuated robot hands: the use of springs as agonists and tendons as antagonists. We formalize this approach in a design matrix also considering its interplay with the underactuation method used (one tendon for multiple joints vs. multiple tendons on one motor shaft). We then show how different cells in this design matrix can be combined in order to facilitate the implementation of desired postural synergies with a single motor. Furthermore, we show that when agonist and antagonist tendons are combined on the same motor shaft, the resulting spring force cancellation can be leveraged to produce multiple desirable behaviors, which we demonstrate in a physical prototype.