cssi/SciSciGPT-SciSciNet huggingface dataset:
Distance-weighted Support Vector Machine

A novel linear classification method that possesses the merits of both the Support Vector Machine (SVM) and the Distance-weighted Discrimination (DWD) is proposed in this article. The proposed Distance-weighted Support Vector Machine method can be viewed as a hybrid of SVM and DWD that finds the classification direction by minimizing mainly the DWD loss, and determines the intercept term in the SVM manner. We show that our method inheres the merit of DWD, and hence, overcomes the data-piling and overfitting issue of SVM. On the other hand, the new method is not subject to imbalanced data issue which was a main advantage of SVM over DWD. It uses an unusual loss which combines the Hinge loss (of SVM) and the DWD loss through a trick of axillary hyperplane. Several theoretical properties, including Fisher consistency and asymptotic normality of the DWSVM solution are developed. We use some simulated examples to show that the new method can compete DWD and SVM on both classification performance and interpretability. A real data application further establishes the usefulness of our approach.
A Dynamic I/O Model for TRACON Traffic Management

This work investigates modeling and control of arriving aircraft flows in the vicinity major airports. Aircraft arrival flows are analyzed through a study of TRACON trajectory records for San Francisco International airport. Aircraft trajectory rerouting processes are interpreted from queuing systems perspective: Airport arrival throughput is evaluated as a function of the number of planes present in the TRACON. Based on these observations, an input-output TRACON queuing and landing model is proposed. This model is validated using available TRACON data. It is shown that the model is able to capture the same important behaviors as the physical system. This model is then used to show how regulating the entering flow of aircraft in the TRACON may lead to improved management of TRACON operations while leaving airport throughput essentially unaffected.
The subelliptic heat kernel on the CR sphere

We study the heat kernel of the sub-Laplacian \(L\) on the CR sphere \(\mathbb{S }^{2n+1}\). An explicit and geometrically meaningful formula for the heat kernel is obtained. As a by-product we recover in a simple way the Green function of the conformal sub-Laplacian \(-L+n^2\) that was obtained by Geller (J Differ Geom 15:417–435, 1980), and also get an explicit formula for the sub-Riemannian distance. The key point is to work in a set of coordinates that reflects the symmetries coming from the fibration \(\mathbb{S }^{2n+1} \rightarrow \mathbb{CP }^n\).
Regularity for Fully Nonlinear P-Laplacian Parabolic Systems: the Degenerate Case

This paper studies H\"older regularity property of bounded weak solutions to a class of strongly coupled degenerate parabolic systems.
Iterated index and the mean Euler characteristic

The aim of the paper is three-fold. We begin by proving a formula, both global and local versions, relating the number of periodic orbits of an iterated map and the Lefschetz numbers, or indices in the local case, of its iterations. This formula is then used to express the mean Euler characteristic (MEC) of a contact manifold in terms of local, purely topological, invariants of closed Reeb orbits, without any non-degeneracy assumption on the orbits. Finally, turning to applications of the local MEC formula to dynamics, we use it to reprove a theorem asserting the existence of at least two closed Reeb orbits on the standard contact S3 (by Cristofaro–Gardiner and Hutchings in the most general form) and the existence of at least two closed geodesics for a Finsler metric on S2 (Bangert and Long).
Modeling Progress in AI

Participants in recent discussions of AI-related issues ranging from intelligence explosion to technological unemployment have made diverse claims about the nature, pace, and drivers of progress in AI. However, these theories are rarely specified in enough detail to enable systematic evaluation of their assumptions or to extrapolate progress quantitatively, as is often done with some success in other technological domains. After reviewing relevant literatures and justifying the need for more rigorous modeling of AI progress, this paper contributes to that research program by suggesting ways to account for the relationship between hardware speed increases and algorithmic improvements in AI, the role of human inputs in enabling AI capabilities, and the relationships between different subfields of AI. It then outlines ways of tailoring AI progress models to generate insights on the specific issue of technological unemployment, and outlines future directions for research on AI progress.
Strong solutions for time-dependent mean field games with non-separable Hamiltonians

Abstract We prove existence theorems for strong solutions of time-dependent mean field games with non-separable Hamiltonian. In a recent announcement, we showed existence of small, strong solutions for mean field games with local coupling. We first generalize that prior work to allow for non-separable Hamiltonians. This proof is inspired by the work of Duchon and Robert on the existence of small-data vortex sheets in incompressible fluid mechanics. Our next existence result is in the case of weak coupling of the system; that is, we allow the data to be of arbitrary size, but instead require that the (still possibly non-separable) Hamiltonian be small in a certain sense. The proof of this theorem relies upon an appeal to the implicit function theorem.
Fundamental groups of simplicial complexes

Abstract We define two different simplicial complexes, the common divisor simplicial complex and the prime divisor simplicial complex, from a set of integers, and explore their similarities. We will define a map between the two simplicial complexes, and use this map to show that for any set of integers, the fundamental groups of the resulting simplicial complexes are isomorphic.
Explicit solutions to utility maximization problems in a regime-switching market model via Laplace transforms

Abstract We study the problem of utility maximization from terminal wealth in which an agent optimally builds her portfolio by investing in a bond and a risky asset. The asset price dynamics follow a diffusion process with regime-switching coefficients modeled by a continuous-time finite-state Markov chain. We consider an investor with a Constant Relative Risk Aversion (CRRA) utility function. We deduce the associated Hamilton–Jacobi–Bellman equation to construct the solution and the optimal trading strategy and verify optimality by showing that the value function is the unique constrained viscosity solution of the HJB equation. By means of a Laplace transform method, we show how to explicitly compute the value function and illustrate the method with the two- and three-states cases. This method is interesting in its own right and can be adapted in other applications involving hybrid systems and using other types of transforms with basic properties similar to the Laplace transform.
Analyzing Uplink SINR and Rate in Massive MIMO Systems Using Stochastic Geometry

This paper proposes a stochastic geometry framework to analyze the signal-to-noise-and-interference ratio (SINR) and rate performance in a large-scale uplink massive multiple-input and multiple-output (MIMO) network. Based on the model, expressions are derived for spatial average SINR distributions over user and base station distributions with maximum ratio combining (MRC) and zero-forcing (ZF) receivers. We show that, using massive MIMO, the uplink SINR in certain urban marcocell scenarios is limited by interference. In the interference-limited regime, the results reveal that for MRC receivers, a superlinear (polynomial) scaling law between the number of base station antennas and scheduled users per cell preserves the uplink signal-to-interference ratio (SIR) distribution, while a linear scaling applies to ZF receivers. ZF receivers are shown to outperform MRC receivers in the SIR coverage, and the performance gap is quantified in terms of the difference in the number of antennas to achieve the same SIR distribution. Numerical results verify the analysis. It is found that the optimal compensation fraction in fractional power control to optimize rate is generally different for MRC and ZF receivers. Besides, simulations show that the scaling results derived from the proposed framework apply to the networks, where base stations are distributed according to a hexagonal lattice.
Equidistribution of bounded torsion CM points

Averaging over imaginary quadratic fields, we prove, quantitatively, the equidistribution of CM points associated to 3-torsion classes in the class group. We conjecture that this equidistribution holds for points associated to ideals of any fixed odd order. We prove a partial equidistribution result in this direction and present empirical evidence.
Accelerating Deep Learning with Shrinkage and Recall

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network (DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data sets. Results show that the speedup using shrinking Deep Learning with recall (sDLr) can reach more than 2.0 while still giving competitive classification performance.
Coherent-vorticity Preserving Large-Eddy Simulation of trefoil knotted vortices

We have performed Coherent-vorticity Preserving Large-Eddy simulations of a trefoil knot-shaped vortex, inspired by the experiments of Kleckner and Irvine. The flow parameter space is extended in the present study, including variations of the circulation Reynolds numbers in the range Re = 2000 - 200000, where Re = 20000 is the value used in the experiments. The vortex line corresponding to the trefoil knot is defined using a parametric equation and the Biot-Savart law is employed to initialize the velocity field. The CvP LES computation displays a good qualitative match with the experiment. In particular, the vortex entanglement process is accurately represented as well as the subsequent separation of the main vortex in two distinct structures - a small and a large vortex - with different self-advection speeds that have been quantified. The small vortex propagates faster than the large oscillatory vortex which carries an important amount of vorticity. The advection velocity of the vortex before bursting is found to be independent of the Reynolds number. The low Reynolds number computation leads to a decrease of the separated vortices velocity after bursting, compared to the higher Reynolds computations. The computation of energy spectra emphasizes intense energy transfers from large to small scales during the bursting process. The evolution of volume-averaged enstrophy shows that the bursting leads to the creation of small scales that are sustained a long time in the flow, when a sufficiently large Reynolds number is considered (Re>20000). The low Reynolds number case Re = 2000 hinders the generation of small scales during the bursting process and yields essentially laminar dynamics. The onset of background turbulence due to the entanglement process can be observed at Re = 200000
SMOGS: Social Network Metrics of Game Success

In this paper we propose a novel metric of basketball game success, derived from a team’s dynamic social network of game play. We combine ideas from random effects models for network links with taking a multi-resolution stochastic process approach to model passes between teammates. These passes can be viewed as directed dynamic relational links in a network. Multiplicative latent factors are introduced to study higher-order patterns in players’ interactions that distinguish a successful game from a loss. Parameters are estimated using a Markov chain Monte Carlo sampler. Results in simulation experiments suggest that the sampling scheme is effective in recovering the parameters. We also apply the model to the first high-resolution optical tracking data set collected in college basketball games. The learned latent factors demonstrate significant differences between players’ passing and receiving patterns in a loss, as opposed to a win. Our model is applicable to team sports other than basketball, as well as other time-varying network observations.
Multiplicative valued difference fields

The theory of valued difference fields ( K , σ , υ ,) depends on how the valuation υ interacts with the automorphism σ . Two special cases have already been worked out - the isometric case, where υ ( σ ( x )) = υ ( x ) for all x Є K , has been worked out by Luc Belair, Angus Macintyre and Thomas Scanlon; and the contractive case, where υ ( σ ( x )) > n υ ( x ) for all x Є K × with υ ( x ) > 0 and n Є ℕ, has been worked out by Salih Azgin. In this paper we deal with a more general version, the multiplicative case, where υ ( σ ( x )) = ρ · υ ( x ), where ρ (> 0) is interpreted as an element of a real-closed field. We give an axiomatization and prove a relative quantifier elimination theorem for this theory.
A tunable base station cooperation scheme for poisson cellular networks

We propose a tunable location-dependent base station (BS) cooperation scheme by partitioning the plane into three regions: the cell centers, cell edges and cell corners. The area fraction of each region is tuned by the cooperation level γ ranging from 0 to 1. Depending on the region a user resides in, he/she receives no cooperation, two-BS cooperation or three-BS cooperation. Here, we use a Poisson point process (PPP) to model BS locations and study a non-coherent joint transmission scheme, i.e., selected BSs jointly serve one user in the absence of channel state information (CSI). For the proposed scheme, we examine its performance as a function of the cooperation level using tools from stochastic geometry. We derive an analytical expression for the signal-to-interference ratio (SIR) distribution and its approximation based on the asymptotic SIR gain, along with the characterization of the normalized spectral efficiency per BS. Our result suggests that the proposed scheme with a moderate cooperation level can improve the SIR performance while maintaining the normalized spectral efficiency.
Tame Circle Actions

In this paper, we consider Sjamaar's holomorphic slice theorem, the birational equivalence theorem of Guillemin and Sternberg, and a number of important standard constructions that work for Hamiltonian circle actions in both the symplectic category and the K\"ahler category: reduction, cutting, and blow-up. In each case, we show that the theory extends to Hamiltonian circle actions on complex manifolds with tamed symplectic forms. (At least, the theory extends if the fixed points are isolated.) Our main motivation for this paper is that the first author needs the machinery that we develop here to construct a non-Hamiltonian symplectic circle action on a closed, connected six-dimensional symplectic manifold with exactly 32 fixed points; this answers an open question in symplectic geometry. However, we also believe that the setting we work in is intrinsically interesting, and elucidates the key role played by the following fact: the moment image of $e^t \cdot x$ increases as $t \in \mathbb{R}$ increases.
Log-Plurigenera in Stable Families

We study the flatness of log-pluricanonical sheaves on stable families of varieties. v2: many changes in presentation, results unchanged.
On positive scalar curvature and moduli of curves

In this article we first show that any finite cover of the moduli space of closed Riemann surfaces of genus $g$ with $g \geqslant 2$ does not admit any Riemannian metric $ds^2$ of nonnegative scalar curvature such that ${\lVert \, \cdotp \rVert}_{ds^2} \succ {\lVert \, \cdotp \rVert}_T$ where ${\lVert \, \cdotp \rVert}_T$ is the Teichmuller metric. Our second result is the proof that any cover $M$ of the moduli space $\mathbb{M}_g$ of a closed Riemann surface $S_g$ does not admit any complete Riemannian metric of uniformly positive scalar curvature in the quasi-isometry class of the Teichmuller metric, which implies a conjecture of Farb–Weinberger in [9].
Prescribing the postsingular dynamics of meromorphic functions

We show that any dynamics on any discrete planar sequence S can be realized by the postsingular dynamics of some transcendental meromorphic function, provided we allow for small perturbations of S. This work is motivated by an analogous result of DeMarco et al. (Mathematische Annalen, 2018) for finite S in the rational setting. The proof contains a method for constructing meromorphic functions with good control over both the postsingular set of f and the geometry of f, using the Folding Theorem of Bishop (Acta Math 214(1):1–60, 2015) and a classical fixpoint theorem (Tychonoff in Math Ann 111(1):767–776, 1935).
When your problem becomes my problem: The impact of airline IT disruptions on on‐time performance of competing airlines

Research Summary We study the effect of firm disruptions on competitor performance in the presence of shared resources in the U.S. airline industry. While scholars have investigated both the effects of industry‐wide and firm‐specific disruptions, little work has examined the effect on competitors, who are increasingly reliant on interconnected resources in the digital age. Results from a series of recent information technology (IT) outages indicate that performance is materially affected by a competitor's disruption. While the disruption of a full‐service carrier significantly delays flights of all airlines leveraging its hubs, the exact opposite is observed during the disruption of a low cost carrier. Further, the effect is strongly moderated by the type of airlines reacting to the disruption. Implications for managers and theory are discussed within. Managerial Summary What happens to my operations when a competitor's operations are disrupted? While research has examined how a disrupted firm can recover, little attention has been paid to competitors, except their ability to exploit the disruption for economic gain. This is problematic, as firms increasingly leverage interconnected resources and infrastructure. We show that an airline's IT outages affect on‐time performance of competitors' flights to and from its hub airports. However, the effects depend on both who is disrupted, and who is reacting to that disruption. The disruptions of full‐service carriers (FSCs) delay competitors' flights, but that of a low‐cost carrier (LCC) leads to early arrivals and departures. Further, LCCs are significantly more nimble reacting to disruptions compared to FSCs.
Near-infrared spectroscopy for prediction of extubation success after neonatal cardiac surgery.

INTRODUCTION Reliable predictors of extubation readiness are needed and may reduce morbidity related to extubation failure. We aimed to examine the relationship between changes in pre-extubation near-infrared spectroscopy measurements from baseline and extubation outcomes after neonatal cardiac surgery. MATERIALS AND METHODS In this retrospective cross-sectional multi-centre study, a secondary analysis of prospectively collected data from neonates who underwent cardiac surgery at seven tertiary-care children's hospitals in 2015 was performed. Extubation failure was defined as need for re-intubation within 72 hours of the first planned extubation attempt. Near-infrared spectroscopy measurements obtained before surgery and before extubation in patients who failed extubation were compared to those of patients who extubated successfully using t-tests. RESULTS Near-infrared spectroscopy measurements were available for 159 neonates, including 52 with single ventricle physiology. Median age at surgery was 6 days (range: 1-29 days). A total of 15 patients (9.4 %) failed extubation. Baseline cerebral and renal near-infrared spectroscopy measurements were not statistically different between those who were successfully extubated and those who failed, but pre-extubation cerebral and renal values were significantly higher in neonates who extubated successfully. An increase from baseline to time of extubation values in cerebral oximetry saturation by ≥ 5 % had a positive predictive value for extubation success of 98.6 % (95%CI: 91.1-99.8 %). CONCLUSION Pre-extubation cerebral near-infrared spectroscopy measurements, when compared to baseline, were significantly associated with extubation outcomes. These findings demonstrate the potential of this tool as a valuable adjunct in assessing extubation readiness after paediatric cardiac surgery and warrant further evaluation in a larger prospective study.
Diet-induced Pigmented Purpuric Dermatosis Confirmed with a Rechallenge Response.

The pigmented purpuric dermatoses (PPDs) are a group of chronic cutaneous eruptions characterized by non-blanching and non-palpable purpuric lesions. Their etiology is not completely understood, although dietary exposures have been implicated in a few case reports. We describe a recurring case of diet-induced PPD in a 73-year-old Caucasian male following the ingestion of tomato-based products on two separate occasions, one year apart. On physical examination, he demonstrated numerous 1-2 mm red/brown, non-blanching, petechial macules scattered on the bilateral anterior lower legs, thighs, trunk, arms, hands, and feet with facial sparing. Histopathologic examination revealed the classic perivascular lymphocytic infiltrate with red blood cell extravasation seen in PPD. Mirroring his first episode, the patient saw a complete resolution of his rashes with careful avoidance of tomato-based products and required no other interventions. This represents a rare case of diet-induced PPD confirmed with a rechallenge response and suggests that acute or recurrent cases of PPD may be a result of a hypersensitivity reaction.
Optimizing Robustness against Cascading Failures under Max-Load Targeted Attack

Motivated by reliability of networks in critical infrastructures, we consider optimal robustness of a class of flow networks against a targeted attack, namely max-load targeted attack, that triggers cascading failures due to removal of largest load carrying portion of lines. The setup involves a network of $N$ lines with initial loads $L_{1}, \ldots, L_{N}$ , drawn from independent and identical uniform distribution, and redundancies $S_{1}, \ldots, S_{N}$ to be allocated. In the failure propagation mechanism, a line fails initially due to attack and later due to overloading. The load that was carried at the moment of failing gets redistributed equally among all remaining lines in the system. We analyze robustness of this network against the max-load targeted attack that removes the largest load carrying $p$ -fraction of the lines from the system. The system designer allocates $S_{i}$ as a stochastic function of the load in each line. Assuming an average resource budget, we show that allocating all lines the free-spaces equally among nodes is optimal under some regulatory assumptions. We provide numerical results verifying that equal free-space allocation to all lines performs optimally in more general targeted attack scenarios.
Do financial reforms promote entrepreneurship

Abstract This paper investigates whether financial reforms promote entrepreneurship. Using a panel of 41 developed and developing countries from around the world, we find that financial sector reforms are positively associated with early-stage entrepreneurial activity. In a variety of robustness checks, including a falsification test, we fail to find the evidence that this relationship is driven due to the omission of unobserved, country-specific factors. Investigating the relationship between reforms in different dimensions of the financial sector and entrepreneurship, we find reforms in directed credit, credit controls, banking supervision, and international capital flows dimensions to be significantly associated with early-stage entrepreneurial activity.
Compressed and Penalized Linear Regression

Modern applications require methods that are computationally feasible on large datasets while retaining good statistical properties. Recent work has focused on developing fast and randomized approx...
Simultaneous multi-area recordings suggest that attention improves performance by reshaping stimulus representations

Visual attention dramatically improves individuals’ ability to see and modulates the responses of neurons in every known visual and oculomotor area, but whether such modulations can account for perceptual improvements is unclear. We measured the relationship between populations of visual neurons, oculomotor neurons and behavior during detection and discrimination tasks. We found that neither of the two prominent hypothesized neuronal mechanisms underlying attention (which concern changes in information coding and the way sensory information is read out) provide a satisfying account of the observed behavioral improvements. Instead, our results are more consistent with the hypothesis that attention reshapes the representation of attended stimuli to more effectively influence behavior. Our results suggest a path toward understanding the neural underpinnings of perception and cognition in health and disease by analyzing neuronal responses in ways that are constrained by behavior and interactions between brain areas. Ruff and Cohen find that the prominent hypotheses about how attention improves perception do not account for behavioral improvements. Instead, their results suggest that attention reshapes sensory representations so the relevant information guides behavior.
Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning

In this paper we present an online wide-area oscillation damping control (WAC) design for uncertain models of power systems using ideas from reinforcement learning. We assume that the exact small-signal model of the power system at the onset of a contingency is not known to the operator and use the nominal model and online measurements of the generator states and control inputs to rapidly converge to a state-feedback controller that minimizes a given quadratic energy cost. However, unlike conventional linear quadratic regulators (LQR), we intend our controller to be sparse, so its implementation reduces the communication costs. We, therefore, employ the gradient support pursuit (GraSP) optimization algorithm to impose sparsity constraints on the control gain matrix during learning. The sparse controller is then implemented using distributed communication. The proposed method is validated using the IEEE 39-bus power system model with 1149 unknown parameters.
Tactical Rewind: Self-Correction via Backtracking in Vision-And-Language Navigation

We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the 2018 Room-to-Room (R2R) Vision-and-Language navigation challenge. Given a natural language instruction and photo-realistic image views of a previously unseen environment, the agent was tasked with navigating from source to target location as quickly as possible. While all current approaches make local action decisions or score entire trajectories using beam search, ours balances local and global signals when exploring an unobserved environment. Importantly, this lets us act greedily but use global signals to backtrack when necessary. Applying FAST framework to existing state-of-the-art models achieved a 17% relative gain, an absolute 6% gain on Success rate weighted by Path Length.
Walking Cadence Affects the Recruitment of the Medial-Lateral Balance Mechanisms.

We have previously identified three balance mechanisms that young healthy adults use to maintain balance while walking. The three mechanisms are: (1) The lateral ankle mechanism, an active modulation of ankle inversion/eversion in stance; (2) The foot placement mechanism, an active shift of the swing foot placement; and (3) The push-off mechanism, an active modulation of the ankle plantarflexion angle during double stance. Here we seek to determine whether there are changes in neural control of balance when walking at different cadences and speeds. Twenty-one healthy young adults walked on a self-paced treadmill while immersed in a 3D virtual reality cave, and periodically received balance perturbations (bipolar galvanic vestibular stimulation) eliciting a perceived fall to the side. Subjects were instructed to match two cadences specified by a metronome, 110 bpm (High) and 80 bpm (Low), which in this experiment, led to faster and slower gait speeds, respectively. The results indicate that subjects altered the use of the balance mechanisms at different cadences. The lateral ankle mechanism was used more in the Low condition, while the foot placement mechanism was used more in the High condition. There was no difference in the use of the push-off mechanism between cadence conditions. These results suggest that neural control of balance is altered when gait characteristics, such as cadence change, suggesting a flexible balance response that is sensitive to the constraints of the gait cycle. We speculate that the use of the balance mechanisms may be a factor resulting in well-known characteristics of gait in populations with compromised balance control, such as slower gait speed in older adults or higher cadence in people with Parkinson's disease.
Improved Algorithms for Time Decay Streams

In the time-decay model for data streams, elements of an underlying data set arrive sequentially with the recently arrived elements being more important. A common approach for handling large data sets is to maintain a coreset, a succinct summary of the processed data that allows approximate recovery of a predetermined query. We provide a general framework that takes any offline-coreset and gives a time-decay coreset for polynomial time decay functions. We also consider the exponential time decay model for k-median clustering, where we provide a constant factor approximation algorithm that utilizes the online facility location algorithm. Our algorithm stores O(k log(h Delta)+h) points where h is the half-life of the decay function and Delta is the aspect ratio of the dataset. Our techniques extend to k-means clustering and M-estimators as well.
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks

Opioids are the preferred medications for the treatment of pain in the intensive care unit. While under-treatment leads to unrelieved pain and poor clinical outcomes, excessive use of opioids puts patients at risk of experiencing multiple adverse effects. In this work, we present a sequential decision making framework for opioid dosing based on deep reinforcement learning. It provides real-time clinically interpretable dosing recommendations, personalized according to each patient’s evolving pain and physiological condition. We focus on morphine, one of the most commonly prescribed opioids. To train and evaluate the model, we used retrospective data from the publicly available MIMIC-3 database. Our results demonstrate that reinforcement learning may be used to aid decision making in the intensive care setting by providing personalized pain management interventions.
A meta-analysis of two high-risk prospective cohort studies reveals autism-specific transcriptional changes to chromatin, autoimmune, and environmental response genes in umbilical cord blood.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects more than 1% of children in the USA. ASD risk is thought to arise from both genetic and environmental factors, with the perinatal period as a critical window. Understanding early transcriptional changes in ASD would assist in clarifying disease pathogenesis and identifying biomarkers. However, little is known about umbilical cord blood gene expression profiles in babies later diagnosed with ASD compared to non-typically developing and non-ASD (Non-TD) or typically developing (TD) children. Genome-wide transcript levels were measured by Affymetrix Human Gene 2.0 array in RNA from cord blood samples from both the Markers of Autism Risk in Babies-Learning Early Signs (MARBLES) and the Early Autism Risk Longitudinal Investigation (EARLI) high-risk pregnancy cohorts that enroll younger siblings of a child previously diagnosed with ASD. Younger siblings were diagnosed based on assessments at 36 months, and 59 ASD, 92 Non-TD, and 120 TD subjects were included. Using both differential expression analysis and weighted gene correlation network analysis, gene expression between ASD and TD, and between Non-TD and TD, was compared within each study and via meta-analysis. While cord blood gene expression differences comparing either ASD or Non-TD to TD did not reach genome-wide significance, 172 genes were nominally differentially expressed between ASD and TD cord blood (log2(fold change) > 0.1, p < 0.01). These genes were significantly enriched for functions in xenobiotic metabolism, chromatin regulation, and systemic lupus erythematosus (FDR q < 0.05). In contrast, 66 genes were nominally differentially expressed between Non-TD and TD, including 8 genes that were also differentially expressed in ASD. Gene coexpression modules were significantly correlated with demographic factors and cell type proportions. ASD-associated gene expression differences identified in this study are subtle, as cord blood is not the main affected tissue, it is composed of many cell types, and ASD is a heterogeneous disorder. This is the first study to identify gene expression differences in cord blood specific to ASD through a meta-analysis across two prospective pregnancy cohorts. The enriched gene pathways support involvement of environmental, immune, and epigenetic mechanisms in ASD etiology.
Hypothalamic C2-domain protein involved in MC4R trafficking and control of energy balance.

Abstract Objective Rates of overweight and obesity epidemic have risen significantly in the past few decades, and 34% of adults and 15–20% of children and adolescents in the United States are now obese. Melanocortin receptor 4 (MC4R), contributes to appetite control in hypothalamic neurons and is a target for future anti-obesity treatments (such as setmelanotide) or novel drug development effort. Proper MC4R trafficking regulation in hypothalamic neurons is crucial for normal neural control of homeostasis and is altered in obesity and in presence of lipids. The mechanisms underlying altered MC4R trafficking in the context of obesity is still unclear. Here, we discovered that C2CD5 expressed in the hypothalamus is involved in the regulation of MC4R endocytosis. This study unmasked a novel trafficking protein nutritionally regulated in the hypothalamus providing a novel target for MC4R dependent pathways involved in bodyweight homeostasis and Obesity. Methods To evaluate the expression of C2cd5, we first used in situ hybridization and RNAscope technology in combination with electronic microscopy. For in vivo, we characterized the energy balance of wild type (WT) and C2CD5 whole-body knockout (C2CD5KO) mice fed normal chow (NC) and/or western-diet (high-fat/high-sucrose/cholesterol) (WD). To this end, we performed comprehensive longitudinal assessment of bodyweight, energy balance (food intake, energy expenditure, locomotor activity using TSE metabolic cages), and glucose homeostasis. In addition, we evaluated the consequence of loss of C2CD5 on feeding behavior changes normally induced by MC4R agonist (Melanotan, MTII) injection in the paraventricular hypothalamus (PVH). For in vitro approach, we tease out the role of C2CD5 and its calcium sensing domain C2 in MC4R trafficking. We focused on endocytosis of MC4R using an antibody feeding experiment (in a neuronal cell line - Neuro2A (N2A) stably expressing HA-MC4R-GFP; against HA-tag and analyzed by flux cytometry). Results We found that 1) the expression of hypothalamic C2CD5 is decreased in diet-induced obesity models compared to controls, 2) mice lacking C2CD5 exhibit an increase in food intake compared to WT mice, 3) C2CD5 interacts with endocytosis machinery in hypothalamus, 4) loss of functional C2CD5 (lacking C2 domain) blunts MC4R endocytosis in vitro and increases MC4R at the surface that fails to respond to MC4R ligand, and, 5) C2CD5KO mice exhibit decreased acute responses to MTII injection into the PVH. Conclusions Based on these, we conclude that hypothalamic C2CD5 is involved in MC4R endocytosis and regulate bodyweight homeostasis. These studies suggest that C2CD5 represents a new protein regulated by metabolic cues and involved in metabolic receptor endocytosis. C2CD5 represent a new target and pathway that could be targeted in Obesity.
Secure trajectory planning against undetectable spoofing attacks

Abstract This paper studies, for the first time, the trajectory planning problem in adversarial environments, where the objective is to design the trajectory of a robot to reach a desired final state despite the unknown and arbitrary action of an attacker. In particular, we consider a robot moving in a two-dimensional space and equipped with two sensors, namely, a Global Navigation Satellite System (GNSS) sensor and a Radio Signal Strength Indicator (RSSI) sensor. The attacker can arbitrarily spoof the readings of the GNSS sensor and the robot control input so as to maximally deviate its trajectory from the nominal precomputed path. We derive explicit and constructive conditions for the existence of undetectable attacks, through which the attacker deviates the robot trajectory in a stealthy way. Conversely, we characterize the existence of secure trajectories, which guarantee that the robot either moves along the nominal trajectory or that attacks remain detectable. We show that secure trajectories can only exist between a subset of states, and provide a numerical technique to compute them. We illustrate our findings through several numerical studies, and show that our methods are applicable to different models of robot dynamics, including unicycles. More generally, our results show how control design affects security in systems with nonlinear dynamics.
Audit market concentration, audit fees, and audit quality: A cross-country analysis of complex audit clients

Abstract Regulators around the world are concerned about the potentially harmful effects of high audit market concentration on audit pricing and quality. However, results in the overall literature have failed to reach consensus on this issue. We contribute to this debate by arguing that the audit market is segmented and that concentration in the Big 4 segment of the market leads to higher audit pricing. Accordingly, our analyses use international data and focus on concentration within the Big 4 group of firms across countries. We find that audit fees are increasing in our concentration measure for clients where the barriers to entry by competing auditors are higher, as proxied by client size, international operations, and IFRS use. Finally, we find evidence that audit quality is decreasing in Big 4 market concentration for these types of engagements. This indicates a wealth transfer from shareholders to audit firms when auditor concentration is high because these complex clients are charged more, but receive audits that are of lower quality.
Reliable and explainable machine-learning methods for accelerated material discovery

Despite ML’s impressive performance in commercial applications, several unique challenges exist when applying ML in materials science applications. In such a context, the contributions of this work are twofold. First, we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material data. Specifically, we show that with imbalanced data, standard methods for assessing quality of ML models break down and lead to misleading conclusions. Furthermore, we find that the model’s own confidence score cannot be trusted and model introspection methods (using simpler models) do not help as they result in loss of predictive performance (reliability-explainability trade-off). Second, to overcome these challenges, we propose a general-purpose explainable and reliable machine-learning framework. Specifically, we propose a generic pipeline that employs an ensemble of simpler models to reliably predict material properties. We also propose a transfer learning technique and show that the performance loss due to models’ simplicity can be overcome by exploiting correlations among different material properties. A new evaluation metric and a trust score to better quantify the confidence in the predictions are also proposed. To improve the interpretability, we add a rationale generator component to our framework which provides both model-level and decision-level explanations. Finally, we demonstrate the versatility of our technique on two applications: (1) predicting properties of crystalline compounds and (2) identifying potentially stable solar cell materials. We also point to some outstanding issues yet to be resolved for a successful application of ML in material science.
Some scalar curvature warped product splitting theorems

We present several rigidity results for Riemannian manifolds $(M^n,g)$ with scalar curvature $S \ge -n(n-1)$ (or $S\ge 0$), and having compact boundary $N$ satisfying a related mean curvature inequality. The proofs make use of results on marginally outer trapped surfaces applied to appropriate initial data sets. One of the results involves an analysis of Obata's equation on manifolds with boundary. This result is relevant to recent work of Lan-Hsuan Huang and the second author concerning the rigidity of asymptotically locally hyperbolic manifolds with zero mass.
Summary of WWW characterizations

To date there have been a number of efforts that attempt to characterize various aspects of the World Wide Web. This paper presents a summary of these efforts, highlighting regularities and insights that have been discovered across the variety of access points available for instrumentation. Characterizations that are derived from client, proxy, and server instrumentation are reviewed as well as efforts to characterize the entire structure of the WWW. Given the dynamic nature of the Web, it may be surprising for some readers to find that many properties of the Web follow regular and predictable patterns that have not changed in form over the Web’s lifetime. Understanding these aspects as well as those that vary is critical to designing a better Web, and as a direct consequence, creating a more enjoyable user experience.
The I/O complexity of hybrid algorithms for square matrix multiplication

Asymptotically tight lower bounds are derived for the I/O complexity of a general class of hybrid algorithms computing the product of $n \times n$ square matrices combining ``\emph{Strassen-like}'' fast matrix multiplication approach with computational complexity $\Theta{n^{\log_2 7}}$, and ``\emph{standard}'' matrix multiplication algorithms with computational complexity $\Omega\left(n^3\right)$. We present a novel and tight $\Omega\left(\left(\frac{n}{\max\{\sqrt{M},n_0\}}\right)^{\log_2 7}\left(\max\{1,\frac{n_0}{M}\}\right)^3M\right)$ lower bound for the I/O complexity a class of ``\emph{uniform, non-stationary}'' hybrid algorithms when executed in a two-level storage hierarchy with $M$ words of fast memory, where $n_0$ denotes the threshold size of sub-problems which are computed using standard algorithms with algebraic complexity $\Omega\left(n^3\right)$. The lower bound is actually derived for the more general class of ``\emph{non-uniform, non-stationary}'' hybrid algorithms which allow recursive calls to have a different structure, even when they refer to the multiplication of matrices of the same size and in the same recursive level, although the quantitative expressions become more involved. Our results are the first I/O lower bounds for these classes of hybrid algorithms. All presented lower bounds apply even if the recomputation of partial results is allowed and are asymptotically tight. The proof technique combines the analysis of the Grigoriev's flow of the matrix multiplication function, combinatorial properties of the encoding functions used by fast Strassen-like algorithms, and an application of the Loomis-Whitney geometric theorem for the analysis of standard matrix multiplication algorithms. Extensions of the lower bounds for a parallel model with $P$ processors are also discussed.
Numerical Approximations for the Variable Coefficient Fractional Diffusion Equations with Non-smooth Data

In this article we study the numerical approximation of a variable coefficient fractional diffusion equation. Using a change of variable, the variable coefficient fractional diffusion equation is transformed into a constant coefficient fractional diffusion equation of the same order. The transformed equation retains the desirable stability property of being an elliptic equation. A spectral approximation scheme is proposed and analyzed for the transformed equation, with error estimates for the approximated solution derived. An approximation to the unknown of the variable coefficient fractional diffusion equation is then obtained by post processing the computed approximation to the transformed equation. Error estimates are also presented for the approximation to the unknown of the variable coefficient equation with both smooth and non-smooth diffusivity coefficient and right-hand side. Three numerical experiments are given whose convergence results are in strong agreement with the theoretically derived estimates.
Deep sampling of Hawaiian Caenorhabditis elegans reveals high genetic diversity and admixture with global populations

Hawaiian isolates of the nematode species Caenorhabditis elegans have long been known to harbor genetic diversity greater than the rest of the worldwide population, but this observation was supported by only a small number of wild strains. To better characterize the niche and genetic diversity of Hawaiian C. elegans and other Caenorhabditis species, we sampled different substrates and niches across the Hawaiian islands. We identified hundreds of new Caenorhabditis strains from known species and a new species, Caenorhabditis oiwi. Hawaiian C. elegans are found in cooler climates at high elevations but are not associated with any specific substrate, as compared to other Caenorhabditis species. Surprisingly, admixture analysis revealed evidence of shared ancestry between some Hawaiian and non-Hawaiian C. elegans strains. We suggest that the deep diversity we observed in Hawaii might represent patterns of ancestral genetic diversity in the C. elegans species before human influence.
Vortex simulations on a 3-sphere

Author(s): Dix, OM; Zieve, RJ | Abstract: We generate vortex tangles using a Hopf flow on a 3-sphere, in place of the standard torus defined by periodic boundary conditions. These tangles are highly anisotropic, with vortices tending to align along the flow direction. Standard power law dependences change accordingly from their values in more isotropic tangles. The line length density $\langle L\rangle$ is proportional to $v_{ns}^{1.28}$, where $v_{ns}$ is the drive velocity, and the reconnection rate depends roughly on $\langle L\rangle^2$. We also discuss the effect of the full Biot-Savart law versus the local induction approximation (LIA). Under LIA the tangle collapses so that all vortices are nearly aligned with a single flow line, in sharp contrast to the torus where they become perpendicular to the driving velocity. Finally we present a few torus simulations with a helical velocity field, which in some ways resembles the 3-sphere flow.