cssi/SciSciGPT-SciSciNet huggingface dataset:
Interactions of fungi with concrete: Significant importance for bio-based self-healing concrete

Abstract The goal of this study is to explore a new self-healing concept in which fungi are used as a self-healing agent to promote calcium mineral precipitation to fill the cracks in concrete. An initial screening of different species of fungi has been conducted. Fungal growth medium was overlaid onto cured concrete plate. Mycelial discs were aseptically deposited at the plate center. The results showed that, due to the dissolving of Ca(OH)2 from concrete, the pH of the growth medium increased from its original value of 6.5 to 13.0. Despite the drastic pH increase, Trichoderma reesei (ATCC13631) spores germinated into hyphal mycelium and grew equally well with or without concrete. X-ray diffraction (XRD) and scanning electron microscope (SEM) confirmed that the crystals precipitated on the fungal hyphae were composed of calcite. These results indicate that T. reesei has great potential to be used in bio-based self-healing concrete for sustainable infrastructure.
Optimal Power Flow with Disjoint Prohibited Zones: New Formulation and Solutions

The constraints induced by prohibited zones (PZs) were traditionally formulated as multiple disjoint regions. It was difficult to solve the optimal power flow (OPF) problems subject to the disjoint constraints. This paper proposes a new formulation for the OPF problem with PZs. The proposed formulation significantly expedites the algorithm implementation. The effectiveness of the new approach is verified by different methods including traditional optimization methods, PSO and particle swarm optimization with adaptive parameter control which is conducted on the IEEE 30-bus test system.
The Dirichlet problem for degenerate complex Monge–Ampere equations

The Dirichlet problem for a Monge–Ampere equation corresponding to a non-negative, possible degenerate cohomology class on a Kahler manifold with boundary is studied. C1,α estimates away from a divisor are obtained, by combining techniques of Blocki, Tsuji, Yau and pluripotential theory. In particular, C1,α geodesic rays in the space of Kahler potentials are constructed for each test configuration.
Channels of energy for the linear radial wave equation

Abstract Exterior channel of energy estimates for the radial wave equation were first considered in three dimensions in [6] , and for the 5-dimensional case in [12] . In this paper we find the general form of the channel of energy estimate in all odd dimensions for the radial free wave equation. This will be used in the companion paper [11] to establish the soliton resolution for equivariant wave maps in R 3 exterior to the ball B ( 0 , 1 ) and in all equivariance classes.
Reasoning About Fine-Grained Attribute Phrases Using Reference Games

We present a framework for learning to describe finegrained visual differences between instances using attribute phrases. Attribute phrases capture distinguishing aspects of an object (e.g., “propeller on the nose” or “door near the wing” for airplanes) in a compositional manner. Instances within a category can be described by a set of these phrases and collectively they span the space of semantic attributes for a category. We collect a large dataset of such phrases by asking annotators to describe several visual differences between a pair of instances within a category. We then learn to describe and ground these phrases to images in the context of a reference game between a speaker and a listener. The goal of a speaker is to describe attributes of an image that allows the listener to correctly identify it within a pair. Data collected in a pairwise manner improves the ability of the speaker to generate, and the ability of the listener to interpret visual descriptions. Moreover, due to the compositionality of attribute phrases, the trained listeners can interpret descriptions not seen during training for image retrieval, and the speakers can generate attribute-based explanations for differences between previously unseen categories. We also show that embedding an image into the semantic space of attribute phrases derived from listeners offers 20% improvement in accuracy over existing attributebased representations on the FGVC-aircraft dataset.
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit

Performing the inference step of deep learning in resource constrained environments, such as embedded devices, is challenging. Success requires optimization at both software and hardware levels. Low precision arithmetic and specifically low precision fixed-point number systems have become the standard for performing deep learning inference. However, representing non-uniform data and distributed parameters (e.g. weights) by using uniformly distributed fixed-point values is still a major drawback when using this number system. Recently, the posit number system was proposed, which represents numbers in a non-uniform manner. Therefore, in this paper we are motivated to explore using the posit number system to represent the weights of Deep Convolutional Neural Networks. However, we do not apply any quantization techniques and hence the network weights do not require re-training. The results of this exploration show that using the posit number system outperformed the fixed point number system in terms of accuracy and memory utilization.
Security Considerations for Galois Non-dual RLWE Families

We explore further the hardness of the non-dual discrete variant of the Ring-LWE problem for various number rings, give improved attacks for certain rings satisfying some additional assumptions, construct a new family of vulnerable Galois number fields, and apply some number theoretic results on Gauss sums to deduce the likely failure of these attacks for 2-power cyclotomic rings and unramified moduli.
User Point Processes in Cellular Networks

The point process of concurrent users is critical for the analysis of cellular systems, in particular for the uplink and for full-duplex communication. We analyze the properties of two popular models. For the first one, we provide an accurate characterization of the pair correlation functions from the user and the base station point of view, which are applied to approximate the user process by Poisson and Ginibre point processes. For the second model, which includes the first model asymptotically, we study the cell vacancy probability, the mean area of vacant and occupied cells, the user-base station distance, and the pair correlation function in lightly and heavily loaded regimes.
Cascaded Boundary Regression for Temporal Action Detection

Temporal action detection in long videos is an important problem. State-of-the-art methods address this problem by applying action classifiers on sliding windows. Although sliding windows may contain an identifiable portion of the actions, they may not necessarily cover the entire action instance, which would lead to inferior performance. We adapt a two-stage temporal action detection pipeline with Cascaded Boundary Regression (CBR) model. Class-agnostic proposals and specific actions are detected respectively in the first and the second stage. CBR uses temporal coordinate regression to refine the temporal boundaries of the sliding windows. The salient aspect of the refinement process is that, inside each stage, the temporal boundaries are adjusted in a cascaded way by feeding the refined windows back to the system for further boundary refinement. We test CBR on THUMOS-14 and TVSeries, and achieve state-of-the-art performance on both datasets. The performance gain is especially remarkable under high IoU thresholds, e.g. map@tIoU=0.5 on THUMOS-14 is improved from 19.0% to 31.0%.
Time‐Dependent Development of the Coulomb Gap

We show that the time development of the Coulomb gap in a Coulomb glass can involve very long relaxation times due to electron rearrangement and hopping. We find that an applied magnetic field reduces the rate of electron hopping and, hence, Coulomb gap formation. These results are consistent with recent conductance experiments on thin semiconducting and metallic films.
Charge qubits and limitations of electrostatic quantum gates

We investigate quantum dot arrays and their application to quantum computation. The arrays analyzed contain a total of a few operating electrons with constant tunneling between the dots. We construct quantum two-level systems near the ground state with a large energy separation to the remainder of the states and with the electrostatic interaction modeled within the capacitance matrix formalism. A set of representative examples is investigated numerically.
Generators for Decompositions of Tensor Products of Modules associated with standard Jordan partitions

ABSTRACTIf K is a field of finite characteristic p, G is a cyclic group of order q = pα, U and W are indecomposable KG-modules with dim U = m and dim W = n, and λ(m,n,p) is a standard Jordan partition of mn, we describe how to find a generator for each of the indecomposable components of the KG-module U ⊗ W.
Spectral Networks and Locally Connected Networks on Graphs

Abstract: Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain. In this paper we consider possible generalizations of CNNs to signals defined on more general domains without the action of a translation group. In particular, we propose two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian. We show through experiments that for low-dimensional graphs it is possible to learn convolutional layers with a number of parameters independent of the input size, resulting in efficient deep architectures.
Exact common information

This paper introduces the notion of exact common information, which is the minimum description length of the common randomness needed for the exact distributed generation of two correlated random variables $(X,Y)$. We introduce the quantity $G(X;Y)=\min_{X\to W \to Y} H(W)$ as a natural bound on the exact common information and study its properties and computation. We then introduce the exact common information rate, which is the minimum description rate of the common randomness for the exact generation of a 2-DMS $(X,Y)$. We give a multiletter characterization for it as the limit $\bar{G}(X;Y)=\lim_{n\to \infty}(1/n)G(X^n;Y^n)$. While in general $\bar{G}(X;Y)$ is greater than or equal to the Wyner common information, we show that they are equal for the Symmetric Binary Erasure Source. We do not know, however, if the exact common information rate has a single letter characterization in general.
Approximate Clustering via Metric Partitioning

In this paper we consider two metric covering/clustering problems - Minimum Cost Covering Problem (MCC) and k-clustering. In the MCC problem, we are given two point sets X (clients) and Y (servers), and a metric on X cup Y. We would like to cover the clients by balls centered at the servers. The objective function to minimize is the sum of the alpha-th power of the radii of the balls. Here alpha geq 1 is a parameter of the problem (but not of a problem instance). MCC is closely related to the k-clustering problem. The main difference between k-clustering and MCC is that in k-clustering one needs to select k balls to cover the clients. For any eps > 0, we describe quasi-polynomial time (1 + eps) approximation algorithms for both of the problems. However, in case of k-clustering the algorithm uses (1 + eps)k balls. Prior to our work, a 3^alpha and a c^alpha approximation were achieved by polynomial-time algorithms for MCC and k-clustering, respectively, where c > 1 is an absolute constant. These two problems are thus interesting examples of metric covering/clustering problems that admit (1 + eps)-approximation (using (1 + eps)k balls in case of k-clustering), if one is willing to settle for quasi-polynomial time. In contrast, for the variant of MCC where alpha is part of the input, we show under standard assumptions that no polynomial time algorithm can achieve an approximation factor better than O(log |X|) for alpha geq log |X|.
A geometric attractor mechanism for self-organization of entorhinal grid modules

In a room, we have a sense of our location relative to the doors and to objects within the room. This is because the brain constructs a mental map of our current environment. As we move around the room, neurons called grid cells fire whenever we are in specific locations. But these locations are not random. They correspond to the corners of a grid of tessellating triangles on the floor, a little like the dots in a regular polka-dot pattern. Grid cells fire whenever we stand on one of the dots. This enables the brain to keep track of where we are and where we are heading. But the brain does not use just a single grid cell map to represent a room. Instead, it uses multiple maps with different spatial scales. These maps differ in the distance between the points at which each grid cell fires, that is, the distance between the polka dots. Some maps have many small triangles, providing high resolution spatial information. Others have fewer, larger triangles. This is similar to how we use maps with different spatial scales when driving between cities versus walking around a single neighborhood. A set of grid cell maps with the same spatial scale—and the same orientation—is known as a grid cell module. Animal experiments suggest that different individuals use a similar combination of grid cell modules that can efficiently map rooms. But how can the brain reliably produce this particular combination? Using a computer model to simulate networks of grid cells, Kang and Balasubramanian identify a mechanism that enables the brain to spontaneously organize into the previously observed combination. The interactions between networks—in particular the balance of inhibitory and excitatory activity—determine the arrangement of grid cell modules. This process still works even with random fluctuations in network activity. Grid cells occupy a brain region that degenerates early in the course of Alzheimer's disease. This may explain why some patients experience difficulty finding their way around as one of their first symptoms. To develop effective treatments, scientists need to understand how neural circuits within this brain region work, and how the disease process disrupts them. The computer model of Kang and Balasubramanian brings the research community a step closer to achieving this. It also provides insights into how neuronal networks self-organize, which is relevant to other brain functions too.
GIGANTEA recruits the UBP12 and UBP13 deubiquitylases to regulate accumulation of the ZTL photoreceptor complex

ZEITLUPE (ZTL), a photoreceptor with E3 ubiquitin ligase activity, communicates end-of-day light conditions to the plant circadian clock. It still remains unclear how ZTL protein accumulates in the light but does not destabilize target proteins before dusk. Two deubiquitylating enzymes, UBIQUITIN-SPECIFIC PROTEASE 12 and 13 (UBP12 and UBP13), which regulate clock period and protein ubiquitylation in a manner opposite to ZTL, associate with the ZTL protein complex. Here we demonstrate that the ZTL interacting partner, GIGANTEA (GI), recruits UBP12 and UBP13 to the ZTL photoreceptor complex. We show that loss of UBP12 and UBP13 reduces ZTL and GI protein levels through a post-transcriptional mechanism. Furthermore, a ZTL target protein is unable to accumulate to normal levels in ubp mutants. This demonstrates that the ZTL photoreceptor complex contains both ubiquitin-conjugating and -deconjugating enzymes, and that these two opposing enzyme types are necessary for circadian clock pacing. This shows that deubiquitylating enzymes are a core element of circadian clocks, conserved from plants to animals.
Fast Implementation of Volumetric Occupancy Grids

Mapping vehicle surroundings using an occupancy grid is commonly a performance bottleneck in many automotive applications. In particular, three dimensional map generation becomes extremely complex because of the additional degree of freedom that significantly increases the amount of occupancy grid cells to be examined. In this work, we present a novel way to speed up generation of volumetric occupancy grids, by approximating heavy calculations and utilizing efficient data structures. Our proposed method produces a IS-fold speedup in a high-resolution mode and demonstrates realtime performance, without discernible impact on the accuracy. A description of the algorithm and the data structures is provided. The algorithm is tested on a multi-sensor system in a challenging environment. Moreover, our design is portable and could suit many platforms, including embedded automotive hardware.
Mix-dependent job scheduling—An application of hybrid simulation

In a computer system, scheduling occurs whenever the next-task-to-receive-service is selected from a queue of waiting tasks. 5 This paper considers the scheduling of jobs which will become active; that is, which will begin to compete for use of the system processors. Jobs waiting to be activated may be scheduled by referring to properties of the job such as priority or total resource usage. 4,7 It is also possible to examine some feature of the jobs already activated (i.e., the jobs in the multiprogramming mix) in order to determine a suitable candidate for admission to the mix. This is called mix-dependent scheduling. Here, we prefer to examine some intrinsic property of the jobs rather than an external one. The property we have chosen to investigate is the rate of processor (CPU or I/O) usage.
A bigenic mouse model of FSGS reveals perturbed pathways in podocytes, mesangial cells and endothelial cells

Focal segmental glomerulosclerosis is a major cause of end stage renal disease. Many patients prove unresponsive to available therapies. An improved understanding of the molecular basis of the disease process could provide insights leading to novel therapeutic approaches. In this study we carried out an RNA-seq analysis of the altered gene expression patterns of podocytes, mesangial cells and glomerular endothelial cells of the bigenic Cd2ap+/-, Fyn-/- mutant mouse model of FSGS. In the podocytes we observed upregulation of many genes related to the Tgfβ family/pathway, including Gdnf, Tgfβ1, Tgfβ2, Snai2, Vegfb, Bmp4, and Tnc. The mutant podocytes also showed upregulation of Acta2, a marker of smooth muscle and associated with myofibroblasts, which are implicated in driving fibrosis. GO analysis of the podocyte upregulated genes identified elevated protein kinase activity, increased expression of growth factors, and negative regulation of cell adhesion, perhaps related to the observed podocyte loss. Both podocytes and mesangial cells showed strong upregulation of aldehyde dehydrogenase genes involved in the synthesis of retinoic acid. Similarly, the Cd2ap+/-, Fyn-/- mesangial cells, as well as podocytes in other genetic models, and the glomeruli of human FSGS patients, all show upregulation of the serine protease Prss23, with the common thread suggesting important functionality. Another gene with strong upregulation in the Cd2ap+/-, Fyn-/- mutant mesangial cells as well as multiple other mutant mouse models of FSGS was thrombospondin, which activates the secreted inactive form of Tgfβ. The Cd2ap+/-, Fyn-/- mutant endothelial cells showed elevated expression of genes involved in cell proliferation, angioblast migration, angiogenesis, and neovasculature, all consistent with the formation of new blood vessels in the diseased glomerulus. The resulting global definition of the perturbed molecular pathways in the three major cell types of the mutant glomerulus provide deeper understanding of the molecular pathogenic pathways.
Laser pulse waveform control of Dirac fermions in graphene

We theoretically study the Dirac fermion dynamics in a graphene monolayer in the presence of an applied ultrafast laser pulse. The pulse has the duration of a few femtoseconds and the amplitude of 0.1 - 0.5 V/A. The waveform of the pulse is described by Hermit Gaussian polynomials with varying carrier-envelope phase. We show that the ultrafast dynamics of Dirac fermions strongly depends on the carrier-envelope phase and the frequency of the applied pulse. The ultrafast pulse generates an electric current which results in a finite transferred charge. The ultrafast field-driven current and the corresponding net transferred charge depend on the waveform of the applied pulse. Our results pave the way for the development of ultrafast information processing in the terahertz domain.
Should We Trust Occupational Income Scores

Historical studies of labor markets frequently suffer from a lack of data on individual income. The occupational income score (OCCSCORE) is often used as an alternative measure of labor market outcomes. Using modern Census data, we find that the use of OCCSCORE biases results towards zero and can frequently result in statistically significant coefficients of the wrong sign. We use a machine learning approach to construct a new adjusted score based on industry, occupation, and individual demographics. Our alternative score substantially outperforms OCCSCORE in both modern and historical contexts. We illustrate our approach by estimating racial and gender earnings gaps in the 1915 Iowa State Census and intergenerational mobility elasticities using linked data from the 1850-1910 Censuses.
Cog in the wheel: Resource release and the scope of interdependencies in corporate adjustment activities

Research Summary This study characterizes and tests interdependencies in corporate activities used to adjust resources and implement change. Because each activity contributes only a partial result and depends on others to complete an intended change, constraining one can create a bottleneck that traps multiple other activities. For my empirical analysis, I leverage the staggered adoptions of employment protection laws intended to constrain one specific adjustment to one specific resource: dismissing employees. The constraint, however, not only affects the contraction but also the expansion in labor as well as capital investments, acquisition, divestiture, CEO turnover, and other adjustments, and increases (shortens) the persistence of negative (positive) performance. The expansive interdependencies are critical to understanding firm rigidity and resilience, implementation failures, and the persistence of firm performance. Managerial Summary This study shows how executing even a seemingly straightforward improvement requires piecing together a series of interdependent corporate strategic activities. As a result, a constraint on just one is sufficient to foil a successful firm response. Using changes in employment laws, I show that a firm's ability to dismiss employees not only affects firing, but also hiring, capital investments, acquisition, divestiture, and CEO turnover, and increases (shortens) the persistence of negative (positive) performance. Underestimating the complex and interdependent nature of these activities can lead to bias‐for‐action and contribute to implementation failures, such as cost overruns, schedule delays, and a post‐merger integration process that fails to realize expected synergies.
Classification of open and closed convex codes on five neurons

Abstract Neural codes, represented as collections of binary strings, encode neural activity and show relationships among stimuli. Certain neurons, called place cells, have been shown experimentally to fire in convex regions in space. A natural question to ask is: Which neural codes can arise as intersection patterns of convex sets? While past research has established several criteria, complete conditions for convexity are not yet known for codes with more than four neurons. We classify all neural codes with five neurons as convex/non-convex codes. Furthermore, we investigate which of these codes can be represented by open versus closed convex sets. Interestingly, we find a code which is an open but not closed convex code and demonstrate a minimal example for this phenomenon.
A Reference Governor for Nonlinear Systems With Disturbance Inputs Based on Logarithmic Norms and Quadratic Programming

This note describes a reference governor design for a continuous-time nonlinear system with an additive disturbance. The design is based on predicting the response of the nonlinear system, by the response of a linear model with a set-bounded prediction error, where a state-and-input-dependent bound on the prediction error is explicitly characterized using logarithmic norms. The online optimization is reduced to a convex quadratic program with linear inequality constraints. Two numerical examples are reported.
Maternal infection and antibiotic use in pregnancy and the risk of childhood obesity in offspring: a birth cohort study.

The reported association between maternal antibiotic use and childhood obesity, if true, could change obstetric practice. However, it is unclear whether the reported association was due to antibiotics, or underlying infection or both. To examine the independent contributions of maternal infection and antibiotic use separately, we conducted a birth cohort study among Kaiser Permanente Northern California (KPNC) members. The study consisted of 145,393 mother-child dyads. The KPNC electronic medical records provided data on maternal infections, antibiotic use during pregnancy, and longitudinal anthropometric measurements throughout childhood. Obesity was defined by BMI using CDC criteria. Mixed effects logistic regression for repeated measurements was used to analyze multiple BMI measurements per child (five measurements per child on average). After controlling for confounders using propensity score methodology, there was no increased risk associated with maternal antibiotic use during pregnancy once underlying infection was controlled for: OR = 0.97 (95% CI: 0.92–1.01). There was also no association with timing of use or use of broad-spectrum antibiotics, nor a dose-response relationship. In contrast, maternal untreated infection (without antibiotic use) during pregnancy was associated with a statistically significant risk of childhood obesity compared with mothers without infection: odds ratio (OR) = 1.09 (95% confidence interval (CI): 1.03–1.16). The association was stronger for GBS positive infection (OR = 1.16) than GBS negative infections (OR = 1.08). These results were further confirmed by a discordant sibling study. This discordant sibling study allowed additional control of unmeasured confounders including genetic, maternal intrauterine, and familiar factors. The consistent findings from this sibling study enhances the reproducibility of our findings. It is maternal infection, NOT antibiotic use, during pregnancy that is associated with increased risk of childhood obesity. While use of antibiotics should always be judicious, in the context of preventing childhood obesity, the focus should be on reducing maternal infections during pregnancy.
Learning to Optimally Segment Point Clouds

We focus on the problem of class-agnostic instance segmentation of LiDAR point clouds. We propose an approach that combines graph-theoretic search with data-driven learning: it searches over a set of candidate segmentations and returns one where individual segments score well according to a data-driven point-based model of “objectness”. We prove that if we score a segmentation by the worst objectness among its individual segments, there is an efficient algorithm that finds the optimal worst-case segmentation among an exponentially large number of candidate segmentations. We also present an efficient algorithm for the average-case. For evaluation, we repurpose KITTI 3D detection as a segmentation benchmark and empirically demonstrate that our algorithms significantly outperform past bottom-up segmentation approaches and top-down object-based algorithms on segmenting point clouds.
Transplanckian censorship and the local swampland distance conjecture

The swampland distance conjecture (SDC) addresses the ability of effective field theory to describe distant points in moduli space. It is natural to ask whether there is a local version of the SDC: is it possible to construct local excitations in an EFT that sample extreme regions of moduli space? In many cases such excitations exhibit horizons or instabilities, suggesting that there are bounds on the size and structure of field excitations that can be achieved in EFT. Static bubbles in ordinary Kaluza-Klein theory provide a simple class of examples: the KK radius goes to zero on a smooth surface, locally probing an in- finite distance point, and the bubbles are classically unstable against radial perturbations. However, it is also possible to stabilize KK bubbles at the classical level by adding flux. We study the impact of imposing the Weak Gravity Conjecture (WGC) on these solutions, finding that a rapid pair production instability arises in the presence of charged matter with q/m ≳ 1. We also analyze 4d electrically charged dilatonic black holes. Small curvature at the horizon imposes a bound log (MBH) ,≳ |∆𝜙|, independent of the WGC, and the bound can be strengthened if the particle satisfying the WGC is sufficiently light. We conjecture that quantum gravity in asymptotically flat space requires a general bound on large localized moduli space excursions of the form |∆𝜙| ≲ | log(RΛ)|, where R is the size of the minimal region enclosing the excitation and Λ−1 is the short-distance cutoff on local EFT. The bound is qualitatively saturated by the dilatonic black holes and Kaluza-Klein monopoles.
CALC2.0: Combining Appearance, Semantic and Geometric Information for Robust and Efficient Visual Loop Closure

Traditional attempts for loop closure detection typically use hand-crafted features, relying on geometric and visual information only, whereas more modern approaches tend to use semantic, appearance or geometric features extracted from deep convolutional neural networks (CNNs). While these approaches are successful in many applications, they do not utilize all of the information that a monocular image provides, and many of them, particularly the deep-learning based methods, require user-chosen thresholding to actually close loops – which may impact generality in practical applications. In this work, we address these issues by extracting all three modes of information from a custom deep CNN trained specifically for the task of place recognition. Our network is built upon a combination of a semantic segmentator, Variational Autoencoder (VAE) and triplet embedding network. The network is trained to construct a global feature space to describe both the visual appearance and semantic layout of an image. Then local keypoints are extracted from maximally-activated regions of low-level convolutional feature maps, and keypoint descriptors are extracted from these feature maps in a novel way that incorporates ideas from successful hand-crafted features. These keypoints are matched globally for loop closure candidates, and then used as a final geometric check to refute false positives. As a result, the proposed loop closure detection system requires no touchy thresholding, and is highly robust to false positives – achieving better precision-recall curves than the state-of-the-art NetVLAD, and with real-time speeds.
Computation-Aware Data Aggregation.

Data aggregation is a fundamental primitive in distributed computing wherein a network computes a function of every nodes' input. However, while compute time is non-negligible in modern systems, standard models of distributed computing do not take compute time into account. Rather, most distributed models of computation only explicitly consider communication time. In this paper, we introduce a model of distributed computation that considers both computation and communication so as to give a theoretical treatment of data aggregation. We study both the structure of and how to compute the fastest data aggregation schedule in this model. As our first result, we give a polynomial-time algorithm that computes the optimal schedule when the input network is a complete graph. Moreover, since one may want to aggregate data over a pre-existing network, we also study data aggregation scheduling on arbitrary graphs. We demonstrate that this problem on arbitrary graphs is hard to approximate within a multiplicative 1.5 factor. Finally, we give an O(log n ⋅ log(OPT/t_m))-approximation algorithm for this problem on arbitrary graphs, where n is the number of nodes and OPT is the length of the optimal schedule.
RISE-SLAM: A Resource-aware Inverse Schmidt Estimator for SLAM

In this paper, we present the RISE-SLAM algorithm for performing visual-inertial simultaneous localization and mapping (SLAM), while improving estimation consistency. Specifically, in order to achieve real-time operation, existing approaches often assume previously-estimated states to be perfectly known, which leads to inconsistent estimates. Instead, based on the idea of the Schmidt-Kalman filter, which has processing cost linear in the size of the state vector but quadratic memory requirements, we derive a new consistent approximate method in the information domain, which has linear memory requirements and adjustable (constant to linear) processing cost. In particular, this method, the resource-aware inverse Schmidt estimator (RISE), allows trading estimation accuracy for computational efficiency. Furthermore, and in order to better address the requirements of a SLAM system during an exploration vs. a relocalization phase, we employ different configurations of RISE (in terms of the number and order of states updated) to maximize accuracy while preserving efficiency. Lastly, we evaluate the proposed RISE-SLAM algorithm on publicly-available datasets and demonstrate its superiority, both in terms of accuracy and efficiency, as compared to alternative visual-inertial SLAM systems.
Ankle Torque During Mid-Stance Does Not Lower Energy Requirements of Steady Gaits

In this paper, we investigate whether applying ankle torques during mid-stance can be a more effective way to reduce energetic cost of locomotion than actuating leg length alone. Ankles are useful in human gaits for many reasons including static balancing. In this work, we specifically avoid the heel-strike and toe-off benefits to investigate whether the progression of the center of pressure from heel-to-toe during mid-stance, or some other approach, is beneficial in and of itself. We use an Ankle Actuated Spring Loaded Inverted Pendulum model to simulate the shifting center of pressure dynamics, and trajectory optimization is applied to find limit cycles that minimize cost of transport. The results show that, for the vast majority of gaits, ankle torques do not affect cost of transport. Ankles reduce the cost of transport during a narrow band of gaits at the transition from grounded running to aerial running. This suggests that applying ankle torque during mid-stance of a steady gait is not a directly beneficial strategy, but is most likely a path between beneficial heel-strikes and toe-offs.
A winner in the Anthropocene: changing host plant distribution explains geographical range expansion in the gulf fritillary butterfly

Author(s): Halsch, CA; Shapiro, AM; Thorne, JH; Waetjen, DP; Forister, ML | Abstract: 1. The changing climate is altering species distributions with consequences for population dynamics, resulting in winners and losers in the Anthropocene. 2. Agraulis vanillae, the gulf fritillary butterfly, has expanded its range in the past 100 years in the western U.S.A. Time series analysis is combined with species distribution modelling to investigate factors limiting the distribution of A. vanillae and to predict future shifts under warming scenarios. 3. Time series analyses from the western U.S.A. show that urban development has a positive association with year of colonisation (the host plant Passiflora is an ornamental in gardens). Colonisation was also associated positively and to a lesser extent with winter maximum temperatures, whereas a negative impact of minimum temperatures and precipitation was apparent on population growth rates after establishment. 4. Species distribution models vary by region. In the eastern U.S.A., the butterfly is primarily limited by minimum temperatures in the winter and host availability later in the season. Eastern U.S. projected expansion broadly follows the expectation of poleward distributional shifts, especially for the butterfly's maximum annual extent. Western U.S. distributions are limited by the host plant, which in turn is dependent on urban centres. Projected western U.S. expansion is not limited to a single direction and is driven by urban centres becoming more suitable for the host plant. 5. These results demonstrate the value of combining time series with spatial modelling, at the same time as incorporating biotic interactions, aiming to understand and predict shifting geographical ranges in the Anthropocene.
A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome

Understanding when and why new species are recruited into microbial communities is a formidable problem with implications for managing microbial systems, for instance by helping us better understand whether a probiotic or pathogen would be expected to colonize a human microbiome. Much theory in microbial temporal dynamics is focused on how phylogenetic relationships between microbes impact the order in which those microbes are recruited; for example, species that are closely related may competitively exclude each other. However, several recent human microbiome studies have observed closely related bacteria being recruited into microbial communities in short succession, suggesting that microbial community assembly is historically contingent, but competitive exclusion of close relatives may not be important. To address this, we developed a mathematical model that describes the order in which new species are detected in microbial communities over time within a phylogenetic framework. We use our model to test three hypothetical assembly modes: underdispersion (species recruitment is more likely if a close relative was previously detected), overdispersion (recruitment is more likely if a close relative has not been previously detected), and the neutral model (recruitment likelihood is not related to phylogenetic relationships among species). We applied our model to longitudinal human microbiome data, and found that for the individuals we analyzed, the human microbiome generally follows the underdispersion (i.e., nepotism) hypothesis. Exceptions were oral communities and the fecal communities of two infants that had undergone heavy antibiotic treatment. None of the datasets we analyzed showed statistically significant phylogenetic overdispersion.
The essential spectrum of canonical systems

Abstract We study the minimum of the essential spectrum of canonical systems J u ′ = − z H u . Our results can be described as a generalized and more quantitative version of the characterization of systems with purely discrete spectrum, which was recently obtained by Romanov and Woracek (2020). Our key tool is oscillation theory.
Shuttle entry guidance revisited

The Shuttle entry guidance concept is reviewed which is aimed at tracking a reference drag trajectory that leads to the specified range and velocity for the initiation of the terminal energy management phase. An approximate method of constructing the domain of attraction is proposed, and its validity is ascertained by simulation. An alternative guidance law yielding global exponential tracking in the absence of control saturation is derived using a feedback linearization method. It is noted that the alternative guidance law does not improve on the stability and performance of the current guidance law, for the operating domain and control capability of the Shuttle. It is suggested that the new guidance law with a larger operating domain and increased lift-to-drag capability would be superior.
Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classifiers have been utilized to assist in the development of neuroimaging-based biomarkers for objective diagnosis of ADHD. However, existing basic model-based studies in ADHD report suboptimal classification performances and inconclusive results, mainly due to the limited flexibility for each type of basic classifier to appropriately handle multi-dimensional source features with varying properties. This study applied ensemble learning techniques (ELTs), a meta-algorithm that combine several basic machine learning models into one predictive model in order to decrease variance, bias, or improve predictions, in multimodal neuroimaging data collected from 72 young adults, including 36 probands (18 remitters and 18 persisters of childhood ADHD) and 36 group-matched controls. All currently available optimization strategies for ELTs (i.e., voting, bagging, boosting and stacking techniques) were tested in a pool of semifinal classification results generated by seven basic classifiers. The high-dimensional neuroimaging features for classification included regional cortical gray matter (GM) thickness and surface area, GM volume of subcortical structures, volume and fractional anisotropy of major white matter fiber tracts, pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process. As a result, the bagging-based ELT with the base model of support vector machine achieved the best results, with significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD vs. controls and 0.9 for ADHD persisters vs. remitters). Features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. Considering their improved robustness than the commonly implemented basic classifiers, findings suggest that ELTs may have the potential to identify more reliable neurobiological markers for neurodevelopmental disorders.
Sparse Representation Classification Beyond ℓ1 Minimization and the Subspace Assumption

The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of $\ell 1$ minimization and works well for image recognition satisfying a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency under a latent subspace model and contamination. The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance and significantly faster.
Estimation in Poisson Noise: Properties of the Conditional Mean Estimator

This paper considers estimation of a random variable in Poisson noise with signal scaling coefficient and dark current as explicit parameters of the noise model. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the scaling coefficient, the dark current parameter, the distribution of the input random variable and channel realizations. With respect to the scaling coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the gradient of the conditional mean estimator with respect to the scaling coefficient and dark current parameter is proportional to the conditional variance. Moreover, a score function is proposed and a Tweedie-like formula for the conditional expectation is recovered. With respect to the distribution, several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in terms of mean squared error, then the input distribution is approximately gamma in the Levy distance. Furthermore, sufficient and necessary conditions for linearity are found. Interestingly, it is shown that the conditional mean estimator cannot be linear when the dark current parameter of the Poisson noise is non-zero.
On the feasibility of returning to the gold standard

Abstract The gold standard is back in the news following a series of announcements from the Trump Administration indicating that the President was considering candidates for the open positions on the Federal Reserve Board who are sympathetic to the idea of restoring the gold standard. The prospect of returning to such a monetary system raises several important questions that would need to be addressed prior to its implementation. What would the appropriate parity be? How much gold would it require? Is the existing gold stock sufficient to support it? How much would it cost? This paper takes up these questions for the world's largest economies. I argue that the current market price of gold closely approximates the appropriate re-entry parity, and that at this price and at the currently required level of fiat reserves, the stock of above ground gold is large enough to support the adoption of gold redeemability, although it would require a one-time outlay of $3.5 trillion. I also find that the costs of maintaining the gold standard would be $383 billion per year. However, both of these cost estimates decline substantially when using historically-realistic reserve ratios. The principle conclusion of my analysis is that returning to the gold standard would be feasible in the technical sense considered in this paper. There is more than enough gold to support the resumption of redeemability, and the costs of doing so are relatively small. Other, more fundamental margins of feasibility should be considered, however.
Formal Connections between Template and Anchor Models via Approximate Simulation

Reduced-order template models like the Linear Inverted Pendulum (LIP) and Spring-Loaded Inverted Pendulum (SLIP) are widely used tools for controlling high-dimensional humanoid robots. However, connections between templates and whole-body models have lacked formal underpinnings, preventing formal guarantees when it comes to integrated controller design. We take a small step towards addressing this gap by considering the notion of approximate simulation. Derived from simulation relations for discrete transition systems in formal methods, approximate similarity means that the outputs of two systems can remain $\epsilon{-}$ close. In this paper, we consider the case of controlling a balancer via planning with the LIP model. We show that the balancer approximately simulates the LIP and derive linear constraints that are sufficient conditions for maintaining ground contact. This allows for rapid planning and replanning with the template model by solving a quadratic program that enforces contact constraints in the full model. We demonstrate the efficacy of this planning and control paradigm in a simulated push recovery scenario for a planar 4-link balancer.