cssi/SciSciGPT-SciSciNet huggingface dataset:
Analytical and experimental vibration and buckling characteristics of a pretensioned stayed column

Modal vibration tests to determine lateral modes of vibration of a stayed column and static axial compression tests to determine the column's buckling and postbuckling behavior have been performed. Effects of stay tension levels and vibration-load interaction are presented. Two finite element models are used to analyze the column, a three-dimensional frame using NASTRAN and an equivalent two-dimensional frame using an exact dynamic stiffness matrix. Both analyses correlated well with the linear vibration and buckling experimental data. Results indicate premature buckling of the column due to vibration-load interaction and nonlinear oscillations due to stay slackening. Postbuckling behavior of the column is unusual because of stay slackening and results in a postbuckling restoring force of less than the bifurcation buckling load. Guidelines for design of pretensioned structures are presented which consider buckling, postbuckling and vibration behavior.
Crystal structure, magnetic properties and bonding analysis of M3Pt23Ge11 (M=Ca, Sr, Ba and Eu)

Abstract The properties of Pt-based materials can be intriguing due to the importance of spin-orbit coupling for Pt. Herein, we report four new phases with formulas M3Pt23Ge11 (M ​= ​Ca, Sr, Ba and Eu), which adopt the same structure type as Ce3Pt23Si11. Magnetic susceptibility measurements indicate that none of the phases is superconducting above 1.8 ​K, while for Eu3Pt23Ge11 ferromagnetic ordering is observed at ~ 3 ​K. The low Curie temperature for that material compared to that of Eu3Pt23Si11 may be due to its larger Eu–Eu distance. One potential factor that destabilizes the structure of other rare-earth based M3Pt23Ge11 is demonstrated through COHP calculations.
ProcData: An R Package for Process Data Analysis

Process data refer to data recorded in log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents’ response problem-solving behaviors. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by utilizing the rich information contained in response processes. The R package ProcData presented in this article is designed to provide tools for inspecting, processing, and analyzing process data. We define an S3 class ‘proc’ for organizing process data and extend generic methods summary and print for ‘proc’. Feature extraction methods for process data are implemented in the package for compressing information in the irregular response processes into regular numeric vectors. ProcData also provides functions for making predictions from neural-network-based sequence models. In addition, a real dataset of response processes from the climate control item in the 2012 Programme for International Student Assessment is included in the package.
Slow and fast light in plasma

Slow and fast light require precise tailoring of the refractive index of a medium. We present the first experimental demonstration of such control inside plasma, reporting group velocity of light between 0.12c and -0.34c.
Bulk dissipation in the quantum anomalous Hall effect

Even at the lowest accessible temperatures, measurements of the quantum anomalous Hall (QAH) effect have indicated the presence of parasitic dissipative conduction channels. There is no consensus whether parasitic conduction is related to processes in the bulk or along the edges. Here, we approach this problem by comparing transport measurements of Hall bar and Corbino geometry devices fabricated from Cr-doped (BiSb)2Te3. We identify bulk conduction as the dominant source of dissipation at all values of temperature and in-plane electric field. Furthermore, we observe identical breakdown phenomenology in both geometries, indicating that breakdown of the QAH phase is a bulk process. The methodology developed in this study could be used to identify dissipative conduction mechanisms in new QAH materials, ultimately guiding material development toward realization of the QAH effect at higher temperatures.
A Bayesian Machine Learning Algorithm for Predicting ENSO Using Short Observational Time Series

A simple and efficient Bayesian machine learning (BML) training and forecasting algorithm, which exploits only a 20-year short observational time series and an approximate prior model, is developed to predict the Nino 3 sea surface temperature (SST) index. The BML forecast significantly outperforms model-based ensemble predictions and standard machine learning forecasts. Even with a simple feedforward neural network, the BML forecast is skillful for 9.5 months. Remarkably, the BML forecast overcomes the spring predictability barrier to a large extent: the forecast starting from spring remains skillful for nearly 10 months. The BML algorithm can also effectively utilize multiscale features: the BML forecast of SST using SST, thermocline, and wind burst improves on the BML forecast using just SST by at least 2 months. Finally, the BML algorithm also reduces the forecast uncertainty of neural networks and is robust to input perturbations.
Meeting Laboratory Course Learning Goals Remotely via Custom Home Experiment Kits

Cost-effective, custom home experiment kits were developed and used for remote teaching of first-semester introductory physics labs. The kit experiments were designed to match the existing onsite lab course learning goals. A revised group project approach optimized for remote use was also developed that leverages the kits. Student survey results at the end of the summer 2020 semester indicate that the critical learning goals were met, student satisfaction with the remote course was maintained, and successful collaboration via video conferencing breakout rooms was achieved.
Commentary Relative to the Distribution of Gamma-Ray Flares on the Sun: Further Evidence for a Distinct Solar Surface

High energy gamma-ray flares are almost always observed near the limb of the Sun and are seldom, if ever, visualized in the central region of the solar disc. As such, they exhibit a powerful anisotropy best explained by invoking a true photospheric surface. In this regard, the anisotropic nature of the gamma-ray emissions from high-energy flares constitute the eighteenth line of evidence that the Sun is condensed matter.
Instance Independence of Single Layer Quantum Approximate Optimization Algorithm on Mixed-Spin Models at Infinite Size

This paper studies the application of the Quantum Approximate Optimization Algorithm (QAOA) to spin-glass models with random multi-body couplings in the limit of a large number of spins. We show that for such mixed-spin models the performance of depth $1$ QAOA is independent of the specific instance in the limit of infinite sized systems and we give an explicit formula for the expected performance. We also give explicit expressions for the higher moments of the expected energy, thereby proving that the expected performance of QAOA concentrates.
PolyAR: A Highly Parallelizable Solver For Polynomial Inequality Constraints Using Convex Abstraction Refinement

Abstract Numerical tools for constraints solving are a cornerstone to control verification problems. This is evident by the plethora of research that uses tools like linear and convex programming for the design of control systems. Nevertheless, the capability of linear and convex programming is limited and is not adequate to reason about general nonlinear polynomials constraints that arise naturally in the design of nonlinear systems. This limitation calls for new solvers that are capable of utilizing the power of linear and convex programming to reason about general multivariate polynomials. In this paper, we propose PolyAR, a highly parallelizable solver for polynomial inequality constraints. PolyAR provides several key contributions. First, it uses convex relaxations of the problem to accelerate the process of finding a solution to the set of the non-convex multivariate polynomials. Second, it utilizes an iterative convex abstraction refinement process which aims to prune the search space and identify regions for which the convex relaxation fails to solve the problem. Third, it allows for a highly parallelizable usage of off-the-shelf solvers to analyze the regions in which the convex relaxation failed to provide solutions. We compared the scalability of PolyAR against Z3 8.9 and Yices 2.6 on control designing problems. Finally, we demonstrate the performance of PolyAR on designing switching signals for continuous-time linear switching systems.
Multifractal analysis of geodesic flows on surfaces without focal points

In this paper, we study multifractal spectra of the geodesic flows on compact rank 1 surfaces without focal points. We compute the entropy of the level sets for the Lyapunov exponents and establish...
Chance-Constrained Motion Planning using Modeled Distance- to-Collision Functions

This paper introduces Chance Constrained Gaussian Process-Motion Planning (CCGP-MP), a motion planning algorithm for robotic systems under motion and state estimate uncertainties. The paper's key idea is to capture the variations in the distance-to-collision measurements caused by the uncertainty in state estimation techniques using a Gaussian Process (GP) model. We formulate the planning problem as a chance constraint problem and propose a deterministic constraint that uses the modeled distance function to verify the chance-constraints. We apply Simplicial Homology Global Optimization (SHGO) approach to find the global minimum of the deterministic constraint function along the trajectory and use the minimum value to verify the chance-constraints. Under this formulation, we can show that the optimization function is smooth under certain conditions and that SHGO converges to the global minimum. Therefore, CCGP-MP will always guarantee that all points on a planned trajectory satisfy the given chance-constraints. The experiments in this paper show that CCGP-MP can generate paths that reduce collisions and meet optimality criteria under motion and state uncertainties. The implementation of our robot models and path planning algorithm can be found on GitHub11https://github.com/jacobjj/gp_prob_planning.
Topological Stability of the Hippocampal Spatial Map and Synaptic Transience

Spatial awareness in mammals is based on internalized representations of the environment—cognitive maps—encoded by networks of spiking neurons. Although behavioral studies suggest that these maps can remain stable for long periods, it is also well-known that the underlying networks of synaptic connections constantly change their architecture due to various forms of neuronal plasticity. This raises a principal question: how can a dynamic network encode a stable map of space? In the following, we discuss some recent results obtained in this direction using an algebro-topological modeling approach, which demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is not only possible, but also may be a generic phenomenon.
It's Hard for Neural Networks to Learn the Game of Life

Efforts to improve the learning abilities of neural networks have focused mostly on the role of optimization methods rather than on weight initializations. Recent findings, however, suggest that neural networks rely on lucky random initial weights of subnetworks called “lottery tickets” that converge quickly to a solution [1]. To investigate how weight initializations affect performance, we examine small convolutional networks that are trained to predict $n$ steps of the two-dimensional cellular automaton Conway's Game of Life, the update rules of which can be implemented efficiently in a small CNN. We find that networks of this architecture trained on this task rarely converge. Rather, networks require substantially more parameters to consistently converge. Furthermore, we find that the initialization parameters that gradient descent converges to a solution are sensitive to small perturbations, such as a single sign change. Finally, we observe a critical value $d_{0}$ such that training minimal networks with examples in which cells are alive with probability $d_{0}$ dramatically increases the chance of convergence to a solution. Our results are consistent with the lottery ticket hypothesis [1].
A 1-separation formula for the graph Kemeny constant and Braess edges

Kemeny’s constant of a simple connected graph G is the expected length of a random walk from i to any given vertex $$j \ne i$$ . We provide a simple method for computing Kemeny’s constant for 1-separable graphs via effective resistance methods from electrical network theory. Using this formula, we furnish a simple proof that the path graph on n vertices maximizes Kemeny’s constant for the class of undirected trees on n vertices. Applying this method again, we simplify existing expressions for the Kemeny’s constant of barbell graphs and demonstrate which barbell maximizes Kemeny’s constant. This 1-separation identity further allows us to create sufficient conditions for the existence of Braess edges in 1-separable graphs. We generalize the notion of the Braess edge to Braess sets, collections of non-edges in a graph such that their addition to the base graph increases the Kemeny constant. We characterize Braess sets in graphs with any number of twin pendant vertices, generalizing work of Kirkland and Zeng (Electron J Linear Algebra 31(1):444–464, 2016) and Ciardo (Linear Algebra Appl, 2020).
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks

Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep learning frameworks, since they provide higher computational efficiency due to event-driven information processing. SNNs distribute the analog values of pixel intensities into binary spikes over time. However, the most widely used input coding schemes, such as Poisson based rate-coding, do not leverage the additional temporal learning capability of SNNs effectively. Moreover, these SNNs suffer from high inference latency which is a major bottleneck to their deployment. To overcome this, we propose a scalable time-based encoding scheme that utilizes the Discrete Cosine Transform (DCT) to reduce the number of timesteps required for inference. DCT decomposes an image into a weighted sum of sinusoidal basis images. At each time step, a single frequency base, taken in order and modulated by its corresponding DCT coefficient, is input to an accumulator that generates spikes upon crossing a threshold. We use the proposed scheme to learn DCT-SNN, a low-latency deep SNN with leaky-integrate-and-fire neurons, trained using surrogate gradient descent based backpropagation. We achieve top-1 accuracy of 89.94%, 68.3% and 52.43% on CIFAR-10, CIFAR-100 and TinyImageNet, respectively using VGG architectures. Notably, DCT-SNN performs inference with 2-14X reduced latency compared to other state-of-the-art SNNs, while achieving comparable accuracy to their standard deep learning counterparts. The dimension of the transform allows us to control the number of timesteps required for inference. Additionally, we can trade-off accuracy with latency in a principled manner by dropping the highest frequency components during inference.
Continuous-time State & Dynamics Estimation using a Pseudo-Spectral Parameterization

We present a novel continuous time trajectory representation based on a Chebyshev polynomial basis, which when governed by known dynamics models, allows for full trajectory and robot dynamics estimation, particularly useful for high-performance robotics applications such as unmanned aerial vehicles. We show that we can gracefully incorporate model dynamics to our trajectory representation, within a factor-graph based framework, and leverage ideas from pseudo- spectral optimal control to parameterize the state and the control trajectories as interpolating polynomials. This allows us to perform efficient optimization at specifically chosen points derived from the theory, while recovering full trajectory estimates. Through simulated experiments we demonstrate the applicability of our representation for accurate flight dynamics estimation for multirotor aerial vehicles. The representation framework is general and can thus be applied to a multitude of high-performance applications beyond multirotor platforms.
Dynamics of three-dimensional turbulence from Navier-Stokes equations

Turbulent flows contain occasional, well-separated sharp features, as illustrated in this picture by Bonn et al. (1993), reproduced with permission, surrounded mostly by low levels of activity. Both interacting structures contribute to the complexity of turbulence. A full theory requires the understanding of both, which is what our paper attempts to do. We predict quantitatively the multi-scaling exponents for these singular-like features of different strengths, while also showing that the background flow is Gaussian. The large spatial separation between the sharp structures enables one to treat them as a ``weakly interacting gas''.
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency

The recent breakthroughs of deep neural networks (DNNs) and the advent of billions of Internet of Things (IoT) devices have excited an explosive demand for intelligent IoT devices equipped with domain-specific DNN accelerators. However, the deployment of DNN accelerator enabled intelligent functionality into real-world IoT devices still remains particularly challenging. First, powerful DNNs often come at prohibitive complexities, whereas IoT devices often suffer from stringent resource constraints. Second, while DNNs are vulnerable to adversarial attacks especially on IoT devices exposed to complex real-world environments, many IoT applications require strict security. Existing DNN accelerators mostly tackle only one of the two aforementioned challenges (i.e., efficiency or adversarial robustness) while neglecting or even sacrificing the other. To this end, we propose a 2-in-1 Accelerator, an integrated algorithm-accelerator co-design framework aiming at winning both the adversarial robustness and efficiency of DNN accelerators. Specifically, we first propose a Random Precision Switch (RPS) algorithm that can effectively defend DNNs against adversarial attacks by enabling random DNN quantization as an in-situ model switch during training and inference. Furthermore, we propose a new precision-scalable accelerator featuring (1) a new precision-scalable MAC unit architecture which spatially tiles the temporal MAC units to boost both the achievable efficiency and flexibility and (2) a systematically optimized dataflow that is searched by our generic accelerator optimizer. Extensive experiments and ablation studies validate that our 2-in-1 Accelerator can not only aggressively boost both the adversarial robustness and efficiency of DNN accelerators under various attacks, but also naturally support instantaneous robustness-efficiency trade-offs adapting to varied resources without the necessity of DNN retraining. We believe our 2-in-1 Accelerator has opened up an exciting perspective for robust and efficient accelerator design.
Virologic features of SARS-CoV-2 infection in children.

Background Data on pediatric COVID-19 has lagged behind adults throughout the pandemic. An understanding of SARS-CoV-2 viral dynamics in children would enable data-driven public health guidance. Methods Respiratory swabs were collected from children with COVID-19. Viral load was quantified by RT-PCR; viral culture was assessed by direct observation of cytopathic effects and semiquantitative viral titers. Correlations with age, symptom duration, and disease severity were analyzed. SARS-CoV-2 whole genome sequences were compared with contemporaneous sequences. Results 110 children with COVID-19 (median age 10 years, range 2 weeks-21 years) were included in this study. Age did not impact SARS-CoV-2 viral load. Children were most infectious within the first five days of illness, and severe disease did not correlate with increased viral loads. Pediatric SARS-CoV-2 sequences were representative of those in the community and novel variants were identified. Conclusions Symptomatic and asymptomatic children can carry high quantities of live, replicating SARS-CoV-2, creating a potential reservoir for transmission and evolution of genetic variants. As guidance around social distancing and masking evolves following vaccine uptake in older populations, a clear understanding of SARS-CoV-2 infection dynamics in children is critical for rational development of public health policies and vaccination strategies to mitigate the impact of COVID-19.
Copy-and-patch compilation: a fast compilation algorithm for high-level languages and bytecode

Fast compilation is important when compilation occurs at runtime, such as query compilers in modern database systems and WebAssembly virtual machines in modern browsers. We present copy-and-patch, an extremely fast compilation technique that also produces good quality code. It is capable of lowering both high-level languages and low-level bytecode programs to binary code, by stitching together code from a large library of binary implementation variants. We call these binary implementations stencils because they have holes where missing values must be inserted during code generation. We show how to construct a stencil library and describe the copy-and-patch algorithm that generates optimized binary code. We demonstrate two use cases of copy-and-patch: a compiler for a high-level C-like language intended for metaprogramming and a compiler for WebAssembly. Our high-level language compiler has negligible compilation cost: it produces code from an AST in less time than it takes to construct the AST. We have implemented an SQL database query compiler on top of this metaprogramming system and show that on TPC-H database benchmarks, copy-and-patch generates code two orders of magnitude faster than LLVM -O0 and three orders of magnitude faster than higher optimization levels. The generated code runs an order of magnitude faster than interpretation and 14% faster than LLVM -O0. Our WebAssembly compiler generates code 4.9X-6.5X faster than Liftoff, the WebAssembly baseline compiler in Google Chrome. The generated code also outperforms Liftoff's by 39%-63% on the Coremark and PolyBenchC WebAssembly benchmarks.
On near-optimal time samplings for initial data best approximation

Leveraging on the work of DeVore and Zuazua, we further explore their methodology and deal with two open questions presented in their paper. We show that for a class of linear evolutionary PDEs of order $2N$ the admissible choice of the parameter $\rho$ which is used to construct the near-optimal sampling sequence is not influenced by the spectrum of of the operator controlling the spatial part of the PDE, but only by its order. Furthermore, we show that it is possible to extend their algorithm to a simple version of a non-autonomous heat equation in which the heat diffusivity coefficient depends explicitly on time.
Shotgun identification on groups

We consider the problem of shotgun identification of patterns on groups, which extends previous work on shotgun identification of DNA sequences and labeled graphs. A shotgun identification problem on a group $G$ is specified by two finite subsets $C \subset G$ and $K \subset G$ and a finite alphabet $\mathcal{A}$. In such problems, there is a ``global" pattern $w \in \mathcal{A}^{CK}$, and one would like to be able to identify this pattern (up to translation) based only on observation of the ``local" $K$-shaped subpatterns of $w$, called reads, centered at the elements of $C$. We consider an asymptotic regime in which the size of $w$ tends to infinity and the symbols of $w$ are drawn in an i.i.d. fashion. Our first general result establishes sufficient conditions under which the random pattern $w$ is identifiable from its reads with probability tending to one, and our second general result establishes sufficient conditions under which the random pattern $w$ is non-identifiable with probability tending to one. Additionally, we illustrate our main results by applying them to several families of examples.
How Data Augmentation affects Optimization for Linear Regression

Though data augmentation has rapidly emerged as a key tool for optimization in modern machine learning, a clear picture of how augmentation schedules affect optimization and interact with optimization hyperparameters such as learning rate is nascent. In the spirit of classical convex optimization and recent work on implicit bias, the present work analyzes the effect of augmentation on optimization in the simple convex setting of linear regression with MSE loss. We find joint schedules for learning rate and data augmentation scheme under which augmented gradient descent provably converges and characterize the resulting minimum. Our results apply to arbitrary augmentation schemes, revealing complex interactions between learning rates and augmentations even in the convex setting. Our approach interprets augmented (S)GD as a stochastic optimization method for a time-varying sequence of proxy losses. This gives a unified way to analyze learning rate, batch size, and augmentations ranging from additive noise to random projections. From this perspective, our results, which also give rates of convergence, can be viewed as Monro-Robbins type conditions for augmented (S)GD.
Subject Verb Agreement in Iraqi Arabic and Modern Standard Arabic: A comparative Study

The present study deals with a comparative study of subject verb agreement in modern standard Arabic and Iraqi Arabic. It aims to show the differences and similarities between these two varieties. It is hypothesized that Iraqi dialect has full agreement between the subject and the verb whereas Modern Standard Arabic in has only partial agreement. Data were collected from authentic books of Arabic and from native speakers of Iraqi Arabic in Baghdad. The goal of this study then, is to raise the awareness of Learners as natives on the issues of sounds, grammar, and vocabulary since Arabic language lacks such dialectal research. The study concludes that there are similarities and differences in both varieties. The findings and conclusions arrived at in this study are expected to be worthy to pedagogical planners and planning, and for other academic purposes.
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives

Despite the potential of reinforcement learning (RL) for building general-purpose robotic systems, training RL agents to solve robotics tasks still remains challenging due to the difficulty of exploration in purely continuous action spaces. Addressing this problem is an active area of research with the majority of focus on improving RL methods via better optimization or more efficient exploration. An alternate but important component to consider improving is the interface of the RL algorithm with the robot. In this work, we manually specify a library of robot action primitives (RAPS), parameterized with arguments that are learned by an RL policy. These parameterized primitives are expressive, simple to implement, enable efficient exploration and can be transferred across robots, tasks and environments. We perform a thorough empirical study across challenging tasks in three distinct domains with image input and a sparse terminal reward. We find that our simple change to the action interface substantially improves both the learning efficiency and task performance irrespective of the underlying RL algorithm, significantly outperforming prior methods which learn skills from offline expert data. Code and videos at https://mihdalal.github.io/raps/
Formation of Unstable Shocks for 2D Isentropic Compressible Euler

In this paper we construct unstable shocks in the context of 2D isentropic compressible Euler in azimuthal symmetry. More specifically, we construct initial data that when viewed in self-similar coordinates, converges asymptotically to the unstable $$C^{\frac{1}{5}}$$ self-similar solution to the Burgers’ equation. Moreover, we show the behavior is stable in $$C^8$$ modulo a two dimensional linear subspace. Under the azimuthal symmetry assumption, one cannot impose additional symmetry assumptions in order to isolate the corresponding manifold of initial data leading to stability: rather, we rely on modulation variable techniques in conjunction with a Newton scheme.
Uncovering non-random binary patterns within sequences of intrinsically disordered proteins

Abstract Sequence-ensemble relationships of intrinsically disordered proteins (IDPs) are governed by binary patterns such as the linear clustering or mixing of specific residues or residue types with respect to one another. To enable the discovery of potentially important, shared patterns across sequence families, we describe a computational method referred to as NARDINI for Non-random Arrangement of Residues in Disordered Regions Inferred using Numerical Intermixing. This work was partially motivated by the observation that parameters that are currently in use for describing different binary patterns are not interoperable across IDPs of different amino acid compositions and lengths. In NARDINI, we generate an ensemble of scrambled sequences to set up a composition-specific null model for the patterning parameters of interest. We then compute a series of pattern-specific z-scores to quantify how each pattern deviates from a null model for the IDP of interest. The z-scores help in identifying putative non-random linear sequence patterns within an IDP. We demonstrate the use of NARDINI derived z-scores by identifying sequence patterns in three well-studied IDP systems. We also demonstrate how NARDINI can be deployed to study archetypal IDPs across homologs and orthologs. Overall, NARDINI is likely to aid in designing novel IDPs with a view toward engineering new sequence-function relationships or uncovering cryptic ones. We further propose that the z-scores introduced here are likely to be useful for theoretical and computational descriptions of sequence-ensemble relationships across IDPs of different compositions and lengths.
Single cell transcriptomics reveal disrupted kidney filter cell-cell interactions after early and selective podocyte injury.

Abstract The health of the kidney filtration barrier requires communication between podocytes, endothelial cells and mesangial cells. Disruption of these cell-cell interactions is thought to contribute to disease progression in chronic kidney diseases (CKD). We recently demonstrated that podocyte ablation via doxycycline-inducible deletion of an essential endogenous molecule, CTCF (iCTCFpod-/-), is sufficient to drive progressive CKD. However, the earliest events connecting podocyte injury to disrupted intercellular communication within the kidney filter remain unclear. Here we performed single-cell RNA sequencing of kidney tissue from iCTCFpod-/- mice after one week of doxycycline induction to generate a map of the earliest transcriptional effects of podocyte injury on cell-cell interactions at single cell resolution. A subset of podocytes showed the earliest signs of injury due to disrupted gene programs for cytoskeletal regulation and mitochondrial function. Surviving podocytes upregulated Col4a5, causing reactive changes in integrin expression in endothelial populations and mesangial cells. Intercellular interaction analysis revealed several receptor-ligand-target gene programs as drivers of endothelial cell injury and abnormal matrix deposition. This analysis reveals the earliest disruptive changes within the kidney filter, pointing to new, actionable targets within a therapeutic window that may allow us to maximize the success of much needed therapeutic interventions for CKD.
Hunting and Prey Switching by Chimpanzees ( Pan troglodytes schweinfurthii ) at Ngogo

Predation can have major effects on vertebrate population dynamics, including predator–prey cycles that occur on multiple time scales when predators switch between preferred and alternative prey and changes in the abundance of preferred prey when the availability of alternative prey changes. Predation has been an important selective force in primate social evolution and may regulate some primate populations, but assessing its impact is often difficult. Chimpanzees (Pan troglodytes) provide opportunities for direct collection of data on predation intensity by following habituated predators because they regularly prey on a variety of vertebrates, including other primates. Chimpanzees mostly hunt red colobus monkeys (Procolobus spp.) wherever they live in sympatry. Previous reports have documented heavy predation on red colobus by chimpanzees in the unusually large Ngogo community, in Kibale National Park, Uganda, and have shown an apparent decline in the local red colobus population. Here, we update information on predation at Ngogo, give further evidence of a red colobus population decline, and ask whether the chimpanzees have made more effort to hunt alternative prey in response. Data collected between 1995 and 2014 show that the rate of alternative prey hunts has increased significantly, while the rate of red colobus hunts has declined. Predation on red colobus peaked in 2002; other prey have since accounted for significantly higher proportions of hunts and prey captures. The overall hunting rate has not changed significantly, but per capita meat availability has decreased because hunts of alternative prey lead to fewer captures than hunts of red colobus. The likelihood that the chimpanzees hunt red colobus on encounter has not changed significantly. In contrast, the likelihood of hunting on encounter has increased (redtail monkeys, Cercopithecus ascanius; gray-cheeked mangabeys, Lophocebus albigena) or remained stable (guerezas, Colobus guereza) for other primates that are major alternative prey species. Encounter rates have not changed for redtails and mangabeys, but the guereza encounter rate has decreased. These results imply that the red colobus population is not recovering and raise the possibility that the chimpanzees are having a negative impact on the guereza population. Chimpanzees are not obligate carnivores, but this may make them prone to cause such impacts because their own populations are probably not limited by those of their prey.
Design and Operating Issues in Flexible Manufacturing

Many decisions have to be made during the design phase, implementation, and subsequent day to day operation of a flexible manufacturing system. Some FMS design problems include, for example, determining the appropriate number of machine tools of each type, the capacity of the material handling system, and the type and size of buffers. FMS planning problems include the determination of which parts should be simultaneously machined next, allocations of pallets and fixtures to part types, and the assignment of operations and associated cutting tools among the limited-capacity tool magazines of the machine tools. FMS scheduling problems include determining the optimal input sequence of parts and perhaps an optimal sequence at each machine tool given the current part mix. FMS control problems are those concerned with, for example, monitoring the system to be sure that requirements and due dates are being met and that unreliability problems are handled appropriately.
Sport: Running up escalators

Until I was eighteen, sport played a large, even dominating, part in my life (music was my other passion). I was a scholar of movement. My PE teachers expected me to become a PE teacher myself, because I was clearly mainly interested in sport and also not too shabby with the book thing. They seemed nonplussed when I announced I was going to university to study psychology (where's the excitement there?). But my intellectual life took off around this time and the opportunities for sport were not as routinely available at university as they were at school. The result was that at university sport largely vanished from my life, to be replaced by books, late nights and sitting around all day drinking coffee. I did on a couple of occasions tag along with a friend who was involved in university gymnastics, but three things put me off: the skin peeling off my palms while practising on the high bar; being hit in the mouth and breaking a tooth while supporting someone else; and all the talk among the student gymnasts about broken backs. Studying the mind seemed much safer. I would still trot out my serial headsprings or my horizontal balance – mainly to impress the girls – but systematic sport disappeared from my life for about four years. It was nerd time for me. I suppose I needed to get my career together, as well as my love life, and it was the late 1960s and early 1970s, when sport was not much appreciated culturally; in any case, I let it go.
Activation of the Murine Sarcoma Virus Genome After Infection with the Murine Leukemia Virus as Determined by Cell Agglutination

Non-virus-producing NIH/3T3 cells transformed by the murine sarcoma virus are agglutinated by conconavalin A to the same low level as normal NIH/3T3 cells. Infection with the murine leukemia virus greatly increases the agglutination of transformed cells but not that of normal cells. These data suggest that the morphological expression of cell transformation and the surface alterations associated with increased cell agglutination are controlled by the expressions of different sarcoma virus genes.
Case-based reasoning for financial decision making

Case-based reasoning provides a computational paradigm for simulating the role of experience and intentions in decision making. The author describes a program that provides a framework for modeling financial decision making. He suggests the use of qualitative decision models as a computationally feasible complement to the traditional quantitative approaches to financial decision making. He contrasts the approach to standard rule-based expert systems. He proposes an analysis based on the goals and relationships among the various economic agents in the financial community. >
Infiltration of Fibrous Preforms by a Pure Metal: Part IV. Morphological Stability of the Remelting Front

The injection of pure liquid metal into a fibrous preform initially at a temperature below the melting point of the metal results in partial solidification of the metal as it contacts the fibers. If the infiltrating metal is superheated, continued infiltration remelts this solid metal at a sharp front which propagates through the fiber preform. Because the permeability of the remelted region is greater than that of the partially solidified region, there is a driving force for the remelted region to advance in an unstable manner. In the present work, the morphological stability of a simplified infiltration system (steady-state, unidirectional, adiabatic infiltration with a pure metal) is analyzed using linear stability analysis. The resulting analytical solution illustrates the relationship between front stability and relevant processing parameters. The practical result of the derivation is that a stable remelting front can be maintained by keeping the infiltration velocity below a certain critical value.
Perspectives on the Field of Behavior Analysis.

Absolute truth is a legend. It is real as Bigfoot, UFOs, and Santa Claus. I think. Perhaps, more data will get us closer to capital T truth, but for now, we better keep it lowercase t. For centuries, philosophers have debated the objectiveness of our world and cautioned us about how our own participation in the observation of events may bias or taint the objective nature of such inquiry. In this issue of Behavior Analysis in Practice (BAP), we must remind ourselves once again about trying to capitalize the T in what we feel as truth. Where we stand as an observer positions ourselves to see things from a certain perspective. The series of commentaries on the Dixon et al. (2015) paper that ranked graduate training programs and faculty research productivity provide 14 different opinions about truth. A similar example can be found with Putman and Knoster paper that replies to many critics of PBIS, some of which appeared in a prior issue of Behavior Analysis in Practice . Both the research ranking and PBIS discussion/response series of BAP in 2015 illustrate the sometimes unsettling reality that Truth has yet to be found. Instead, discourse such as this helps researchers and clinicians hone the questions of tomorrow, and also refine the perspective by which they approach and observe the event in question. Furthermore, these intellectual exercises help move closer towards T even if we never can actually capitalize it.
Production systems: dealing with turbulence

The use of microcomputers with ACE-type tool boxes in a DAE-type client-server network has become a genuine trump card for control engineers, who are to an increasing extent obliged to work with the client's existing information system. With solutions like ACE, they can develop all the functions needed for controlling automatic systems, communicate directly with the production control functions, and organize the system gradually over time according to the needs. PFSACE (Plant Floor Series Automation Control Edition) is a software platform especially designed to create equipment control applications by offering users a simple language: AMU2, based on "OBJECT" programming techniques. The most important thing about the PFSACE Tool Box is that the user in total control of the pace of integration and remains completely autonomous.
Studies on the amino acid sequence of tobacco mosaic virus (TMV) protein. I. Fractionation of products of tryptic hydrolysis by countercurrent distribution.

Abstract Tobacco mosaic virus (TMV) protein (mol. wt. of subunit 18,270) was hydrolyzed with trypsin. The material soluble at pH 4.0 was fractionated by countercurrent distribution with the solvent system 2-butanol-0.1 M dichloroacetic acid. Preliminary investigations on these fractions have shown that nine apparently major tryptic hydrolysis products containing arginine and one containing lysine have been obtained from this fractionation. One arginine-containing peptide of large molecular weight was found in the portion of the digest insoluble at pH 4.0. One of these peptides has been found to contain arginine in both the N- and C-terminal positions. The nature and number of the N- and C-terminal residues produced as a result of the action of trypsin on TMV protein have been investigated. These results have revealed the presence of about one mole each of N-terminal glutamic acid, aspartic acid, serine, threonine, phenylalanine, tyrosine, and arginine and approximately two moles of glycine. Fractional amounts of N-terminal leucine and valine were also found. One mole each of C-terminal threonine and lysine and slightly more than nine moles of C-terminal arginine were determined.
Bayesian quickest detection with observation-changepoint feedback

We study Bayesian quickest detection problems where the observations and the underlying change-point are coupled. This setup supersedes classical models that assume independence of the two. We develop several continuous-time formulations of this problem for the cases of Poissonian and Brownian sensors. Our approach to detection uses methods of nonlinear filtering and optimal stopping and lends itself to an efficient numerical scheme that combines particle filtering with Monte Carlo dynamic programming. The developed models and algorithms are illustrated with numerical examples.
Sub-tidal benthic habitats of central San Francisco Bay and offshore Golden Gate area — A review

Abstract Deep-water potential estuarine and marine benthic habitat types were defined from a variety of new and interpreted data sets in central San Francisco Bay and offshore Golden Gate area including multibeam echosounder (MBES), side-scan sonar and bottom grab samples. Potential estuarine benthic habitats identified for the first time range from hard bedrock outcrops on island and mainland flanks and some Bay floor regions, to soft, very dynamic bedforms consisting of sediment waves and ripples. Soft sediment ranges from mud and sand to bimodal (two or more grain sizes) sediment of gravel, pebbles, and cobbles. In addition, considerable anthropogenic features (i.e., pipelines, bridge abutments, dredged channels, dump sites) were distinguished. Of the 52 potential benthic habitat types mapped (compressed to 14 types for this paper), 24 were of unconsolidated sediment with five of these comprised of dynamic bedforms or sediment waves and dunes, five of mixed (soft over hard) substrate type, six of hard substrate or rock outcrop, 13 of anthropogenically disturbed areas and four hard anthropogenic features. Rock outcrops and rubble are considered the primary habitat type for rockfish ( Sebastes spp.), lingcod ( Ophiodon elongatus ) and in shallow water for herring ( Clupea pallasii ) spawning. Dynamic bedforms such as sand waves are considered potential foraging habitat for juvenile lingcod, may be sub-tidal habitat for the Pacific sand lance ( Ammodytes hexapterus ) forage fish, and possibly resting habitat for migratory fishes such as sturgeon ( Acipenser medirostris ). The potential marine benthic habitats identified in San Francisco Bay are not unlike those found in other estuaries around the world and this study should contribute significant information that will be of interest to scientists, managers and fishers investigating and utilizing bay and estuarine resources. As described in the many papers of this special issue, the understanding of the interrelationship of geology and ecology is critical to the identification of essential habitats and the sustainability of a healthy ecosystem.
Shaping metal-matrix composites by thixotropic machining

Abstract Thixotropic machining was demonstrated for the shaping of an aluminium-matrix particulate composite. The process involved deforming the remelted composite in a thixotropic state and releasing the applied stress while the composite was still in that state. However, the process-induced cracking was particularly severe when the cutting tool tended to adhere to the thixotropic composite.
Underwater oviposition in a damselfly (Odonata: Coenagrionidae) favors male vigilance, and multiple mating by females

Female Enallagma hageni oviposit underwater where they are inaccessible to males. I demonstrate that males guard submerged females rather than perch sites, and are behaviorally distinct from lone males at the water. In contrast to lone males, which always attempt to copulate with females presented to them, guarding males exhibit a conditional latency to remating which corresponds closely to the time required by females to oviposit a complete clutch of eggs. By ovipositing underwater, females decrease the risk that their eggs become exposed. Risks associated with submerged oviposition favor both mate guarding, and multiple, within-clutch matings by females. Both guarding mates and lone males ‘rescue’ females that float on the water surface as the result of improper resurfacing. Such behavior reduces the mortality risk to females from 0.06 to 0.02 per oviposition bout. By remating between bouts, females benefit from the additional vigilance of lone males, who rescue floating females 1.4 times as often as original mates. A second consequence of multiple mating is an increase in the selective advantage of vigilance by mates. Because receptive females become scarce by early afternoon, whereas male density remains high, a male has little (3%) chance of encountering a second receptive female that day. However, he incurs a 42% risk of losing fertilizations if he abandons a mate. For male E. hageni mate guarding functions in the context of both natural and sexual selection. It insures that a mate lives to lay a complete egg clutch in addition to protecting a male's sperm investment.