cssi/SciSciGPT-SciSciNet huggingface dataset:
Electromigration Response of Microjoints in 3DIC Packaging Systems

In multilevel 3D integrated packaging, three major microstructures are viable due to the application of low volume of solders in different sizes, and/or processing conditions. Thermodynamics and kinetics of binary compounds in Cu/Sn/Cu low volume interconnection is taken into account. We show that current crowding effects can induce a driving force to cause excess vacancies saturate and ultimately cluster in the form of microvoids. A kinetic analysis is performed for electromigration mediated intermetallic growth using multi-phase-field approach. Faster growth of intermetallic compounds (IMCs) in anode layer in the expense of shrinkage of cathode IMC layer in shown. This work paves the road for computationally study the ductile failure due to formation of microvoids in low volume solder interconnects in 3DICs.
Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients

Septic shock is a devastating health condition caused by uncontrolled sepsis. Advancements in high-throughput sequencing techniques have increased the number of potential genetic biomarkers under review. Multiple genetic markers and functional pathways play a part in development and progression of pediatric septic shock. We identified 53 differentially expressed pediatric septic shock biomarkers using gene expression data sampled from 181 patients admitted to the pediatric intensive care unit within the first 24 hours of their admission. The gene expression signatures showed discriminatory power between pediatric septic shock survivors and nonsurvivor types. Using functional enrichment analysis of differentially expressed genes, we validated the known genes and pathways in septic shock and identified the unexplored septic shock-related genes and functional groups. Differential gene expression analysis revealed the genes involved in the immune response, chemokine-mediated signaling, neutrophil chemotaxis, and chemokine activity and distinguished the septic shock survivor from non-survivor. The identification of the septic shock gene biomarkers may facilitate in septic shock diagnosis, treatment, and prognosis.
Propagation Networks for Model-Based Control Under Partial Observation

There has been an increasing interest in learning dynamics simulators for model-based control. Compared with off-the-shelf physics engines, a learnable simulator can quickly adapt to unseen objects, scenes, and tasks. However, existing models like interaction networks only work for fully observable systems; they also only consider pairwise interactions within a single time step, both restricting their use in practical systems. We introduce Propagation Networks (PropNet), a differentiable, learnable dynamics model that handles partially observable scenarios and enables instantaneous propagation of signals beyond pairwise interactions. With these innovations, our propagation networks not only outperform current learnable physics engines in forward simulation, but also achieves superior performance on various control tasks. Compared with existing deep reinforcement learning algorithms, model-based control with propagation networks is more accurate, efficient, and generalizable to novel, partially observable scenes and tasks.
Low-latency Visual SLAM with Appearance-Enhanced Local Map Building

A local map module is often implemented in modern VO/VSLAM systems to improve data association and pose estimation. Conventionally, the local map contents are determined by co-visibility. While co-visibility is cheap to establish, it utilizes the relatively-weak temporal prior (i.e. seen before, likely to be seen now), therefore admitting more features into the local map than necessary. This paper describes an enhancement to co-visibility local map building by incorporating a strong appearance prior, which leads to a more compact local map and latency reduction in downstream data association. The appearance prior collected from the current image influences the local map contents: only the map features visually similar to the current measurements are potentially useful for data association. To that end, mapped features are indexed and queried with Multi-index Hashing (MIH). An online hash table selection algorithm is developed to further reduce the query overhead of MIH and the local map size. The proposed appearance-based local map building method is integrated into a state-of-the-art VO/VSLAM system. When evaluated on two public benchmarks, the size of the local map, as well as the latency of real-time pose tracking in VO/VSLAM are significantly reduced. Meanwhile, the VO/VSLAM mean performance is preserved or improves.
On bodies in $\mathbb R^5$ with directly congruent projections or sections

Let K and L be two convex bodies in R5 with countably many diameters, such that their projections onto all 4 dimensional subspaces containing one fixed diameter are directly congruent. We show that if these projections have no rotational symmetries, and the projections of K,L on certain 3 dimensional subspaces have no symmetries, then K=±L up to a translation. We also prove the corresponding result for sections of star bodies.
Efficient Implementation of Second-Order Stochastic Approximation Algorithms in High-Dimensional Problems

Stochastic approximation (SA) algorithms have been widely applied in minimization problems when the loss functions and/or the gradient information are only accessible through noisy evaluations. Stochastic gradient (SG) descent–a first-order algorithm and a workhorse of much machine learning–is perhaps the most famous form of SA. Among all SA algorithms, the second-order simultaneous perturbation stochastic approximation (2SPSA) and the second-order stochastic gradient (2SG) are particularly efficient in handling high-dimensional problems, covering both gradient-free and gradient-based scenarios. However, due to the necessary matrix operations, the per-iteration floating-point-operations (FLOPs) cost of the standard 2SPSA/2SG is $O(p^{3})$ , where $p$ is the dimension of the underlying parameter. Note that the $O(p^{3})$ FLOPs cost is distinct from the classical SPSA-based per-iteration $O(1)$ cost in terms of the number of noisy function evaluations. In this work, we propose a technique to efficiently implement the 2SPSA/2SG algorithms via the symmetric indefinite matrix factorization and show that the FLOPs cost is reduced from $O(p^{3})$ to $O(p^{2})$ . The formal almost sure convergence and rate of convergence for the newly proposed approach are directly inherited from the standard 2SPSA/2SG. The improvement in efficiency and numerical stability is demonstrated in two numerical studies.
PhenoGeneRanker: A Tool for Gene Prioritization Using Complete Multiplex Heterogeneous Networks

Uncovering genotype-phenotype relationships is a fundamental challenge in genomics. Gene prioritization is an important step for this endeavor to make a short manageable list from a list of thousands of genes coming from high-throughput studies. Network propagation methods are promising and state of the art methods for gene prioritization based on the premise that functionally-related genes tend to be close to each other in the biological networks. In this study, we present PhenoGeneRanker, an improved version of a recently developed network propagation method called Random Walk with Restart on Multiplex Heterogeneous Networks (RWR-MH). PhenoGeneRanker allows multi-layer gene and disease networks. It also calculates empirical p-values of gene ranking using random stratified sampling of genes based on their connectivity degree in the network. We ran PhenoGeneRanker using multi-omics datasets of rice to effectively prioritize the cold tolerance-related genes. We observed that top genes selected by PhenoGeneRanker were enriched in cold tolerance-related Gene Ontology (GO) terms whereas bottom ranked genes were enriched in general GO terms only. We also observed that top-ranked genes exhibited significant p-values suggesting that their rankings were independent of their degree in the network.
Stochastic simulation and parameter estimation of enzyme reaction models

The development of models and estimators that can satisfactorily describe enzyme reactions are extremely valuable to understand life processes. In this paper, the problem of modeling and parameter estimation of enzyme reactions from a stochastic perspective is addressed. The simulation results show that obtained estimators are adequate and accurate enough for this type of systems.
A new pillared lithium bismuth phosphate, LiBi7.37P3O19, with elliptical channels

The structure of a new lithium bismuth phosphate, LiBi(7.37)P(3)O(19), consists of infinite OBi(4) edge-sharing tetrahedral chains in the ac plane, which form Bi(2)O(2) layers parallel to the b axis. They are sandwiched between PO(4) tetrahedral and Bi polyhedral layers. The PO(4)-Bi-PO(4) layers are bridged by columns formed by one Bi polyhedron flanked on each side by LiO(4) tetrahedra. This bridging Bi atom lies on a twofold axis, special position 4e of the C2/c space group. This arrangement creates pillared open elliptical channels parallel to [010].
An assessment of transparency and reproducibility-related research practices in otolaryngology.

OBJECTIVES/HYPOTHESIS Clinical research serves as the foundation for evidence-based patient care, and reproducibility of results is consequently critical. We sought to assess the transparency and reproducibility of research studies in otolaryngology by evaluating a random sample of publications in otolaryngology journals between 2014 and 2018. STUDY DESIGN Review of published literature for reproducible and transparent research practices. METHODS We used the National Library of Medicine catalog to identify otolaryngology journals that met the inclusion criteria (available in the English language and indexed in MEDLINE). From these journals, we extracted a random sample of 300 publications using a PubMed search for records published between January 1, 2014 and December 31, 2018. Specific indicators of reproducible and transparent research practices were evaluated in a blinded, independent, and duplicate manner using a pilot-tested Google form. RESULTS Our initial search returned 26,498 records, from which 300 were randomly selected for analysis. Of these 300 records, 286 met inclusion criteria and 14 did not. Among the empirical studies, 2% (95% confidence interval [CI]: 0.4%-3.5%) of publications indicated that raw data were available, 0.6% (95% CI: 0.3%-1.6%) reported an analysis script, 5.3% (95% CI: 2.7%-7.8%) were linked to an accessible research protocol, and 3.9% (95% CI: 1.7%-6.1%) were preregistered. None of the publications had a clear statement claiming to replicate, or to be a replication of, another study. CONCLUSIONS Inadequate reproducibility practices exist in otolaryngology. Nearly all studies in our analysis lacked a data or material availability statement, did not link to an accessible protocol, and were not preregistered. Taking steps to improve reproducibility would likely improve patient care. LEVEL OF EVIDENCE NA Laryngoscope, 130: 1894-1901, 2020.
InGaN / GaN nanostructures for efficient LEDs

GaN nanorods and nanosheets with non-polar facets are used as templates to form InGaN QW active regions for LEDs on the nonpolar facets. Uniform, narrow spectra light emitting regions are formed on the nonpolar facets.
Corticolimbic Circuit Structure Moderates an Association Between Early Life Stress and Later Trait Anxiety

Abstract Childhood adversity is associated with a wide range of negative behavioral and neurodevelopmental consequences. However, individuals vary substantially in their sensitivity to such adversity. Here, we examined how individual variability in structural features of the corticolimbic circuit, which plays a key role in emotional reactivity, moderates the association between childhood adversity and later trait anxiety in 798 young adult university students. Consistent with prior research, higher self-reported childhood adversity was significantly associated with higher self-reported trait anxiety. However, this association was attenuated in participants with higher microstructural integrity of the uncinate fasciculus and greater thickness of the orbitofrontal cortex. These structural properties of the corticolimbic circuit may capture a neural profile of relative resiliency to early life stress, especially against the negative effects of childhood adversity on later trait anxiety.
Lyubeznik numbers for Pfaffian rings

Abstract We study the structure of local cohomology with support in Pfaffian varieties as a module over the Weyl algebra D X of differential operators on the space of skew-symmetric matrices X = ⋀ 2 C n . The simple composition factors of these modules are known by the work of Raicu-Weyman, and when n is odd, the general theory implies that the local cohomology modules are semi-simple. When n is even, we show that the local cohomology is a direct sum of indecomposable modules coming from the pole order filtration of the Pfaffian hypersurface. We then determine the Lyubeznik numbers for Pfaffian rings by computing local cohomology with support in the origin of the indecomposable summands referred to above.
70 Years of the International Law Commission, Its Future Role in the Changing Landscape of International Law and the Small-Developing States Nexus

The International Law Commission (ILC) in its 70 illustrious years has been credited with its acclaimed pivotal role in the progressive development of international law and its codification. However, given that the principle of consensus underpins the progressive development of international law and its codification, how much of this process has involved and incorporated the perspectives and needs of Small and Developing States? In the immediate aftermath of commemorating the 70th Anniversary of the ILC, this paper measures the level of participation by Small and Developing Sates and examines the future role of the ILC through the lens of its relationship with the Sixth Committee of the United Nations General Assembly. It asserts that the envisioned symbiosis and optimal actualization of the extensive engagement principle in the progressively development of international law and its codification is being inhibited by the current working relationship between the ILC and the Sixth Committee, coupled with the lack of resources and capacity on the part of Small and Developing States.
Biophysical models of cis-regulation as interpretable neural networks

Abstract The adoption of deep learning techniques in genomics has been hindered by the difficulty of mechanistically interpreting the models that these techniques produce. In recent years, a variety of post-hoc attribution methods have been proposed for addressing this neural network interpretability problem in the context of gene regulation. Here we describe a complementary way to address this problem. Our approach is based on the observation that two large classes of biophysical models for cis-regulatory mechanisms can be expressed as deep neural networks in which nodes and weights have explicit physiochemical interpretations. We also demonstrate how such biophysical networks can be rapidly learned, using modern deep learning frameworks, from the data produced by certain types of massively parallel reporter assays (MPRAs). These results suggest a scalable strategy for using MPRAs to systematically characterize the biophysical basis of gene regulation in a wide range of biological contexts. They also highlight gene regulation as an ideal venue for the development of scientifically interpretable approaches to deep learning.
Area spectral efficiency of cellular mobile radio systems

A general analytical framework quantifying the spectral efficiency of cellular systems with variable-rate transmission is introduced. This efficiency, the area spectral efficiency, defines the sum of the maximum average data rates per unit bandwidth per unit area supported by a cell's base station. Expressions for this efficiency as a function of the reuse distance for the worst and best case interference configurations are derived. Moreover, Monte Carlo simulations are developed to estimate the value of this efficiency for average interference conditions. Both fully loaded and partially loaded cellular systems are investigated. The effect of random user location is taken into account, and the impact of lognormal shadowing and Nakagami (1960) multipath fading is also studied.
Thermal stress responses of Sodalis glossinidius, an indigenous bacterial symbiont of hematophagous tsetse flies.

Tsetse flies (Diptera: Glossinidae) house a taxonomically diverse microbiota that includes environmentally acquired bacteria, maternally transmitted symbiotic bacteria, and pathogenic African trypanosomes. Sodalis glossinidius, which is a facultative symbiont that resides intra and extracellularly within multiple tsetse tissues, has been implicated as a mediator of trypanosome infection establishment in the fly’s gut. Tsetse’s gut-associated population of Sodalis are subjected to marked temperature fluctuations each time their ectothermic fly host imbibes vertebrate blood. The molecular mechanisms that Sodalis employs to deal with this heat stress are unknown. In this study, we examined the thermal tolerance and heat shock response of Sodalis. When grown on BHI agar plates, the bacterium exhibited the most prolific growth at 25oC, and did not grow at temperatures above 30oC. Growth on BHI agar plates at 31°C was dependent on either the addition of blood to the agar or reduction in oxygen levels. Sodalis was viable in liquid cultures for 24 hours at 30oC, but began to die upon further exposure. The rate of death increased with increased temperature. Similarly, Sodalis was able to survive for 48 hours within tsetse flies housed at 30oC, while a higher temperature (37oC) was lethal. Sodalis’ genome contains homologues of the heat shock chaperone protein-encoding genes dnaK, dnaJ, and grpE, and their expression was up-regulated in thermally stressed Sodalis, both in vitro and in vivo within tsetse fly midguts. Arrested growth of E. coli dnaK, dnaJ, or grpE mutants under thermal stress was reversed when the cells were transformed with a low copy plasmid that encoded the Sodalis homologues of these genes. The information contained in this study provides insight into how arthropod vector enteric commensals, many of which mediate their host’s ability to transmit pathogens, mitigate heat shock associated with the ingestion of a blood meal.
On the Yang–Yau inequality for the first Laplace eigenvalue

In a seminal paper published in 1980, P. C. Yang and S.-T. Yau proved an inequality bounding the first eigenvalue of the Laplacian on an orientable Riemannian surface in terms of its genus $$\gamma $$ and the area. The equality in Yang–Yau’s estimate is attained for $$\gamma =0$$ by an old result of J. Hersch and it was recently shown by S. Nayatani and T. Shoda that it is also attained for $$\gamma =2$$. In the present article we combine techniques from algebraic geometry and minimal surface theory to show that Yang–Yau’s inequality is strict for all genera $$\gamma >2$$. Previously this was only known for $$\gamma =1$$. In the second part of the paper we apply Chern-Wolfson’s notion of harmonic sequence to obtain an upper bound on the total branching order of harmonic maps from surfaces to spheres. Applications of these results to extremal metrics for eigenvalues are discussed.
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks

Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time. Previously proposed solutions require complex inductive biases inside network architectures with highly specialized computation, including segmentation masks, optical flow, and foreground and background separation. In this work, we question if such handcrafted architectures are necessary and instead propose a different approach: finding minimal inductive bias for video prediction while maximizing network capacity. We investigate this question by performing the first large-scale empirical study and demonstrate state-of-the-art performance by learning large models on three different datasets: one for modeling object interactions, one for modeling human motion, and one for modeling car driving.
Sharp bounds on the relative treatment effect for ordinal outcomes

For ordinal outcomes, the average treatment effect is often ill-defined and hard to interpret. Echoing Agresti and Kateri, we argue that the relative treatment effect can be a useful measure, especially for ordinal outcomes, which is defined as γ=pr{Yi(1)>Yi(0)}-pr{Yi(1)<Yi(0)} , with Yi(1) and Yi(0) being the potential outcomes of unit i under treatment and control, respectively. Given the marginal distributions of the potential outcomes, we derive the sharp bounds on γ, which are identifiable parameters based on the observed data. Agresti and Kateri focused on modeling strategies under the assumption of independent potential outcomes, but we allow for arbitrary dependence.
Area of scalar isosurfaces in homogeneous isotropic turbulence as a function of Reynolds and Schmidt numbers

A fundamental effect of fluid turbulence is turbulent mixing, which results in the stretching and wrinkling of scalar isosurfaces. Thus, the area of isosurfaces is of interest in understanding turbulence in general, with specific applications in, for example, combustion and the identification of turbulent/non-turbulent interfaces. We report measurements of isosurface areas in 28 direct numerical simulations (DNS) of homogeneous isotropic turbulence with a mean scalar gradient resolved on up to .
How weather affects the decomposition of total factor productivity in U.S. agriculture

This study illustrates and quantifies how overlooking the impact of weather shocks can affect the measurement and decomposition of agricultural total factor productivity (TFP) change. The underlying technology is represented by a flexible input distance function with quasi‐fixed inputs estimated with Bayesian methods. Using agricultural production and weather data for 16 states in the Pacific Region, Central Region, and Southern Plains of the United States, we estimate TFP change as the direct sum of multiple components, including a net weather effect. To assess the role of weather, we conduct a comparative analysis based on two distinct sets of input and output variables. A traditional set of variables that ignore weather variations, and a new set of “weather‐filtered” variables that represent input and output levels that would have been chosen under average weather conditions. From this comparative analysis, we derive biases in the decomposition of TFP growth from the omission of weather shocks. We find that weather shocks accelerated productivity growth in 12 out of 16 states by the equivalent of 11.4% of their group‐average TFP growth, but slowed down productivity by the equivalent of 6.5% of the group‐average TFP growth in the other four states (located in the Northern‐most part of the country). We also find substantial biases in the estimated contribution of technical change, scale effects, technical efficiency change, and output allocation effects to TFP growth (varying in magnitude and direction across regions) when weather effects are excluded from the model. This is the first study to present estimates of those biases based on a counterfactual analysis. One major implication from our study is that the official USDA's measures of TFP change would appear to overestimate the rate of productivity growth in U.S. agriculture stemming from technical change, market forces, agricultural policies, and other nonweather drivers.
Comparison of the Scaling Properties of EUV Intensity Fluctuations in Coronal Hole and Quiet-Sun Regions

Using detrended fluctuation analysis (DFA) and rescaled range (R/S) analysis, we investigate the scaling properties of EUV intensity fluctuations of low-latitude coronal holes (CHs) and neighboring quiet-Sun (QS) regions in signals obtained with the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) instrument. Contemporaneous line-of-sight SDO/Helioseismic and Magnetic Imager (HMI) magnetic fields provide a context for the physical environment. We find that the intensity fluctuations in the time series of EUV images present at each spatial point a scaling symmetry over the range $\sim 20$ min to $\sim$ 1 hour. Thus we are able to calculate a generalized Hurst exponent and produce image maps, not of physical quantities like intensity or temperature, but of a single dynamical parameter that sums up the statistical nature of the intensity fluctuations at each pixel. In quiet-Sun (QS) regions and in coronal holes (CHs) with magnetic bipoles, the scaling exponent ($1.0 < \alpha \leq 1.5$) corresponds to anti-correlated turbulent-like processes. In coronal holes, and in quiet-Sun regions primarily associated with (open) magnetic field of dominant polarity, the generalized exponent (0.5 $< \alpha <$ 1) corresponds to positively-correlated (persistent) processes. We identify a tendency for $\alpha$ $\sim$ $1$ near coronal hole boundaries and in other regions in which open and closed magnetic fields are in proximity. This is a signature of an underlying $1/f$ type process that is characteristic for self-organized criticality and shot-noise models.
Determination and validation of mycophenolic acid by a UPLC-MS/MS method: Applications to pharmacokinetics and tongue tissue distribution studies in rats.

Abstract Mycophenolic acid (MPA) has being used clinically for organ rejection prophylaxis. Recent studies have revealed that MPA can also act as a chemo-sensitizing agent when used in combination with various chemotherapeutic agents in a cancer type-specific manner, including with oxaliplatin on oral squamous cell carcinoma (OSCC) cells. To prepare for the analysis of a novel drug delivery route for MPA absorption via oral mucosa as a potential therapeutic product, it is essential to develop and validate a highly sensitive analytical method for the quantification of MPA in biological samples for pharmacokinetic and tissue distribution studies. Herein, we report a sensitive, specific and reproducible UPLC-MS/MS method to do so. Blank rat plasma or tongue tissue homogenates coupled with griseofulvin, as internal standard, was used for generating standard curves ranging from 0.5 to 1000 ng/mL (r > 0.9990) for both plasma and tongue tissue homogenates. The chromatographic separation was achieved by a reverse phase ACE Excel 2 Super C18 column with a flow rate of 0.4 mL/min under gradient elution. Mass detection was performed under positive ionization electrospray. Inter- and intra-day accuracy and precision of the assay were ≤15% in both plasma and tongue tissue homogenates. The matrix effect was non-significant and extraction recovery rates were within 87.99% and 109.69% in plasma and tongue homogenates, respectively. The validity of this assay has been confirmed by measuring MPA in rat plasma for pharmacokinetics following intravenous administration of 0.5 mg/kg of mycophenolate sodium, as well as monitoring MPA in rat tongues for tissue distribution and detecting MPA that diffused into systemic circulation following a 4-h transmucosal delivery of 357 μg/cm2 of mycophenolate sodium.
On Computable Geometric Expressions in Quantum Theory

Geometric Algebra and Calculus are mathematical languages that encode fundamental geometric relations that theories of physics must respect, and eliminate from our vocabulary those they do not. We propose criteria given which statistics of expressions in geometric algebra are computable in quantum theory, in such a way that preserves their algebraic character. They are that one must be able to arbitrarily transform the basis of the Clifford algebra, via multiplication by elements of the algebra that act trivially on the state space; all such elements must be neighbored by operators corresponding to factors in the original expression and not the state vectors. We explore the consequences of these criteria for a physics of dynamical multivector fields.
A role for phagocytosis in inducing cell death during thymocyte negative selection

Autoreactive thymocytes are eliminated during negative selection in the thymus, a process important for establishing self-tolerance. Thymic phagocytes serve to remove dead thymocytes, but whether they play additional roles during negative selection remains unclear. Here, using a murine thymic slice model in which thymocytes undergo negative selection in situ, we demonstrate that phagocytosis promotes negative selection, and provide evidence for the escape of autoreactive CD8 T cells to the periphery when phagocytosis in the thymus is impaired. We also show that negative selection is more efficient when the phagocyte also presents the negative selecting peptide. Our findings support a model for negative selection in which the death process initiated following strong TCR signaling is facilitated by phagocytosis. Thus, the phagocytic capability of cells that present self-peptides is a key determinant of thymocyte fate.
Correlators in the $\mathcal{N}=2$ Supersymmetric SYK Model

We study correlation functions in the one-dimensional $$ \mathcal{N}=2 $$ supersymmetric SYK model. The leading order 4-point correlation functions are computed by summing over ladder diagrams expanded in a suitable basis of conformal eigenfunctions. A novelty of the $$ \mathcal{N}=2 $$ model is that both symmetric and antisymmetric eigenfunctions are required. Although we use a component formalism, we verify that the operator spectrum and 4-point functions are consistent with $$ \mathcal{N}=2 $$ supersymmetry. We also confirm the maximally chaotic behavior of this model and comment briefly on its 6-point functions.
Multirobot Coordination With Counting Temporal Logics

In many multirobot applications, planning trajectories in a way to guarantee that the collective behavior of the robots satisfies a certain high-level specification is crucial. Motivated by this problem, we introduce counting temporal logics —formal languages that enable concise expression of multirobot task specifications over possibly infinite horizons in this article. We first introduce a general logic called counting linear temporal logic plus (cLTL+), and propose an optimization-based method that generates individual trajectories such that satisfaction of a given cLTL+ formula is guaranteed when these trajectories are synchronously executed. We then introduce a fragment of cLTL+, called counting linear temporal logic (cLTL), and show that a solution to a planning problem with cLTL constraints can be obtained more efficiently if all robots have identical dynamics. In the second part of this article, we relax the synchrony assumption and discuss how to generate trajectories that can be asynchronously executed, while preserving the satisfaction of the desired cLTL+ specification. In particular, we show that when the asynchrony between robots is bounded, the method presented in this article can be modified to generate robust trajectories. We demonstrate these ideas with an experiment and provide numerical results that showcase the scalability of the method.
PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design

The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices. Owing to the large parameter space and cost of evaluating each parameter in the search space, manually tuning of DNN hyperparameters is impractical. Automatic joint DNN and hardware hyperparameter optimization is indispensable for such problems. Bayesian optimization-based approaches have shown promising results for hyperparameter optimization of DNNs. However, most of these techniques have been developed without considering the underlying hardware, thereby leading to inefficient designs. Further, the few works that perform joint optimization are not generalizable and mainly focus on CMOS-based architectures. In this work, we present a novel pseudo agent-based multiobjective hyperparameter optimization (PABO) for maximizing the DNN performance while obtaining low hardware cost. Compared to the existing methods, our work poses a theoretically different approach for joint optimization of accuracy and hardware cost and focuses on memristive crossbar based accelerators. PABO uses a supervisor agent to establish connections between the posterior Gaussian distribution models of network accuracy and hardware cost requirements. The agent reduces the mathematical complexity of the co-optimization problem by removing unnecessary computations and updates of acquisition functions, thereby achieving significant speed-ups for the optimization procedure. PABO outputs a Pareto frontier that underscores the trade-offs between designing high-accuracy and hardware efficiency. Our results demonstrate a superior performance compared to the state-of-the-art methods both in terms of accuracy and computational speed (∼100x speed up).
Device-to-Device Coded-Caching With Distinct Cache Sizes

This paper considers a cache-aided device-to-device (D2D) system where the users are equipped with cache memories of different size. During low traffic hours, a server places content in the users’ cache memories, knowing that the files requested by the users during peak traffic hours will have to be delivered by D2D transmissions only. The worst-case D2D delivery load is minimized by jointly designing the uncoded cache placement and linear coded D2D delivery. Next, a novel lower bound on the D2D delivery load with uncoded placement is proposed and used in explicitly characterizing the minimum D2D delivery load (MD2DDL) with uncoded placement for several cases of interest. In particular, having characterized the MD2DDL for equal cache sizes, it is shown that the same delivery load can be achieved in the network with users of unequal cache sizes, provided that the smallest cache size is greater than a certain threshold. The MD2DDL is also characterized in the small cache size regime, the large cache size regime, and the three-user case. Comparisons of the server-based delivery load with the D2D delivery load are provided. Finally, connections and mathematical parallels between cache-aided D2D systems and coded distributed computing (CDC) systems are discussed.
Dynamical models for Liouville and obstructions to further progress on sign patterns

Abstract We define a class of dynamical systems by modifying a construction due to Tao, which includes certain Furstenburg limits arising from the Liouville function. Most recent progress on the Chowla conjectures and sign patterns of the Mobius and Liouville functions uses methods that apply to any dynamical system in this class. Hence dynamical systems in this class with anomalous local behavior present obstructions to further progress on these problems by the same techniques. We construct straightforward examples of dynamical systems in this class based on polynomial phases and calculate the resulting obstruction. This requires explicit bounds for the number of sign patterns arising in a certain way from polynomials, which is elementary but not completely trivial.
Effect of Hydrophilicity and Interfacial Water Structure on Particle Attachment

Oriented attachment (OA) of nanoparticles is an important and common crystallization pathway. Efforts to isolate and quantify the interparticle forces that underlie OA have only recently been initi...
Governance of the Internet of Things (IoT)

Today's increasing rate of technological change results from the rapid growth in computer processing speed, when combined with the cost decline of processing capacity, and is of historical import. The daily life of billions of individuals worldwide has been forever changed by technology in just the last few years. Costly data breaches continue at an alarming rate. The challenge facing humans as they attempt to govern the process of artificial intelligence, machine learning, and the impact of billions of sensory devices connected to the Internet is the subject of this Article. We proceed in nine sections. First, we define the Internet of Things (IoT), comment on the explosive growth in sensory devices connected to the Internet, provide examples of IoT devices, and speak to the promise of the IoT. Second, we discuss legal requirements for corporate governance as a foundation for considering the challenge of governing the IoT. Third, we look at potential IoT threats. Fourth, we discuss the Mirai botnet. Fifth, is a look at the IoT threat vector vulnerabilities during times of crisis. Sixth, we discuss the Manufactured Usage Description (MUD) methodology. Seventh, is a discussion of recent regulatory developments. Next, we look at a few recommendations. And finally, we conclude. We believe this Article contributes to our understanding of the widespread exposure to malware associated with IoT and adds to the nascent but emerging literature on governance of enterprise risk, a subject of vital societal importance.
Observation of Anomalous Non-Ohmic Transport in Current-Driven Nanostructures

Microstructure response to electrical current cannot be described as Joule heating, which warrants a reexamination of many observations of current-induced heating and suggests a new way to study the electron-phonon interaction.
Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.
Jumps in the convenience yield of crude oil

Abstract The potential presence of jumps and time-varying volatility in convenience yields can lead to abnormally fat tails, which has implications for investment in storage capacity, leasing and drilling for crude oil. In this paper we evaluate the potential for these features in convenience yields. To that end, we analyze the rate of change in convenience yields for five futures prices time horizons (1, 3-, 6-, 9- and 12-month ahead), allowing for the both jumps and time-varying volatility. We find that both features exert a statistically important effect on convenience yields, for each of the five time horizons. We also calculate the implied probability that at least one jump would occur on any date, which reveals a period of relative calm at the start of the fracking boom, when large stockpiles built up at the trading hub for West Texas Intermediate, and a period of considerable churn, after the ban on exporting crude oil was lifted. Both elements underscore a linkage between inventory holdings and convenience yields.
HodgeNet: Graph Neural Networks for Edge Data

Networks and network processes have emerged as powerful tools for modeling social interactions, disease propagation, and a variety of additional dynamics driven by relational structures. Recently, neural networks have been generalized to process data on graphs, thus being able to learn from the aforementioned network processes achieving cutting-edge performance in traditional tasks such as node classification and link prediction. However, these methods have all been formulated in a way suited only to data on the nodes of a graph. The application of these techniques to data supported on the edges of a graph, namely flow signals, has not been explored in detail. To bridge this gap, we propose the use of the so-called Hodge Laplacian combined with graph neural network architectures for the analysis of flow data. Specifically, we apply two graph neural network architectures to solve the problems of flow interpolation and source localization.
Constructing Cultures Then and Now

Constructing Cultures Then and Now:. Centenary Conference Celebrating Franz Boas and the Jesup North Pacific Expedition 1897-1997. American Museum of Natural History, New York, New York, November 13-17 1997.
SCATTER: Selective Context Attentional Scene Text Recognizer

Scene Text Recognition (STR), the task of recognizing text against complex image backgrounds, is an active area of research. Current state-of-the-art (SOTA) methods still struggle to recognize text written in arbitrary shapes. In this paper, we introduce a novel architecture for STR, named Selective Context ATtentional Text Recognizer (SCATTER). SCATTER utilizes a stacked block architecture with intermediate supervision during training, that paves the way to successfully train a deep BiLSTM encoder, thus improving the encoding of contextual dependencies. Decoding is done using a two-step 1D attention mechanism. The first attention step re-weights visual features from a CNN backbone together with contextual features computed by a BiLSTM layer. The second attention step, similar to previous papers, treats the features as a sequence and attends to the intra-sequence relationships. Experiments show that the proposed approach surpasses SOTA performance on irregular text recognition benchmarks by 3.7% on average.
New Technologies and Old Treaties

Every road vehicle must have a driver able to control it while in motion. These requirements, explicit in two important conventions on road traffic, have an uncertain relationship to the automated motor vehicles that are currently under development—often colloquially called “self-driving” or “driverless.” The immediate legal and policy questions are straightforward: Are these requirements consistent with automated driving and, if not, how should the inconsistency be resolved? More subtle questions go directly to international law's role in a world that artificial intelligence is helping to rapidly change: In a showdown between a promising new technology and an entrenched treaty regime, which prevails? Should international law bend to avoid breaking? If so, what kind of flexibility is appropriate with respect to both the status and the substance of treaty obligations? And what role should deliberate ambiguity play in addressing these obligations? This essay raises these questions through the concrete case of automated driving. It introduces the road traffic conventions, identifies competing interpretations of their core driver requirements, and highlights ongoing efforts at the Global Forum for Road Traffic Safety to reach a consensus.
Family firms and crash risk: Alignment and entrenchment effects

Abstract Stock price crash risk could be lower in family firms because the controlling family investors have a longer-term interest, hold greater decision rights and are better informed than investors in diffusely owned firms (alignment effect). However, the agency costs between family and nonfamily investors (entrenchment effect) could affect crash risk in two opposing ways. Non-controlling investor skepticism about insider entrenchment limits overvaluation and reduces the crash risk. In contrast, entrenched insiders could hide bad news to exploit private benefits, which could increase the crash risk. We show that family firms exhibit a lower crash risk than similar nonfamily firms after controlling for lower overvaluation, which is consistent with the better alignment effect. Moreover, we show that better governance further reduces the crash risk, which indicates that the substitutive relationship between strong governance and family ownership shown in countries with low investor protection rights does not carry over to the U.S. where investor protection rights are strong.
Microphase Separation in Triblock Copolymer Melts

Phase diagrams of the microphase morphologies for triblock copolymer melts are calculated for the weak segregation limit. The diagrams are found to be highly asymmetric with block copolymer composition.
Regression Discontinuity Design

The regression discontinuity design is a quasi-experimental design in which the experimenter assigns participants to treatment conditions on the basis of a cutoff score on an assignment variable – usually a measure of need or merit. This design yields unbiased estimates of treatment effects when certain assumptions are met. Just as in a randomized experiment, the selection process is both completely known and perfectly measured in the regression discontinuity design. Numerous variations of the design are possible, including combinations with randomized experiments. Keywords: quasi-experimental designs; threats to internal validity
Chapter 4 – Increased Intracranial Pressure

I ncreased intracranial pressure is not a presenting complaint ( Table 4–1 ). Most often, headache, vomiting, personality change, and alterations in states of consciousness bring it to medical attention. Less frequently, diplopia or the observation that one or both eyes are turning in is the initial complaint. The basis of referral for some children is the diagnosis of papilledema by another physician. Some conditions causing increased intracranial pressure are discussed elsewhere in this book (see Chapters 2 , 3 , 10 , and 15 ). This chapter is restricted to conditions in which symptoms of increased intracranial pressure are initial and prominent features.
The distributed snapshot of K.M. Chandy and L. Lamport

We consider a distributed system of the form of a strongly connected, finite, directed graph, of which each vertex is a machine and each edge a uni-directional first-in-first-out buffer of sufficient capacity. (Strongly connected means that there is a directed path from any vertex to any vertex.)
1 – Basic Properties and Definitions

Publisher Summary his chapter discusses the basic properties and definitions of intermediate algebra. It presents comparison symbols, operation symbols, exponents, and order of operations. A set is a collection of objects or things. The objects in the set are called elements or members of the set. There are two basic operations used to combine sets. The operations are union and intersection. The chapter also presents the definition of real numbers in terms of the real number line. The real number line is constructed by drawing a straight line and labeling a convenient point with the number 0. Positive numbers are in increasing order to the right of 0; negative numbers are in decreasing order to the left of 0. The point on the line corresponding to 0 is called the origin.
Sup-norm of Hecke-Laplace Eigenforms on $S^3$

We prove sub-convex bounds on the fourth moment of Hecke–Laplace eigenforms on $$S^3$$. As a corollary, we get a Burgess-type sub-convex bound on the sup-norm of an individual eigenform. This constitutes an improvement over what is achievable through employing the Iwaniec–Sarnak amplifier.