cssi/SciSciGPT-SciSciNet huggingface dataset:
The effects of vanadium substitution on one-dimensional tunnel structures of cryptomelane: Combined TEM and DFT study

Abstract Rational design of energy storage materials requires mechanistic understanding which relies on atomic scale characterization of structure and defects. Here we utilize vanadium (V)-substituted one-dimensional cryptomelane as a model material to study structural features critical to electrochemical reversibility of the cathode in lithium-ion batteries. We found the substitution of manganese (Mn) atoms in the tunnel wall with V enhances structural uniformity in the parent KMn8O16 material by inhibiting formation of irregular (2 × 3 and 2 × 1) manganese oxide tunnel structures. Upon lithiation, both transmission electron microscopy (TEM) and density functional theory (DFT) reveal significant structural evolution, eventually resulting in phase separation and formation of highly reduced manganese oxide. During this process, Mn4+ accepts electrons donated by intercalated Li, while the V4+ substituent is not electrochemically reduced but acts to stabilize the tunnel framework structure. As a result, the V substituted material shows higher operating voltage and improved electrochemical reversibility. Our results highlight the benefits of V-substitution in cryptomelane, and the significance of integrating characterization with simulation for understanding and developing design principles for lithium ion battery materials.
Rate-Distortion-Memory Trade-Offs in Heterogeneous Caching Networks

Caching at the wireless edge can be used to keep up with the increasing demand for high-definition wireless video streaming. By prefetching popular content into memory at wireless access points or end-user devices, requests can be served locally, relieving strain on expensive backhaul. In addition, using network coding allows the simultaneous serving of distinct cache misses via common coded multicast transmissions, resulting in significantly larger load reductions compared to those achieved with traditional delivery schemes. Most prior works simply treat video content as fixed-size files that users would like to fully download. This work is motivated by the fact that video can be coded in a scalable fashion and that the decoded video quality depends on the number of layers a user receives in sequence. Using a Gaussian source model, caching and coded delivery methods are designed to minimize the squared error distortion at end-user devices in a rate-limited caching network. The framework is very general and accounts for heterogeneous cache sizes, video popularities and user-file play-back qualities. As part of the solution, a new decentralized scheme for lossy cache-aided delivery subject to preset user distortion targets is proposed, which further generalizes prior literature to a setting with file heterogeneity.
Fluidized bed regenerators for Brayton cycles

A recuperator consisting of two fluidized bed regenerators with circulating solid particles is considered for use in a Brayton cycle. These fluidized beds offer the possibility of high temperature operation if ceramic particles are used. Calculations of the efficiency and size of fluidized bed regenerators for typical values of operating parameters were made and compared to a shell and tube recuperator. The calculations indicate that the fluidized beds will be more compact than the shell and tube as well as offering a high temperature operating capability.
The SK Model Is Infinite Step Replica Symmetry Breaking at Zero Temperature

We prove that the Parisi measure of the mixed p-spin model at zero temperature has infinitely many points in its support. This establishes Parisi's prediction that the functional order parameter of the Sherrington-Kirkpatrick model is not a step function at zero temperature. As a consequence, we show that the number of levels of broken replica symmetry in the Parisi formula of the free energy diverges as the temperature goes to zero.
Using recursive partitioning to find and estimate heterogenous treatment effects in randomized clinical trials

When for an RCT heterogeneous treatment effects are inductively obtained, significant complications are introduced. Special loss functions may be needed to find local, average treatment effects followed by techniques that properly address post-selection statistical inference. Reanalyzing a recidivism RCT, we use a new form of classification trees to seek heterogeneous treatment effects and then correct for “data snooping” with novel inferential procedures. There are perhaps increases in recidivism for a small subset of offenders whose risk factors place them toward the right tail of the risk distribution. A legitimate but partial account for uncertainty might well reject the null hypothesis of no heterogenous treatment effects. An equally legitimate but far more complete account of uncertainty for this study fails to reject the null hypothesis of no heterogeneous treatment effects.
Causal relationships between NAFLD, T2D and obesity have implications for disease subphenotyping.

Background & Aims Non-alcoholic fatty liver disease (NAFLD), type 2 diabetes (T2D) and obesity are epidemiologically correlated with each other but the causal inter-relationships between them remain incompletely understood. We aimed to explore the causal relationships between the 3 diseases. Methods Using both UK Biobank and publicly available genome-wide association study data, we performed a 2-sample bidirectional Mendelian randomization analysis to test the causal inter-relationships between NAFLD, T2D, and obesity. Transgenic mice expressing the human PNPLA3-I148M isoforms (TghPNPLA3-I148M) were used as an example to validate causal effects and explore underlying mechanisms. Results Genetically driven NAFLD significantly increased the risk of T2D and central obesity but not insulin resistance or generalized obesity, while genetically driven T2D, body mass index and WHRadjBMI causally increased NAFLD risk. The animal study focusing on PNPLA3 corroborated these causal effects: compared to the TghPNPLA3-I148I controls, the TghPNPLA3-I148M mice developed glucose intolerance and increased visceral fat, but maintained normal insulin sensitivity, reduced body weight, and decreased circulating total cholesterol. Mechanistically, the TghPNPLA3-I148M mice demonstrated decreased pancreatic insulin but increased glucagon secretion, which was associated with increased pancreatic inflammation. In addition, transcription of hepatic cholesterol biosynthesis pathway genes was significantly suppressed, while transcription of thermogenic pathway genes was activated in subcutaneous and brown adipose tissues but not in visceral fat in TghPNPLA3-I148M mice. Conclusions Our study suggests that lifelong, genetically driven NAFLD causally promotes T2D with a late-onset type 1-like diabetic subphenotype and central obesity; while genetically driven T2D, obesity, and central obesity all causally increase the risk of NAFLD. This causal relationship revealed new insights into how nature and nurture drive these diseases, providing novel hypotheses for disease subphenotyping. Lay summary Non-alcoholic fatty liver disease, type 2 diabetes and obesity are epidemiologically correlated with each other, but their causal relationships were incompletely understood. Herein, we identified causal relationships between these conditions, which suggest that each of these closely related diseases should be further stratified into subtypes. This is important for accurate diagnosis, prevention and treatment of these diseases.
Decentralized network connection preemption algorithms

Connection preemption provides available and reliable services to high-priority connections when a network is heavily loaded and connection request arrival patterns are unknown, or when the network experiences link or node failures. Coupled with the capability to reroute connections (preempted due to failure or preemption), connection preemption allows a high quality of service to be provided to network connections and bandwidth to be used more efficiently. The main contributions of this paper are the following. It presents a comprehensive simulation study of preemption in a general connection-oriented network setting. Our simulation study also provides useful insights into connection preemption and network dimensioning problems in order to achieve a desired level of network availability. Based on the observations made in this study, we designed two connection preemption selection algorithms that operate in a decentralized/distributed network where individual link managers run the algorithm for connection preemption selection on their outgoing links. The first algorithm optimizes the criteria of (i) the bandwidth to be preempted, (ii) the priority of connections to be preempted, and (iii) the number of connections to be preempted, in that order, and has exponential complexity. The second algorithm optimizes the criteria of (i) the number of connections to be preempted, (ii) the bandwidth to be preempted, and (iii) the priority of connections to be preempted, in that order, and has polynomial complexity. From a comparison study of these two algorithms we conclude that the polynomial algorithm is almost as good as the exponential algorithm in terms of overall network performance.
A minimizing valuation is quasi-monomial

We prove a version of Jonsson-Mustaţǎ's Conjecture, which says for any graded sequence of ideals, there exists a quasi-monomial valuation computing its log canonical threshold. As a corollary, we confirm Chi Li's conjecture that a minimizer of the normalized volume function is always quasi-monomial. Applying our techniques to a family of klt singularities, we show that the volume of klt singularities is a constructible function. As a corollary, we prove that in a family of klt log Fano pairs, the K-semistable ones form a Zariski open set. Together with [Jia17], we conclude that all K-semistable klt Fano varieties with a fixed dimension and volume are parametrized by an Artin stack of finite type, which then admits a separated good moduli space by [BX18, ABHLX19], whose geometric points parametrize K-polystable klt Fano varieties.
Antibody targeting of B7-H4 enhances the immune response in urothelial carcinoma

Patients with locally advanced and metastatic urothelial carcinoma have a low survival rate (median 15.7 months, 13.1–17.8), with only a 23% response rate to monotherapy treatment with anti-PDL1 ch...
Creation and Evolution of Impact-generated Reduced Atmospheres of Early Earth

The origin of life on Earth seems to demand a highly reduced early atmosphere, rich in CH4, H2, and NH3, but geological evidence suggests that Earth's mantle has always been relatively oxidized and its emissions dominated by CO2 H2O, and N2. The paradox can be resolved by exploiting the reducing power inherent in the "late veneer," i.e., material accreted by Earth after the Moon-forming impact. Isotopic evidence indicates that the late veneer consisted of extremely dry, highly reduced inner solar system materials, suggesting that Earth's oceans were already present when the late veneer came. The major primary product of reaction between the late veneer's iron and Earth's water was H2. Ocean vaporizing impacts generate high pressures and long cooling times that favor CH4 and NH3. Impacts too small to vaporize the oceans are much less productive of CH4 and NH3, unless (i) catalysts were available to speed their formation, or (ii) additional reducing power was extracted from pre-existing crustal or mantle materials. The transient H2-CH4 atmospheres evolve photochemically to generate nitrogenated hydrocarbons at rates determined by solar radiation and hydrogen escape, on timescales ranging up to tens of millions of years and with cumulative organic production ranging up to half a kilometer. Roughly one ocean of hydrogen escapes. The atmosphere after the methane's gone is typically H2 and CO rich, with eventual oxidation to CO2 rate-limited by water photolysis and hydrogen escape.
A Unified 3D Mapping Framework Using a 3D or 2D LiDAR

Simultaneous Localization and Mapping (SLAM) has been considered as a solved problem thanks to the progress made in the past few years. However, the great majority of LiDAR-based SLAM algorithms are designed for a specific type of payload and therefore don’t generalize across different platforms. In practice, this drawback causes the development, deployment and maintenance of an algorithm difficult. Consequently, our work focuses on improving the compatibility across different sensing payloads. Specifically, we extend the Cartographer SLAM library to handle different types of LiDAR including fixed or rotating, 2D or 3D LiDARs. By replacing the localization module of Cartographer and maintaining the sparse pose graph (SPG), the proposed framework can create high-quality 3D maps in real-time on different sensing payloads. Additionally, it brings the benefit of simplicity with only a few parameters need to be adjusted for each sensor type.
Relative Accuracy of Judgmental and Extrapolative Methods in Forecasting Annual Earnings

This paper identifies and analyses previously published studies on annual earnings forecasts. Comparisons of forecasts produced by management, analysts, and extrapolative techniques indicated that: (1) management forecasts were superior to professional analyst forecasts (the mean absolute percentage errors were 15.9 and 17.7, respectively, based on five studies using data from 1967-1974) and (2) judgmental forecasts (both management and analysts) were superior to extrapolation forecasts on 14 of 17 comparisons from 13 studies using data from 1964-1979 (the mean absolute percentage errors were 21.0 and 28.4 for judgment and extrapolation, respectively). These conclusions, based on recent research, differ from those reported in previous reviews, which commented on less than half of the studies identified here.
Chapter 4 – Spatial Considerations

Publisher Summary The chapter focuses on indoor and outdoor locations, roofed spaces in hazardous areas, nonhazardous spaces above or below hazardous areas, spaces giving access to hazardous areas. In indoor locations, air movements may not be as favorable as in outdoor locations. This is because of the obstructions natural ventilation may encounter. Access openings have no impact on whether the location should be considered as indoors or outdoors. Roofed spaces without a source of hazard normally do not require classification, unless they are located in a hazardous area. Even if they are located in a hazardous area, they can be classified as non-hazardous provided there is sufficient ventilation, and a suitable safeguard. Roofed spaces that have only one or no walls are normally considered sufficiently ventilated by natural ventilation. However, roofed spaces still need safeguards in spite of non hazardous location because ignitable gases or vapors may enter the non hazardous space. The chapter illustrates a tabular representation of roofed spaces in hazardous locations and non hazardous spaces above or below hazardous areas, Natural ventilation is capable of diluting and dispersing any flammable material in roofed spaces, because of the lack of air pockets. A location is considered outdoors when natural ventilation is restricted to not more than 25% of the building enclosure in passing through the location. This means that the location has no corners in which a flammable gas or vapor can accumulate. Usually Under three different conditions a flammable gas or vapor is released from a source of hazard: (1) a continuous release from the open sources of hazard. (2) A frequent release from closed sources of hazard; as a result of frequent repairs and maintenance. (3) An occasional release from the closed sources of hazard; as a result of a failure of the sources of hazard.
1 – The Basics

Publisher Summary This chapter discusses the basics of Algebra. As much of what done in algebra involves comparison of quantities, it is good to begin by listing a few symbols used to compare mathematical quantities. The comparison symbols fall into two major groups: (1) equality symbols and (2) inequality symbols. The symbols for inequality, , always point to the smaller of the two quantities being compared. The symbols used to represent the four basic operations are addition, subtraction, multiplication, and division. The word sum implies the operation of addition. To find the sum of two numbers, they are added. Difference implies subtraction, product implies multiplication, and quotient implies division. There is more than one way to write the product or quotient of two numbers. The symbols for comparing, operating, and grouping are to mathematics what punctuation symbols are to English. These symbols are the punctuation symbols for mathematics. When evaluating a mathematical expression, the operations are performed beginning with the expression in the innermost parentheses or brackets first and working our way out.
5 – Respiratory Chains

The general principles of electron transfer are described and are applied to mitochondrial and bacterial electron transfer complexes and their linkage to proton translocation. The availability of crystal structures for most of the key components allows models to be constructed of the molecular mechanisms by which redox energy is conserved in the proton electrochemical gradient.
Photolysis of H2O-H2O2 Mixtures: The Destruction of H2O2

Abstract We present laboratory results on the loss of H 2 O 2 in solid H 2 O + H 2 O 2 mixtures at temperatures between 21 and 145 K initiated by UV photolysis (193 nm). Using infrared spectroscopy and microbalance gravimetry, we measured the decrease of the 3.5 μm infrared absorption band during UV irradiation and obtained a photodestruction cross section that varies with temperature, being lowest at 70 K. We use our results, along with our previously measured H 2 O 2 production rates via ionizing radiation and ion energy fluxes from the spacecraft to compare H 2 O 2 creation and destruction at icy satellites by ions from their planetary magnetosphere and from solar UV photons. We conclude that, in many cases, H 2 O 2 is not observed on icy satellite surfaces because the H 2 O 2 photodestruction rate is much higher than the production rate via energetic particles, effectively keeping the H 2 O 2 infrared signature at or below the noise level.
Pointwise persistence in full chemotaxis models with logistic source on bounded heterogeneous environments

Abstract The current paper is concerned with pointwise persistence in full chemotaxis models with local as well as nonlocal time and space dependent logistic source in bounded domains. We first prove the global existence and boundedness of nonnegative classical solutions under some conditions on the coefficients in the models. Next, under the same conditions on the coefficients, we show that pointwise persistence occurs, that is, any globally defined positive solution is bounded below by a positive constant independent of its initial condition when the time is large enough. It should be pointed out that in [21] , the authors established the persistence of mass for globally defined positive solutions, which indicates that any extinction phenomenon, if occurring at all, necessarily must be spatially local in nature, whereas the population as a whole always persists. The pointwise persistence proved in the current paper implies that not only the population as a whole persists, but also it persists at any location eventually. It also implies the existence of strictly positive entire solutions.
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type

The human genome harbors a variety of genetic variations. Single-nucleotide changes that alter amino acids in protein-coding regions are one of the major causes of human phenotypic variation and diseases. These single-amino acid variations (SAVs) are routinely found in whole genome and exome sequencing. Evaluating the functional impact of such genomic alterations is crucial for diagnosis of genetic disorders. We developed DeepSAV, a deep-learning convolutional neural network to differentiate disease-causing and benign SAVs based on a variety of protein sequence, structural and functional properties. Our method outperforms most stand-alone programs, and the version incorporating population and gene-level information (DeepSAV+PG) has similar predictive power as some of the best available. We transformed DeepSAV scores of rare SAVs in the human population into a quantity termed "mutation severity measure" for each human protein-coding gene. It reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. Genes implicated in cancer, autism, and viral interaction are found by this measure as intolerant to mutations, while genes associated with a number of other diseases are scored as tolerant. Among known disease-associated genes, those that are mutation-intolerant are likely to function in development and signal transduction pathways, while those that are mutation-tolerant tend to encode metabolic and mitochondrial proteins.
Efficient Bitmap-based Indexing and Retrieval of Similarity Search Image Queries

Finding similar images is a necessary operation in many multimedia applications. Images are often represented and stored as a set of high-dimensional features, which are extracted using localized feature extraction algorithms. Locality Sensitive Hashing is one of the most popular approximate processing techniques for finding similar points in highdimensional spaces. Locality Sensitive Hashing (LSH) and its variants are designed to find similar points, but they are not designed to find objects (such as images, which are made up of a collection of points) efficiently. In this paper, we propose an index structure, Bitmap-Image LSH (bImageLSH), for efficient processing of high-dimensional images. Using a real dataset, we experimentally show the performance benefit of our novel design while keeping the accuracy of the image results high.
Can We Learn Heuristics for Graphical Model Inference Using Reinforcement Learning

Combinatorial optimization is frequently used in computer vision. For instance, in applications like semantic segmentation, human pose estimation and action recognition, programs are formulated for solving inference in Conditional Random Fields (CRFs) to produce a structured output that is consistent with visual features of the image. However, solving inference in CRFs is in general intractable, and approximation methods are computationally demanding and limited to unary, pairwise and hand-crafted forms of higher order potentials. In this paper, we show that we can learn program heuristics, i.e., policies, for solving inference in higher order CRFs for the task of semantic segmentation, using reinforcement learning. Our method solves inference tasks efficiently without imposing any constraints on the form of the potentials. We show compelling results on the Pascal VOC and MOTS datasets.
Non-invasive red-light optogenetic control of Drosophila cardiac function

Drosophila is a powerful genetic model system for cardiovascular studies. Recently, optogenetic pacing tools have been developed to control Drosophila heart rhythm noninvasively with blue light, which has a limited penetration depth. Here we developed both a red-light sensitive opsin expressing Drosophila system and an integrated red-light stimulation and optical coherence microscopy (OCM) imaging system. We demonstrated noninvasive control of Drosophila cardiac rhythms using a single light source, including simulated tachycardia in ReaChR-expressing flies and bradycardia and cardiac arrest in halorhodopsin (NpHR)-expressing flies at multiple developmental stages. By using red excitation light, we were able to pace flies at higher efficiency and with lower power than with equivalent blue light excitation systems. The recovery dynamics after red-light stimulation of NpHR flies were observed and quantified. The combination of red-light stimulation, OCM imaging, and transgenic Drosophila systems provides a promising and easily manipulated research platform for noninvasive cardiac optogenetic studies.
The effects of rural electrification in India: An instrumental variable approach at the household level

Abstract Governments in developing countries are investing billions of dollars to increase electricity access in rural areas, but the literature on the impact of these investments has produced mixed results. We leverage a unique characteristic of household electrification policy in Uttar Pradesh, India, whereby only households within 40 m of an electricity pole are eligible for a legal electrical connection, to estimate the causal impact of electrification using a pre-registered instrumental variable design. With an original survey of 686 households across 120 habitations in the Bahraich district of Uttar Pradesh, we find that legal electrification has a positive impact on household expenditures, adult household activities, and ownership and usage of appliances. The results suggest a more optimistic picture about the impact of rural electrification than some previous studies.
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans

We present a unified representation for actionable spatial perception: 3D Dynamic Scene Graphs. Scene graphs are directed graphs where nodes represent entities in the scene (e.g. objects, walls, rooms), and edges represent relations (e.g. inclusion, adjacency) among nodes. Dynamic scene graphs (DSGs) extend this notion to represent dynamic scenes with moving agents (e.g. humans, robots), and to include actionable information that supports planning and decision-making (e.g. spatio-temporal relations, topology at different levels of abstraction). Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data. We integrate state-of-the-art techniques for object and human detection and pose estimation, and we describe how to robustly infer object, robot, and human nodes in crowded scenes. To the best of our knowledge, this is the first paper that reconciles visual-inertial SLAM and dense human mesh tracking. Moreover, we provide algorithms to obtain hierarchical representations of indoor environments (e.g. places, structures, rooms) and their relations. Our third contribution is to demonstrate the proposed spatial perception engine in a photo-realistic Unity-based simulator, where we assess its robustness and expressiveness. Finally, we discuss the implications of our proposal on modern robotics applications. 3D Dynamic Scene Graphs can have a profound impact on planning and decision-making, human-robot interaction, long-term autonomy, and scene prediction. A video abstract is available at this https URL
Anonymity-aware face-to-face mobile payment

On-line payments are increasingly popular in paying bills for Internet shopping, and payment-capable mobile phones support making purchases anytime and anywhere, without cash. However, mobile payments are rarely used for making face-to-face payments, with concerns about anonymity, security, and usability. This paper proposes a face-to-face mobile payment protocol that addresses these concerns. To address anonymity and security concerns, the proposed protocol uses unique information for the payment transaction, such as the location and the time, and introduces two procedures for optimizing the matching time slots and exchanging random numbers when needed, to secure the transactions without exposing the seller’s or the buyer’s personal identification. To address usability concerns, the proposed protocol optimizes the parameters for the two introduced procedures to match the seller-buyer pairs, depending on the number of the people involved in the mobile payments, the delays caused by human operations with the mobile phones, mobile communication, and so on. Experimental results prove that the proposed protocol is practical, solving the addressed concerns.
Lacunary eta-quotients modulo powers of primes

An integral power series is called lacunary modulo M if almost all of its coefficients are divisible by M. Motivated by the parity problem for the partition function, Gordon and Ono studied the generating functions for t-regular partitions, and determined conditions for when these functions are lacunary modulo powers of primes. We generalize their results in a number of ways by studying infinite products called Dedekind eta-quotients and generalized Dedekind eta-quotients. We then apply our results to the generating functions for the partition functions considered by Nekrasov, Okounkov, and Han.
ADMIXPIPE: population analyses in ADMIXTURE for non-model organisms

Research on the molecular ecology of non-model organisms, while previously constrained, has now been greatly facilitated by the advent of reduced-representation sequencing protocols. However, tools that allow these large datasets to be efficiently parsed are often lacking, or if indeed available, then limited by the necessity of a comparable reference genome as an adjunct. This, of course, can be difficult when working with non-model organisms. Fortunately, pipelines are currently available that avoid this prerequisite, thus allowing data to be a priori parsed. An oft-used molecular ecology program (i.e., Structure), for example, is facilitated by such pipelines, yet they are surprisingly absent for a second program that is similarly popular and computationally more efficient (i.e., Admixture). The two programs differ in that Admixture employs a maximum-likelihood framework whereas Structure uses a Bayesian approach, yet both produce similar results. Given these issues, there is an overriding (and recognized) need among researchers in molecular ecology for bioinformatic software that will not only condense output from replicated Admixture runs, but also infer from these data the optimal number of population clusters (K). Here we provide such a program (i.e., AdmixPipe) that (a) filters SNPs to allow the delineation of population structure in Admixture, then (b) parses the output for summarization and graphical representation via Clumpak. Our benchmarks effectively demonstrate how efficient the pipeline is for processing large, non-model datasets generated via double digest restriction-site associated DNA sequencing (ddRAD). Outputs not only parallel those from Structure, but also visualize the variation among individual Admixture runs, so as to facilitate selection of the most appropriate K-value. AdmixPipe successfully integrates Admixture analysis with popular variant call format (VCF) filtering software to yield file types readily analyzed by Clumpak. Large population genomic datasets derived from non-model organisms are efficiently analyzed via the parallel-processing capabilities of Admixture. AdmixPipe is distributed under the GNU Public License and freely available for Mac OSX and Linux platforms at: https://github.com/stevemussmann/admixturePipeline .
Barrier Functions in Cascaded Controller: Safe Quadrotor Control

Safe control for inherently unstable systems such as quadrotors is crucial. Imposing multiple dynamic constraints simultaneously on the states for safety regulation can be a challenging problem. In this paper, we propose a quadratic programming (QP) based approach on a cascaded control architecture for quadrotors to enforce safety. Safety regions are constructed using control barrier functions (CBF) while explicitly considering the nonlinear underactuated dynamics of the quadrotor. The safety regions constructed using CBFs establish a non-conservative forward invariant safe region for quadrotor navigation. Barriers imposed across the cascaded architecture allows independent safety regulation in quadrotor's altitude and lateral domains. Despite barriers appearing in a cascaded fashion, we show preservation of safety for quadrotor motion in SE(3). We demonstrate the feasibility of our method on a quadrotor in simulation with static and dynamic constraints enforced on position and velocity spaces simultaneously.
On Suboptimality of Least Squares with Application to Estimation of Convex Bodies

We develop a technique for establishing lower bounds on the sample complexity of Least Squares (or, Empirical Risk Minimization) for large classes of functions. As an application, we settle an open problem regarding optimality of Least Squares in estimating a convex set from noisy support function measurements in dimension $d\geq 6$. Specifically, we establish that Least Squares is mimimax sub-optimal, and achieves a rate of $\tilde{\Theta}_d(n^{-2/(d-1)})$ whereas the minimax rate is $\Theta_d(n^{-4/(d+3)})$.
SwarmControl: An Automated Distributed Control Framework for Self-Optimizing Drone Networks

Networks of Unmanned Aerial Vehicles (UAVs), composed of hundreds, possibly thousands of highly mobile and wirelessly connected flying drones will play a vital role in future Internet of Things (IoT) and 5G networks. However, how to control UAV networks in an automated and scalable fashion in distributed, interference-prone, and potentially adversarial environments is still an open research problem. This article introduces SwarmControl, a new software-defined control framework for UAV wireless networks based on distributed optimization principles. In essence, SwarmControl provides the Network Operator (NO) with a unified centralized abstraction of the networking and flight control functionalities. High-level control directives are then automatically decomposed and converted into distributed network control actions that are executed through programmable software-radio protocol stacks. SwarmControl (i) constructs a network control problem representation of the directives of the NO; (ii) decomposes it into a set of distributed sub-problems; and (iii) automatically generates numerical solution algorithms to be executed at individual UAVs.We present a prototype of an SDR-based, fully reconfigurable UAV network platform that implements the proposed control framework, based on which we assess the effectiveness and flexibility of SwarmControl with extensive flight experiments. Results indicate that the SwarmControl framework enables swift reconfiguration of the network control functionalities, and it can achieve an average throughput gain of 159% compared to the state-of-the-art solutions.
Design and analysis of extended depth of focus metalenses for achromatic computational imaging

Metasurface optics have demonstrated vast potential for implementing traditional optical components in an ultracompact and lightweight form factor. Metasurfaces, however, suffer from severe chromatic aberrations, posing serious limitations on their practical use. Existing approaches for circumventing this involving dispersion engineering are limited to small apertures and often entail multiple scatterers per unit cell with small feature sizes. Here, we present an alternative technique to mitigate chromatic aberration and demonstrate high-quality, full-color imaging using extended depth of focus (EDOF) metalenses and computational reconstruction. Previous EDOF metalenses have relied on cubic phase masks, where the image quality suffers from asymmetric artefacts. Here we demonstrate the use of rotationally symmetric masks, including logarithmic-aspherical, and shifted axicon masks, to mitigate this problem. Our work will inspire further development in achromatic metalenses beyond dispersion engineering and hybrid optical–digital metasurface systems.
Thermodynamic Origin of Reaction Non-Uniformity in Battery Porous Electrodes and Its Mitigation

The development of non-uniform reaction current distribution within porous electrodes is a ubiquitous phenomenon during battery charging / discharging and frequently controls the rate performance of battery cells. Reaction inhomogeneity in porous electrodes is usually attributed to the kinetic limitation of mass transport within the electrolyte and/or solid electrode phase. In this work, however, we reveal that it is also strongly influenced by the intrinsic thermodynamic behavior of electrode materials, specifically the dependence of the equilibrium potential on the state of charge: electrode reaction becomes increasingly non-uniform when the slope of the equilibrium potential curve is reduced. We employ numerical simulation and equivalent circuit model to elucidate such a correlation and show that the degree of reaction inhomogeneity and the resultant discharge capacity can be predicted by a dimensionless reaction uniformity number. For electrode materials that have equilibrium potentials insensitive to the state of charge and exhibit significant reaction non-uniformity, we demonstrate several approaches to spatially homogenizing the reaction current inside porous electrodes, including matching the electronic and ionic resistances, introducing graded electronic conductivity and reducing the surface reaction kinetics.
New upper bounds for the spectral variation of a general matrix

Let A ∈ C n × n be a normal matrix with spectrum { λ i } i = 1 n , and let A ~ = A + E ∈ C n × n be a perturbed matrix with spectrum { λ ~ i } i = 1 n . If A ~ is still normal, the celebrated Hoffm...
A brief prehistory of China’s social credit system:

This article situates China’s social credit system in a historical perspective by exploring its antecedents. The historical roots of the social credit system can be found in personnel archives for ...
A Data-driven Hierarchical Control Structure for Systems with Uncertainty

The paper introduces a Data-driven Hierarchical Control (DHC) structure to improve performance of systems operating under the effect of system and/or environment uncertainty. The proposed hierarchical approach consists of two parts: 1) A data-driven model identification component to learn a linear approximation between reference signals given to an existing lower-level controller and uncertain time-varying plant outputs. 2) A higher-level controller component that utilizes the identified approximation and wraps around the existing controller for the system to handle modeling errors and environment uncertainties during system deployment. We derive loose and tight bounds for the identified approximation's sensitivity to noisy data. Further, we show that adding the higher-level controller maintains the original system's stability. A benefit of the proposed approach is that it requires only a small amount of observations on states and inputs, and it thus works online; that feature makes our approach appealing to robotics applications where real-time operation is critical. The efficacy of the DHC structure is demonstrated in simulation and is validated experimentally using aerial robots with approximately-known mass and moment of inertia parameters and that operate under the influence of ground effect.
An Experimental mmWave Channel Model for UAV-to-UAV Communications

Unmanned Aerial Vehicle (UAV) networks can provide a resilient communication infrastructure to enhance terrestrial networks in case of traffic spikes or disaster scenarios. However, to be able to do so, they need to be based on high-bandwidth wireless technologies for both radio access and backhaul. With this respect, the millimeter wave (mmWave) spectrum represents an enticing solution, since it provides large chunks of untapped spectrum that can enable ultra-high data-rates for aerial platforms. Aerial mmWave channels, however, experience characteristics that are significantly different from terrestrial deployments in the same frequency bands. As of today, mmWave aerial channels have not been extensively studied and modeled. Specifically, the combination of UAV micro-mobility (because of imprecisions in the control loop, and external factors including wind) and the highly directional mmWave transmissions require ad hoc models to accurately capture the performance of UAV deployments. To fill this gap, we propose an empirical propagation loss model for UAV-to-UAV communications at 60 GHz, based on an extensive aerial measurement campaign conducted with the Facebook Terragraph channel sounders. We compare it with 3GPP channel models and make the measurement dataset publicly available.
The Impact of the COVID-19 Pandemic on Information Systems Management

COVID-19 can be likened to a seismic event – it does significant shaking, moves the organization around, but in some time, things fall into place and life goes on. Six companies were evaluated to d...
Enzyme-free synthesis of natural phospholipids in water

All living organisms synthesize phospholipids as the primary constituent of their cell membranes. Enzymatic synthesis of diacylphospholipids requires preexisting membrane-embedded enzymes. This limitation has led to models of early life in which the first cells used simpler types of membrane building blocks and has hampered integration of phospholipid synthesis into artificial cells. Here we demonstrate an enzyme-free synthesis of natural diacylphospholipids by transacylation in water, which is enabled by a combination of ion pairing and self-assembly between lysophospholipids and acyl donors. A variety of membrane-forming cellular phospholipids have been obtained in high yields. Membrane formation takes place in water from natural alkaline sources such as soda lakes and hydrothermal oceanic vents. When formed vesicles are transferred to more acidic solutions, electrochemical proton gradients are spontaneously established and maintained. This high-yielding non-enzymatic synthesis of natural phospholipids in water opens up new routes for lipid synthesis in artificial cells and sheds light on the origin and evolution of cellular membranes.
Identification of 14 Known Drugs as Inhibitors of the Main Protease of SARS-CoV-2

A consensus virtual screening protocol has been applied to ca. 2000 approved drugs to seek inhibitors of the main protease (Mpro) of SARS-CoV-2, the virus responsible for COVID-19. 42 drugs emerged as top candidates, and after visual analyses of the predicted structures of their complexes with Mpro, 17 were chosen for evaluation in a kinetic assay for Mpro inhibition. Remarkably 14 of the compounds at 100-μM concentration were found to reduce the enzymatic activity and 5 provided IC50 values below 40 μM: manidipine (4.8 μM), boceprevir (5.4 μM), lercanidipine (16.2 μM), bedaquiline (18.7 μM), and efonidipine (38.5 μM). Structural analyses reveal a common cloverleaf pattern for the binding of the active compounds to the P1, P1', and P2 pockets of Mpro. Further study of the most active compounds in the context of COVID-19 therapy is warranted, while all of the active compounds may provide a foundation for lead optimization to deliver valuable chemotherapeutics to combat the pandemic.
Dirty money: Does the risk of infectious disease lower demand for cash?

The COVID-19 pandemic is a global crisis like no other in modern times, and there is a growing apprehension about handling potentially contaminated cash This paper is the first empirical attempt in the literature to investigate whether the risk of infectious diseases affects demand for physical cash Since the intensity of cash use may influence the spread of infectious diseases, this paper utilizes two-stage least squares methodology with instrumental variable to address omitted variable bias and account for potential endogeneity The empirical analysis indicates that the spread of infectious diseases lowers demand for physical cash, after controlling for macroeconomic, financial, and technological factors This effect, withstanding several robustness checks, is economically and statistically significant While the transactional constraints imposed by the coronavirus pandemic could become a catalyst for the use of digital technologies around the world, electronic payment methods may not be universally available in every country owing to financial and technological bottlenecks
The Statistical Analysis of the Live TV Bit Rate

This paper studies the statistical nature of TV channels streaming variable bit rate distribution and allocation. The goal of the paper is to derive the best-fit rate distribution to describe TV streaming bandwidth allocation, which can reveal traffic demands of users. Our analysis uses multiplexers channel bandwidth allocation (PID) data of 13 TV live channels. We apply 17 continuous and 3 discrete distributions to determine the best-fit distribution function for each individual channel and for the whole set of channels. We found that the generalized extreme distribution fitting most of our channels most precisely according to the Bayesian information criterion. By the same criterion tlocationscale distribution matches best for the whole system. We use these results to propose parameters for streaming server queuing model. Results are useful for streaming servers scheduling policy design process targeting to improve limited infrastructural resources, traffic engineering through dynamic routing at CDN, SDN.
HYPERION: an open-source parallelized three-dimensional dust continuum radiative transfer code

Hyperion is a new three-dimensional dust continuum Monte-Carlo radiative transfer code that is designed to be as generic as possible, allowing radiative transfer to be computed through a variety of three-dimensional grids. The main part of the code is problemindependent, and only requires an arbitrary three-dimensional density structure, dust properties, the position and properties of the illuminating sources, and parameters controlling the running and output of the code. Hyperion is parallelized, and is shown to scale well to thousands of processes. Two common benchmark models for protoplanetary disks were computed, and the results are found to be in excellent agreement with those from other codes. Finally, to demonstrate the capabilities of the code, dust temperatures, SEDs, and synthetic multi-wavelength images were computed for a dynamical simulation of a low-mass star formation region. Hyperion is being actively developed to include new features, and is publicly available �� .
The ``Coulomb Phase'' in Frustrated Systems

The “Coulomb phase” is an emergent state for lattice models (particularly highly frustrated antiferromagnets), which have local constraints that can be mapped to a divergence-free “flux.” The coarse-grained versions of this flux or polarization behave analogously to electric or magnetic fields; in particular, defects at which the local constraint is violated behave as effective charges with Coulomb interactions. I survey the derivation of the characteristic power-law correlation functions and the pinch points in reciprocal space plots of diffuse scattering, as well as applications to magnetic relaxation, quantum-mechanical generalizations, phase transitions to long-range-ordered states, and the effects of disorder.
Dynamical symmetries and Nambu mechanics

It is shown that several Hamiltonian systems possessing dynamical or hidden symmetries can be realized within the framework of Nambu's generalized mechanics. Among such systems are the SU(n)-isotropic harmonic oscillator and the SO(4) Kepler problem. As required by the formulation of Nambu dynamics, the integrals of motion for these systems necessarily become the so-called generalized Hamiltonians. Furthermore, in most of these problems, the definition of these generalized Hamiltonians is not unique.
Asymmetric butterfly velocities in 2-local Hamiltonians

The speed of information propagation is finite in quantum systems with local interactions. In many such systems, local operators spread ballistically in time and can be characterized by a "butterfly velocity", which can be measured via out-of-time-ordered correlation functions. In general, the butterfly velocity can depend asymmetrically on the direction of information propagation. In this work, we construct a family of simple 2-local Hamiltonians for understanding the asymmetric hydrodynamics of operator spreading. Our models live on a one dimensional lattice and exhibit asymmetric butterfly velocities between the left and right spatial directions. This asymmetry is transparently understood in a free (non-interacting) limit of our model Hamiltonians, where the butterfly speed can be understood in terms of quasiparticle velocities.